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Important Note: DASFAA-2022 Awards

Welcome to the DASFAA-2022 Virtual Conference, April 11-14, 2022, Hyderabad, India
The DASFAA-2022 conference is held in online mode. All timings are as per Indian Standard Time (IST) (UTC + 05:30).
We are hosting the conference on Airmeet virtual event platform.

Instructions to join the event:

Go through the following steps to join the sessions.

We recommend using a laptop or a desktop computer with the Chrome browser and good internet connectivity for the best experience. If you are using your mobile phone, we recommend installing Airmeet App and join the sessions.
Detailed instructions for speakers are available at the following link. https://help.airmeet.com/support/solutions/articles/82000477727-step-by-step-guide-for-speaker-meetup-format. A video format is available at the following link. https://vimeo.com/685341544.
Detailed instructions for attendees are available at the following link. https://help.airmeet.com/support/solutions/articles/82000480786-step-by-step-guide-for-attendees-conference-format. A video format is available at the following link. https://vimeo.com/685338020.

Workshop Schedule
April 11, 2022 (Monday)
Timings Hall 1 Hall 2 Hall 3 Hall 4 Hall 5
9.00-13.00 IWBT-2022
International Workshop on Blockchain Technologies (IWBT2022)
Organizers: Sanjay Chaudhary (Ahmedabad University, India), Krishnasuri Narayanam (IBM Research, India)
MAQTDS-2022
Managing Air Quality Through Data Science (MAQTDS 2022)
Organizers: Girish Agrawal (O.P. Jindal Global University, India), Jai Ganesh (Mphasis Ltd., India)
PMBD-2022
1st Workshop on Pattern mining and Machine learning in Big complex Databases (PMBD 2022)
Organizers: Philippe Fournier-Viger (Shenzhen University, China), Mourad Nouioua (Harbin Institute of Technology Shenzhen, China), Hamido Fujita (Iwate Prefectural University, Japan), Lin Zhang (Tencent.com), Vincent S. Tseng (National Chiao Tung University, Taiwan)
BDMS-2022
The 8th International Workshop on Big Data Management and Service (BDMS 2022)
Organizers: Xiaoling Wang (East China Normal University, China), Kai Zheng (University of Electronic Science and Technology of China), An Liu (Soochow University, China)
BDQM-2022
The 7th International Workshop on Big Data Quality Management (BDQM 2022)
Organizers: Xiaoou Ding (Harbin Institute of Technology, China), Xueli Liu (Tianjin University, China)
13.00-14.00 Lunch break
14.00-18.00 IWBT2022 (Afternoon session) MAQTDS 2022 (Afternoon session) GDMA-2022
The 6th International Workshop on Graph Data Management and Analysis (GDMA 2022)
Organizers: Lei Zou (Peking University, China)
Main Conference Schedule (April 12-14, 2022)
April 12, 2022 (Tuesday)
Timings Hall 1 Hall 2 Hall 3 Hall 4 Hall 5
08.30-09.00 Inauguration (Hall 1)
Lei Chen (Hong Kong University of Science and Technology, Hong Kong), P. J. Narayanan (IIIT Hyderabad, India), S. Sudarshan (IIT Bombay, India), Masaru Kitsuregawa (University of Tokyo, Japan), P. Krishna Reddy (IIIT Hyderabad, India), Mukesh Mohania (IIIT Delhi, India), Anirban Mondal (Ashoka University, India), Arnab Bhattacharya (IIT Kanpur, India), Lee Mong Li Janice (National University of Singapore, Singapore), Divyakant Agrawal (University of California, Santa Barbara, USA), Prasad M. Deshpande (Google), Rajeev Gupta (Microsoft, India)
09.00-10.00 Keynote by Gautham Das (University of Texas at Arlington) (Hall 1)
Title: Fairness in Database Querying
Chair: Mukesh Mohania (IIIT, Delhi)
10.00-10.15 Tea break
10.15-12.30 Research Session #1
(Queries)
Chair: P Radha Krishna (NIT Warangal, India)
Research Session #2
(Text and Image-I)
Chair: Anil Kumar Vuppala (IIIT Hyderabad, India)
Research Session #3
(Applications of ML-I)
Chair: Rajeev Gupta (Microsoft)
Industry Session #1 (Advancing Recommendation Systems)
Chair: Prasad Deshpande (Google)
Tutorial Session #2
(Title: Reachability on Large-scale Graphs: Models, Techniques, and Trends)
Chair: Lini Thomas (IIIT Hyderabad)
12.30-13.30 Lunch break
13.30-14.45 Demo and PhD Consortium (Hall 1)
Chair: Anirban Mondal (Ashoka University, India)
14.45-16.30 Research Session #4
(Graphs-I)
Chair: Vasudha Bhatnagar (University of Delhi)
Research Session #5
(Text and Image-II)
Chair: Arvind Agarwal (IBM)
Research Session #6
(Recommendation-I)
Chair: Manish Singh (IIT Hyderabad)
Tutorial Session #1
(Title: Make Wise Decisions for Your DBMSs: Workload Forecasting and Performance Prediction Before Execution)
Chair: Vikram Pudi (IIIT Hyderabad)
16.30-17.00 Tea break
17.00-18.30 Panel Session (Hall 1)
Moderator: Kurt Stockinger (Zurich University of Applied Sciences, Switzerland)
Panelists: Georgia Koutrika (Athena Research, Greece), Jaydeep Sen (IBM Research, India), Immanuel Trummer (Cornell University, USA) , Lei Zou (Peking University, China)
Chair: Jayant Haritsa (Indian Institute of Science, Bangalore, India)
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April 13, 2022 (Wednesday)
Timings Hall 1 Hall 2 Hall 3 Hall 4 Hall 5
09.00-10.00 Keynote by Tirthankar Lahri (Oracle Corporation) (Hall 1)
Title: Oracle Database In-Memory: The Enterprise, at Warp Speed!
Chair: Masaru Kitsuregawa (University of Tokyo, Japan)
10.00-10.15 Tea break
10.15-12.30 Research Session #7
(Spatio-temporal Data)
Chair: Uday Kiran, (The University of Aizu, Japan)
Research Session #8
(Recommendation-II)
Chair: Vikram Goel (IIIT Delhi)
Research Session #9
(Applications of ML-II)
Chair: Ranganath Kondapally (Microsoft)
Industry Session #2
(Data Management and Search)
Chair: Rajasekar Krishnamurthy (Adobe)
Tutorial Session #3
(Title: A tutorial on biomedical image segmentation using deep learning)
Chair: Sudipta Banerjee (IIIT Hyderabad, India)
12.30-13.30 Lunch break
13.30-14.30 Keynote by Ioana Manolescu (Inria and Institut Polytechnique de Paris) (Hall 1)
Title: Teasing journalistic findings out of heterogeneous sources: a data/AI journey
Chair: Srinath Srinivasa (IIIT Bangalore, India)
14.30-16.15 Research Session #10
(Algorithms-I)
Chair: Anirban Mondal (Ashoka University)
Research Session #11
(Systems)
Chair: Satish Narayana Srirama (University of Hyderabad)
Research Session #12
(Applications of ML-III)
Chair: Ranganath Kondapally (Microsoft)
  Tutorial Session #4
(Title: AI Meets NoSQL Database: Methods, Opportunities and Challenges)
Chair: Kamal Karlapalem (IIIT Hyderabad)
16.15-16.30 Tea break
16.30-18.00 Research Session #13
(Security)
Chair: Ashok Kumar Das (IIIT Hyderabad)
Research Session #14
(Text and Image-III)
Chair: Anil Kumar Vuppala (IIIT Hyderabad, India)
Research Session #15 (Graphs-II)
Chair: Tanmoy Chakraborty (IIIT Delhi)
  

Program Details

1. Keynote Speakers

Prof. Gautam Das

Director of Center for Artificial Intelligence and Big Data (CARIDA) and Database Exploration Laboratory (DBXLAB),
University of Texas at Arlington

Title:

Fairness in Database Querying.
April 12, 2022 (Tuesday), 09.00-10.00, Hall 1

Abstract:

We are being constantly judged by automated decision systems that have been criticized for being sometimes discriminatory and unfair. In this talk, we focus on fairness issues that arise when users perform ad-hoc exploration of databases using commonly available querying mechanisms such as selection/range queries, ranking queries, top-k queries, etc. For example, a user may use such queries to retrieve suitable employment opportunities in a jobs database, dating partners in a matching website, or apartments to rent in a real estate database. We will discuss how such querying mechanisms can give sometimes give results that are discriminatory, and discuss approaches to detect, mitigate and prevent such scenarios from occurring. Our work represents some of the initial steps towards the broader goal of integrating fairness conditions into database query processing and data management.

Bio:

Dr. Das is the Associate Dean for Research, College of Engineering, a Distinguished University Chair Professor of Computer Science and Engineering, Director of the Center for Artificial Intelligence and Big Data (CARIDA), and Director of the Database Exploration Laboratory (DBXLAB) at UT-Arlington. Prior to joining UTA in 2004, he has held positions at Microsoft Research, Compaq Corporation and the University of Memphis. He graduated with a B.Tech in computer science from IIT Kanpur, India in 1983, and with a Ph.D in computer science from the University of Wisconsin, Madison in 1990. He is a Fellow of the IEEE and a member of the ACM.

Dr. Das has published over 200 papers, many of which have appeared in premier data mining, database and algorithms conferences and journals. His work has received several awards, including the Communications of the ACM Research Highlights in 2021, ACM SIGMOD Research Highlights in 2019, IEEE ICDE 10-Year Influential Paper Award in 2012, ACM SIGKDD Doctoral Dissertation Award (honorable mention) in 2014 for his former student, and numerous other awards. He has presented keynotes and invited lectures, tutorials and courses at various universities, research labs, and conferences. He has been on the Editorial Board of the journals ACM Transactions on Database Systems and IEEE Transactions on Knowledge and Data Engineering. He has served in the organization roles of several major conferences, including as General Chair of ACM SIGMOD/PODS 2018.

Tirthankar Lahiri

Senior Vice President,
Data and In-Memory Technologies,
Oracle Corporation.

Title:

Oracle Database In-Memory: The Enterprise, at Warp Speed !
April 13, 2022 (Wednesday), 09.00-10.00, Hall 1

Abstract:

In-memory computing is more than simply about speed. It enables a fundamental transformation in business processes. Just as air travel enabled more than just the ability to travel faster: It enabled a completely new global economy, reshaped politics, and transformed society. Oracle's Database In-Memory feature similarly enables not just faster analytics, but a fundamental rethinking and drastic simplification of the traditional analytic platform. Combined with Oracle's many converged database capabilities that bring together many data models and many workloads, and with Oracle's Autonomous Database platform that makes self-driving machine-learning powered databases a reality, Oracle Database In-Memory allows for the development of a new category of enterprise architectures, with significant reduction in cost and complexity, while providing unmatched performance for both transactional and analytic workloads.

Bio:

Tirthankar Lahiri is Senior Vice President of the Data and In-Memory Technologies area within Oracle Database. This includes the Oracle Database Engine (Transactions, Data formats, Indexes, Advanced Compression, Database In-Memory, the Database Filesystem, etc.), the Oracle TimesTen In-Memory Database, and Oracle NoSQLDB. Tirthankar has 26 years of experience in the database industry and has worked on a number of areas such as Performance, Scalability, Manageability and In-Memory architectures. He has 45 issued and several pending patents and a number of academic publications. He has a B.Tech in Computer Science from the Indian Institute of Technology (Kharagpur) and an MS in Electrical Engineering from Stanford University. He was in the PhD program at Stanford and his research included NUMA Operating Systems (the Hive project) and Semistrucured Data (the Ozone project) before his PhD was superceded by his industrial career.

Ioana Manolescu

Inria and Institut Polytechnique de Paris.

Title:

Teasing journalistic findings out of heterogeneous sources: a data/AI journey.
April 13, 2022 (Wednesday), 13.30-14.30, Hall 1

Abstract:

Freedom of the press is under thread worldwide, and the quality of information that people have access to is dangerously degraded, under the joint threat of non-democratic governments and fake information propagation. The press as an industry needs powerful data management tools to help them interpret the complex reality surrounding us.

Since 2018, I have been cooperating with journalists from Le Monde, France's leading newspaper, in devising tools for analyzing large and heterogeneuos data sources that they are interested in. This research has been embodied in ConnectionLens, a graph ETL tool capable of ingesting heterogeneous data sources into a graph, enriched (with the help of ML methods) with entities extracted from data of any type. On such integrated graphs, we devised novel algorithms for keyword search, and combine them in more recent research with structured querying. The talk describes the architecture and main algorithmic challenges in building and exploiting ConnectionLens graphs, illustrated in particular on an application where we study conflicts of interest in the biomedical domain. This is joint work with A. Anadiotis, O. Balalau, H. Galhardas and many others. ConnectionLens Web site (papers+code): https://team.inria.fr/cedar/connectionlens/

This research has been funded by Agence Nationale de la Recherche AI Chair SourcesSay (https://sourcessay.inria.fr)

Bio:

Ioana Manolescu is a senior researcher at Inria Saclay and a part-time professor at Ecole Polytechnique, France. She is the lead of the CEDAR INRIA team focusing on rich data analytics at cloud scale. She is also the scientific director of LabIA, a program ran by the French government whereas AI problems raised by branches of the local and national French public administration are tackled by French research teams. She is a member of the PVLDB Endowment Board of Trustees, and has been Associate Editor for PVLDB, president of the ACM SIGMOD PhD Award Committee, chair of the IEEE ICDE conference, and a program chair of EDBT, SSDBM, ICWE among others. She has co-authored more than 150 articles in international journals and conferences and co-authored books on "Web Data Management" and on "Cloud-based RDF Data Management". Her main research interests algebraic and storage optimizations for semistructured data, in particular Semantic Web graphs, novel data models and languages for complex data management, data models and algorithms for fact-checking and data journalism, a topic where she is collaborating with journalists from Le Monde. She is also a recipient of the ANR AI Chair titled "SourcesSay: Intelligent Analysis and Interconnexion of Heterogeneous Data in Digital Arenas" (2020-2024).

Prof. Sunita Sarawagi

Institute Chair Professor, Computer Science and Engineering, IIT Bombay.

Title:

Modern AI for Age-old problems of Database Systems.
April 14, 2022 (Thursday), 09.00-10.00, Hall 1

Abstract:

Modern deep learning methods are pushing the frontiers of many challenging problems in database systems. We will discuss state-of-the-art machine learning models that are providing record breaking accuracies on age-old tasks such as entity resolution, missing value imputation and natural language querying. We are also witnessing brand new capabilities that were not possible a few years back. We can perform entity resolution across heterogeneous, multilingual datasets via actively learned nearest neighbor indices, thereby eliminating the need for hand-designing blocking predicates. On multi-dimensional analytical datasets, we can now obtain joint distributions over thousands of interacting time series. Advances in pre-trained language models have significantly increased the capability of handling natural variations in parsing text input to SQL. In this talk we will go over the latest ML research that is enabling these capabilities, and present directions for future research.

Bio:

Sunita Sarawagi researches in the fields of databases and machine learning. She is institute chair professor at IIT Bombay. She got her PhD in databases from the University of California at Berkeley and a bachelors degree from IIT Kharagpur. She has also worked at Google Research (2014-2016), CMU (2004), and IBM Almaden Research Center (1996-1999). She was awarded the Infosys Prize in 2019 for Engineering and Computer Science, and the distinguished Alumnus award from IIT Kharagpur. She has several publications including best paper awards at ACM SIGMOD, VLDB, ICDM, NIPS, and ICML conferences. She has served on the board of directors of the ACM SIGKDD and VLDB foundation. She was program chair for the ACM SIGKDD 2008 conference, research track co-chair for the VLDB 2011 conference and has served as program committee member for SIGMOD, VLDB, SIGKDD, ICDE, and ICML conferences, and on the editorial boards of the ACM TODS and ACM TKDD journals.

Prof. Guoliang Li

Tsinghua University, Beijing, China.

Title:

openGauss: An Autonomous Database System.
April 14, 2022 (Thursday), 13.30-14.30, Hall 1

Abstract:

In this talk, I will present how to build an autonomous database system. I discuss how to integrate effective learning-based models into database systems to build learned optimizers (including learned query rewrite, learned cost/cardinality estimation, learned join order selection and physical operator selection) and learned database advisors (including self-monitoring, self-diagnosis, self-configuration, and self- optimization). I also propose an effective validation model to validate the effectiveness of learned models. I discuss effective training data management and model management platforms to easily deploy learned models. Finally I will introduce our autonomous database system openGauss.

Bio:

Guoliang Li is a full professor and the deputy head of Department of Computer Science, Tsinghua University, Beijing, China. His research interests include large-scale data integration and cleaning, human-in-the-loop data management, machine learning for database, and database for machine learning. He is a general co-chair of SIGMOD 2021, demo co-chair of VLDB 2021, industry co-chair of ICDE 2022, and PC co-chair of DASFAA 2019. He is also an associate editor of VLDB journal and IEEE TKDE. He is a steering committee member of IEEE TCDE and DASFAA. He received best paper awards (candidates) of VLDB 2020, ICDE 2018, KDD 2018, CIKM 2017 and DASFAA 2014. He received Early Research Contribution Award of VLDB and Early Career Award of IEEE TCDE.


2. Panel Session

Title: Futuristic Data Interfaces
Hall 1, April 12, 2022 (Tuesday), 17:00-18:30 India Time

Overview:

Database systems have been around for more than four decades and have been widely used in academia and industry across the globe. In this panel we discuss the following two questions from various perspectives with four different internationally renowned database experts.

  • What are the challenges of current database interfaces?
  • What solutions do you propose to solve these challenges?

Moderator:

Kurt Stockinger

Zurich University of Applied Sciences,
Switzerland.

Panelists:

Georgia Koutrika

Athena Research,
Greece.

Jaydeep Sen

IBM Research,
India.

Immanuel Trummer

Cornell University,
USA.

Lei Zou

Peking University,
China.


Moderator: Kurt Stockinger (Zurich University of Applied Sciences, Switzerland)

Bio: Prof. Dr. Kurt Stockinger is Professor of Computer Science, Director of Studies in Data Science at Zurich University of Applied Sciences (ZHAW) and Co-Head of the ZHAW Datalab. His research focuses on Data Science with emphasis on Big Data, Natural Language Query Processing, Query Optimization and Quantum Computing. Essentially, his research interests are at the intersection of databases, natural language processing and machine learning. He is also on the Advisory Board of Callista Group AG and the International AIQT Foundation. Previously Kurt Stockinger worked at Credit Suisse in Zurich, Switzerland, at Lawrence Berkeley National Laboratory in Berkeley, California, at California Institute of Technology, California as well as at CERN in Geneva, Switzerland. He holds a Ph.D. in computer science from CERN / University of Vienna.

(i) Georgia Koutrika (Athena Research, Greece)

Title: Intelligent Data Assistants

Abstract: Data is considered the 21st century’s most valuable commodity. Analysts exploring data sets for insight, scientists looking for patterns, and consumers looking for information are just a few examples of user groups that need to access and dig into data. Despite technological advances in the data exploration and data management domains, existing systems are falling behind in bridging the chasm between data and users, making data accessible and useful only to the few. A futuristic data interface would enable interaction with data using natural language, would understand the data as well as the user intent, would guide the user, and make suggestions, and altogether help the user leverage data for all sorts of purposes (from finding answers to questions to revealing patterns and finding solutions to problems) in a more natural way. These systems, which we call intelligent data assistants, require the synergy of several technologies and innovation in all these fronts, including natural language interfaces, data exploration, conversational AI, and data management.

Bio: Georgia Koutrika is a Research Director at Athena Research Center in Greece. She has more than 15 years of experience in multiple roles at HP Labs, IBM Almaden, and Stanford. Her work emerges at the intersection of data management, natural language processing and deep learning and focuses on intelligent and interactive data exploration, conversational data systems, and user-driven data management. Her work has been incorporated in commercial products, described in 14 granted patents and 26 patent applications in the US and worldwide, and published in more than 100 papers in top-tier conferences and journals. Georgia is an ACM Senior Member and IEEE Senior Member. She is a member of the VLDB Endowment Board of Trustees, member of the PVLDB Advisory Board, member of the ACM-RAISE Working Group, co-Editor-in-chief for VLDB Journal, PC co-chair for VLDB 2023, co-EiC of Proceedings of VLDB (PVLDB). She has been associate editor in top-tier conferences (such as ACM SIGMOD, VLDB) and journals (VLDB Journal, IEEE TKDE), and she has been in the organizing committee of several conferences including SIGMOD, ICDE, EDBT, among others. She has received a PhD and a diploma in Computer Science from the Department of Informatics and Telecommunications, University of Athens, Greece.

(ii) Jaydeep Sen (IBM Research, India)

Title: Evolution of NLIDB systems and their application in Industry setups

Abstract:: In this modern era of technology, multitude of business applications are rapidly moving towards data driven insights for intelligent decision making, analytics and more. As we continue to see heaps of digital exhaust being generated, access to data is still limited to technical users who can query the datastores with specific query languages. Natural Language Interface to Databases (NLIDB) systems have gained a lot of focus recently owing to its fascinating aim of democratizing data access to non-technical business users. The research space for NL interfaces for data has evolved a lot since its inception, starting from simple keyword based queries, all the way to machine learning based systems, also dubbed as text-to-sql challenge. While the appeal of NLIDB system is common across different persona and use-cases, deploying a NLIDB system for an industry application has its own set of challenges which are often closely coupled with the exact domain and use-case. With no "one size fits all" solution in place, it is the right time for the community to review how the different methodologies adopted for NLIDB systems correlate with their applicability across different use-cases seen in academia and industry.

Bio: Jaydeep Sen is a Research Staff Member in IBM Research AI, India Lab . His research interests include applications for natural language understanding, semantic reasoning, designing intelligent algorithms for “learning from small data” applications. His work at IBM has powered some of IBM's most prominent QA and NL application portfolio. He has publications at conferences like VLDB, SIGMOD, IJCAI, IEEE SCC, EMNLP, COLING etc. and has served as Program Committee members for AAAI, SIGMOD, ICDE etc. He has more than 20 patents (granted/filed) in USPTO as of Dec-2021.

(iii) Immanuel Trummer (Cornell University, USA)

Title: Voice Interfaces for Data Access

Abstract:The communication between user and user is shifting more and more towards voice interfaces. This trend is evidenced by devices and services such as Google Home, Amazon Alexa, or Apple’s Siri. For many users, speech is the most natural form of interaction. It enables computer use from a distance, even in scenarios where hands or the visual attention are bound (e.g., while driving). All those advantages motivate the question of how to leverage voice interfaces for convenient data access. Accessing data via voice query interfaces is challenging. First, noisy speech recognition adds uncertainty on top of the inherent difficulties of natural language understanding. Second, transferring query results to users via voice output is difficult. Verbose speech output risks overwhelming the listener. Hence, output needs to summarize and to focus on the most important trends in the data. Recent research tackles some of those challenges. Still, many research questions remain open and must be answered to make the vision of natural data access via voice query interfaces a reality.

Bio: Immanuel Trummer is assistant professor at Cornell University, working towards making data analysis more efficient and more user-friendly. His papers were selected for “Best of VLDB”, “Best of SIGMOD”, for the ACM SIGMOD Research Highlight Award, and for publication in CACM as CACM Research Highlight. His current research is funded by the NSF and by multiple Google Faculty Research Awards.

(iv) Lei Zou (Peking University, China)

Title: Natural Language Question Answering over Knowledge Graph

Abstract: AS more and more structured data become available on the web, the question of how end users can access this body of knowledge becomes of crucial importance. As a de facto standard of a knowledge base, RDF (Resource Description Framework) repository is a collection of triples, denoted as . Although SPARQL is a standard way to access RDF data, it remains tedious and difficult for end users because of the complexity of the SPARQL syntax and the RDF schema. An ideal system should allow end users to profit from the expressive power of Semantic Web standards (such as RDF and SPARQLs) while at the same time hiding their complexity behind an intuitive and easy-to-use interface.
Generally, there are two categories of existing methods on natural language question answering (Q/A) over RDF database---one is IR (Information Retrieval)-based and the other one is called semantic parsing method. In this panel, I will talk about our solution gAnswer, which is based on graph matching-based technique, to design an effective natural language interface to access KG database.

Bio: Lei Zou is a professor at Peking University, China, and his recent research interests include graph databases, knowledge graph, particularly in graph-based RDF data management, natural language question answering over knowledge graph, hardware assisted graph database systems. Lei Zou’s research is supported by multiple NSFC projects. Prof. Zou also obtained Newton Advanced Fellowships of UK Royal Society. Lei Zou has publications at conferences like VLDB, SIGMOD, ICDE etc, and has served as Program Committee members for SIGMOD, VLDB and ICDE. He served PC Area Chair of ICDE 2021 and PC Chair of WISE 2022. Now, he is an Associate Editor of IEEE Transactions on Knowledge and Data Engineering (TKDE).

3. Tutorial talks

Date and Time (Hall) Tutorial talks
Tutorial 1
(April 12 (Tuesday), 2022, 14:45 to 16:30, Hall 5)

Title:

Make Wise Decisions for Your DBMSs: Workload Forecasting and Performance Prediction Before Execution

Speakers:

  • Zhengtong Yan (University of Helsinki)
  • Jiaheng Lu (University of Helsinki)
  • Qingsong Guo (University of Helsinki)
  • Gongsheng Yuan (University of Helsinki)
  • Calvin Sun (Huawei Toronto)
  • Steven Yang (Huawei Toronto)

Brief outline of the tutorial:

In this tutorial, we will focus on 1) how to forecast the future workloads (e.g., workload shift detection, arrival rate prediction, and next query prediction), and 2) how to analyze the behaviors of the workloads (e.g., execution time prediction and resource usage estimation). We will provide a comprehensive overview and detailed introduction of the two topics, from state-of-the-art methods, real-world applications, to open problems and future directions. Specifically, we will not only discuss traditional methods, such as time-series analysis, Markov modeling, analytical modeling, and experiment-driven methods, but also cover the state-of-the-art AI techniques, including machine learning, deep learning, reinforcement learning, and graph embedding.

Speakers Bio

Zhengtong Yan is a doctoral student at the University of Helsinki. His research topics include autonomous multi-model databases and cross-model query optimization.
Jiaheng Lu is a professor at the University of Helsinki. His main research interests lie in database systems specifically in the challenge of efficient data processing from real-life, massive data repositories and the Web. He has written four books on Hadoop and NoSQL databases, and more than 100 papers published in SIGMOD, VLDB, TODS, and TKDE, etc. He has given several tutorials on multi-model data management and autonomous databases in VLDB, CIKM, and EDBT conferences. He frequently serves as a PC member for conferences including SIGMOD, VLDB, ICDE, EDBT, CIKM, etc.
Qingsong Guo is a postdoctoral researcher at the University of Helsinki His research interests include multi-model databases and automatic management of big data with deep learning.
Gongsheng Yuan is a doctoral student at the University of Helsinki. His research topics lie in databases with quantum theory or reinforcement learning.
Calvin Sun is the Chief Database Architect at Huawei Cloud. He has 20+ years of experience in developing several database systems, ranging from embedded databases, large-scale distributed databases, to cloud-native databases.
Steven Yuan is the Director of Huawei Toronto Distributed Scheduling and Data Engine Lab. He leads a research team in the big data and cloud domain, focusing on distributed scheduling and distributed database, from IaaS to PaaS.
Tutorial 2
(April 12 (Tuesday), 2022, 10:15 to 12.30, Hall 5)

Title:

Reachability on Large-scale Graphs: Models, Techniques, and Trends

Speakers:

  • Xiaoshuang Chen (Guangzhou University)
  • Long Yuan (Nanjing University of Science and Technology)
  • Wenjie Zhang (University of New South Wales )
  • Ying Zhang (University of Technology Sydney)

Brief outline of the tutorial:

In this tutorial, we will first show the importance and challenges of studying reachability queries. Then, we will introduce the existing reachability models defined over different graphs. The computing algorithms regarding different models and environmental settings will also be presented. Finally, we will discuss future research directions in this important research area.

Speakers Bio

Xiaoshuang Chen is an Associate Professor in the Cyberspace Institute of Advanced Technology, Guangzhou University. Before that, she was a Postdoctoral Fellow in the School of Computer Science and Engineering, University of New South Wales. Her research interest lies in large-scale graph data analysis. She has published several papers in ICDE, VLDB and VLDBJ since 2017.
Long Yuan is a Professor in the School of Computer Science and Engineering, Nanjing University of Science and Technology, China. His research focuses on graph data management and analysis. He has published papers in top venues such as VLDB, WWW, ICDE, VLDBJ, and TKDE.
Wenjie Zhang is an Australian ARC Future Fellow (2021-2025) and Professor in the School of Computer Science and Engineering, University of New South Wales. Her research interests include spatial-temporal data analysis and graph data processing. She has published more than 100 papers in top venues such as TKDE, TODS, VLDBJ, SIGMOD, VLDB, and ICDE. She received the Discovery Early Career Researcher Award in 2011 and the prestigious Chris Wallace Award in 2019.
Ying Zhang is an Australian ARC Future Fellow and Professor at the University of Technology, Sydney (UTS). He has been the head of the database group at the Centre for Artificial Intelligence (CAI) since 2014. His research focuses on efficient query processing and analytics on big data. He has published more than 80 papers in top venues. He had received seven ARC grants which are under the National Competitive Grants Programme (NCGP) including one ARC ADP fellowship, one ARC DECRA fellowship, one ARC future fellowship and four ARC discovery projects.
Tutorial 3
(April 13 (Wednesday), 2022, 10:15 to 12:30, Hall 5)

Title:

A tutorial on biomedical image segmentation using deep learning

Speakers:

  • Sonali Agarwal (IIIT Allahabad)
  • Krishna Pratap Singh (IIIT Allahabad)
  • Sanjay Kumar Sonbhadra (ITER Bhubaneswar)
  • Narinder Singh Punn (IIIT Allahabad)

Brief outline of the tutorial:

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. Most medical applications require identifying and localizing the objects or regions (damaged tissues, cells or nuclei) found in medical imaging such as CAT scans, X-Rays, Ultrasound, etc. for diagnosis, monitoring and treatment. This delineation is generally performed by expert clinicians or radiologists which is a complex and time-consuming task. In recent studies, the implication of transfer learning and U-Net based approaches have illustrated state-of-the-art performance in different applications for the development of computer-aided diagnosis systems to localize the infected or damaged tissues or cells in the body using various modalities for early diagnosis and treatment of diseases such as brain tumor, lung cancer, Alzheimer, breast cancer, etc. With this motivation, this tutorial focuses on state-of-the-art deep learning approaches, a critical discussion of open challenges and directions for future research in the area of biomedical image segmentation.

Speakers Bio

Sonali Agarwal is working as an Associate Professor in the Information Technology Department of Indian Institute of Information Technology (IIIT), Allahabad, India. She received her Ph. D. Degree at IIIT Allahabad and joined as faculty at IIIT Allahabad, where she has been teaching since October 2009. She holds Bachelor of Engineering (B.E.) degree in Electrical Engineering from Bhilai Institute of Technology, Bhilai, (C.G.) India and Masters of Engineering (M.E.) degree in Computer Science from Motilal Nehru National Institute of Technology (MNNIT), Allahabad, India Her main research interests are in the areas of Artificial Intelligence and Big Data. She is the head of Big Data Analytics Lab at IIIT Allahabad, India.
Krishna Pratap Singh is working as an Associate professor in the Information Technology Department at IIIT Allahabad, India. He received his PhD and Master from IIT Roorkee. He has been working at IIIT Allahabad since 2009. His main research areas are Machine Learning, Transfer Learning and Optimization.
Sanjay Kumar Sonbhadra is presently working as Assistant Professor in the Computer Science and Engineering Department of ITER, Shiksha ‘O’ Anusandhan, Bhubaneswar, Odisha, India. He is mainly working on One-class classification, Anomaly detection, Target class guided dimensionality reduction and training sample selection techniques and Big data analytics. During 2017-2021, he worked as a senior member of “Big Data Analytics Lab” at IIIT Allahabad, India. He has published many articles in the area of machine learning applications to address recent challenges of COVID-19. He has working experience of machine learning algorithms to address the challenging problem of target specific learning with limited target samples.
Narinder Singh Punn is working as a Teaching Research Assistant (TRA) in the Information Technology Department of Indian Institute of Information Technology (IIIT), Allahabad, India. Narinder’s main research includes Medical Imaging segmentation, Deep learning and Artificial Intelligence techniques in healthcare. He is a senior member of “Big Data Analytics Lab” at IIIT Allahabad, India. His recent publications cover applications of deep learning in the detection and prevention of COVID-19, while also exploiting the potential of self-supervised learning in biomedical image segmentation.
Tutorial 4
(April 13 (Wednesday), 2022, 14:30 to 16:15, Hall 5)

Title:

AI Meets NoSQL Database: Methods, Opportunities and Challenges

Speakers:

  • Hongzhi Wang (Harbin Institute of Technology)
  • Zhixin Qi (Harbin Institute of Technology)
  • Yu Yan (Harbin Institute of Technology)

Brief outline of the tutorial:

This tutorial is planned for 1.5 hours and consists of the following parts.
(1) Background and Motivation (10’): We introduce the background of AI for database and motivate the need for applying AI techniques on NoSQL database with several scenarios.
(2) Cost Estimation for Graph and Document Databases (20’): We discuss how AI techniques estimate query costs for graph and document databases.
(3) Physical Design for Key-Value and Graph Databases (20’): We introduce physical design methods for key-value and graph databases based on AI techniques.
(4) Index Recommendation for Key-Value and Document Databases (15’): We discuss existing AI-based index recommendation approaches for key-value and document databases.
(5) Opportunities and Challenges (20’): We present the research opportunities and challenges for NoSQL database management based on AI techniques.
(6) Summary (5’): We summarize this tutorial and give our critical thoughts to AI for NoSQL database.

Speakers Bio

Hongzhi Wang, Professor, PHD supervisor, the head of massive data computing center and the vice dean of the honors school of Harbin Institute of Technology, the secretary general of ACM SIGMOD China, outstanding CCF member, a standing committee member CCF databases and a member of CCF big data committee. Research Fields include big data management and analysis, database systems, knowledge engineering and data quality. He was “starring track” visiting professor at MSRA and postdoctoral fellow at University of California, Irvine. Prof. Wang has been PI for more than 10 national or international projects including NSFC key project, NSFC projects and National Technical support project, and co-PI for more than 10 national projects include 973 project, 863 project and NSFC key projects. He also serves as a member of ACM Data Science Task Force. He has won First natural science prize of Heilongjiang Province, MOE technological First award, Microsoft Fellowship, IBM PHD Fellowship and Chinese excellent database engineer. His publications include over 300 papers in the journals and conferences such as VLDB Journal, IEEE TKDE, VLDB, SIGMOD, ICDE and SIGIR, 6 books and 6 book chapters. His PHD thesis was elected to be outstanding PHD dissertation of CCF and Harbin Institute of Technology. He severs as the reviewer of more than 20 international journal including VLDB Journal, IEEE TKDE, and PC members of over 50 international conferences including SIGMOD, VLDB, KDD, ICML, NeurpIS, ICDE, etc. His papers were cited more than 3000 times. His personal website is http://homepage.hit.edu.cn/wang.
Zhixin Qi is currently a PhD student in School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China. She received her bachelor degree from Harbin Engineering University in 2016, and received her master degree from Harbin Institute of Technology in 2018. Her research interests include AI4DB, knowledge graph, and graph data management. She was awarded National Scholarship for PhD students in 2021, National Scholarship for master students in 2017, and National Scholarship for undergraduates in 2014. She has published more than 10 papers in international journals and conferences, including TKDE, KAIS, KBS, Neurocomputing, JCST, CIKM, and DASFAA.
Yu Yan is currently a PhD student in School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China. She received her master degree from Harbin Institute of Technology in 2021. She committed to the research of database tuning, multi-model database, and database auto-management. She got National Scholarship for master students in 2020. She has published many papers in international conferences and journals, such as Information Sciences, ApWeb and etc.
Tutorial 5
(April 14 (Thursday), 2022, 10:15 to 12:30, Hall 5)

Title:

Time Series Anomaly Detection Toolkit for AI Applications [Slides]

Speakers:

  • Dhaval Patel (IBM Research)
  • Dzung Phan (IBM Research)
  • Markus Mueller (IBM Germany)

Brief outline of the tutorial:

The tutorial is organized in a sequence of three sections: Introduction, Theory and Hands-on-demo. In part one, we will briefly discuss foundations of time series dataset with the help of real-world examples. We will also present a broad taxonomy of time series dataset. We will also present general definition of anomalies in time series data and discuss three common variants of Anomaly/Outlier Detection problems. Next, we discuss basic machine learning primitives such as Estimator, Transformer, Data Stationarizer, etc that are useful for building anomaly pipeline. In machine learning field, these components become a backbone for building a complex model learning pipelines. We will formally introduce the key API such as ``fit'', ``predict'', ``decision\_function'', to the participant with the help of 30+ different anomaly detection algorithms. Apart from provide the categorization of these algorithms, we will also discuss one algorithm namely Gaussian Graphical Model for interpretable anomaly detection. The access to the toolkit is made available via IBM API Hub Platform (https://developer.ibm.com/apis/catalog/ai4industry--anomaly-detection-product/Introduction). The example notebooks are accessible at IBM's public github (https://github.com/IBM/anomaly-detection-code-pattern/). The tutorial finally analyzes open issues and future directions in this vibrant and rapidly evolving research area.

Speakers Bio

Dhaval Patel work at IBM Research since 2016. Dr. Dhaval Patel hold PhD in Computer Science from National University of Singapore and master's degree in Information Technology from Indian Institute of Technology – Kharagpur. Dr. Patel is an expert in Data Mining, Machine Learning, Time Series Data Analysis, etc. The significance of his research contributions has been demonstrated in 60+ published papers (10 journal papers and 50+ conference papers) in high impact, refereed, top-notch venues in Database, Data Mining, Big Data and Machine Learning, 1 issued US patent and 18 patent applications. He is recipients of 9 outstanding technical/research accomplishments awards from IBM for advancing AI technology to solve several real-world industrial problems. He is key contributor in many Flagship IBM Product including AutoAI-TS, Maximo Application Suites for Anomaly Detection at Scale, etc.
Dzung Phan is a Research Staff Member at IBM Research, New York, USA since 2010. He received a Ph.D. degree in applied mathematics from the University of Florida in 2010, a M.S. degree in computational engineering from National University of Singapore in 2004, and a B.S. degree in mathematics from Vietnam National University, Hanoi in 2001. His research interests include optimization theory and algorithms, machine learning, and operations research. In particular, he is currently working on anomaly and change detection, sparse learning, and data-driven decision making. He has published more than 40 technical papers in refereed conferences and journals including top machine learning/data mining conferences such as ICML, NeurIPS, IJCAI, and ICDM. He has also filed about 40 U.S. patents. He received the 2012 Pat Goldberg Best Paper Award and a 2020 INFORMS Wagner Prize semi-finalist.
Markus Muller studied Math, Operations Research and Computer Science and has over 2 decades of experience in IT in different roles, mainly as software architect. He has shifted towards Machine Learning in 2018, first as an architect for an NLU related offering, then as data scientist for an offering in the IIoT space. Markus worked at IBM Watson Center, Munich.

4. Workshops (April 11, 2022, Monday, 09.00- 18.00)


Workshop Name Program
International Workshop on Blockchain Technologies (IWBT2022) https://sites.google.com/ahduni.edu.in/iwbt2022/home
9:00 – 9:15 (IST) "Welcome and Introduction" by Dr. Sanjay Chaudhary, Workshop co-chair, Dean and Professor, Ahmedabad University
9:15 - 10:15 (IST) Keynote Talk: "From Impossible Triangles to innovations through Blockchains" by Professor C. Pandu Rangan, Sathish Dhawan Chair professor, Computer science and Automation, IISc, Bangalore, India
10:15 to 11:00 (IST) Invited Talk: "SnarkWars: The promise of Zero Knowledge Proofs for Scaling Blockchains" by Dr. Nitin Singh, Senior Research Scientist, IBM Research India
11:00 - 11:15 (IST) Tea / Coffee Break
11:15 - 12:00 (IST) Invited Talk: "Proofs of Storage and Applications" by Dr. Sushmita Ruj, Senior Lecturer, School of Computer Science and Engineering, UNSW, Sydney
12:00 - 12:30 (IST) Research Paper Presentation
"Securing Cookies/Sessions through Non-Fungible Tokens" by Dr. Kaushal Shah, Uday Khokhariya, Nidhay Pancholi, Shambhavi Kumar and Keyur Parmar, Pandit Deendayal Energy University (PDEU), Gandhinagar, India
12:30 - 14:00 (IST) Lunch Break
14:00 - 14:45 (IST) Invited Talk “Next-Generation Blockchains -- Potential and Challenges” by Dr. Sujit Prakash Gujar, Assistant Professor, International Institute of Information Technology, Hyderabad, India
14:45 - 16:15(IST) Research Paper Presentations
"Towards a Blockchain Solution for Customs Duty-Related Fraud" by Dr. Christopher G. Harris, Associate Professor, Computer Science, School of Mathematical Sciences, University of Northern Colorado, USA (14:45 - 15:15)
"Model-Driven Development of Distributed Ledger Applications" by Piero Fraternali, Dr. Sergio Luis Herrera Gonzalez, Matteo Frigerio and Mattia Righetti, Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, Milan, Italy (15:15 - 15:45)
"Collaborative Blockchain based Distributed Denial of Service Attack Mitigation approach with IP Reputation System" by Darshi Patel and Dr. Dhiren Patel, Gujarat Vidyapith, India (15:45 - 16:15)
16:15 - 16:30(IST) Tea / Coffee Break
16:30 - 17:30(IST) Panel Discussion on "Trends and Challenges in the adoption of blockchain for real-world problems" by
Dr. Raghavendra Deshmukh, Engineering Leader, Google
Dr. Sandip Chakraborty, Associate Professor, Indian Institute of Technology, Kharagpur, West Bengal, India
Dr. Anu Singh, Blockchain Supply Chain Transformation Leader, IBM
17:30 - 17:45(IST) Concluding Remarks by Dr. Krishnasuri Narayanam, Workshop co-chair, Senior Research Software Engineer, IBM Research, India
Managing Air Quality Through Data Science https://cs.ashoka.edu.in/maqtds/
9:30 – 9:45 AM(IST) Welcome and Introduction to Workshop
9:45 – 10:00 AM(IST) Opening address by "Prof. Gautam Menon, Director, Centre for Climate Change and Sustainability (3CS) at Ashoka University"
10:00 – 11:00 AM(IST) Keynote 1: Big Data Resources to Support Research Opportunities on Air Pollution Analysis in India by "Dr. Sarath Guttikunda, Adjunct Faculty, TRIP-C, IIT Delhi, and Founder/ Director, Urban Emissions"
11:00 – 11:15 AM(IST) Break
11:15 – 11:45 AM(IST) Presentation 1: Leveraging geospatial technology for air quality monitoring by "Tejasvini Puri, BlueSky Analytics"
11:45 AM – 12:15 PM(IST) Presentation 2: Visualizing Spatio-Temporal Variation of Ambient air pollution in selected small cities in India by "Prof. Girish Agrawal & Hifzur Rahman, OP Jindal Global University"
12:15 – 1:15 PM(IST) Lunch Break
1:15 – 1:45 PM(IST) Presentation 3: Air Quality Data Collection in Hyderabad using Low Cost Sensors: Initial Experiences by "Srinivas Annappalli & Chandra Shekar N, IIIT Hyderabad"
1:45 – 2:45 PM(IST) Keynote 2: Making air pollution legible! Data and governance in Indian cities by "Dr. Anant Maringanti, Director, Hyderabad Urban Lab"
2:45 – 3:15 PM(IST) Presentation 4: AIR-Tree: Air-quality Indices based R-Tree for Air-Quality Data Management by "Prof. Anirban Mondal & Mr. Raghav Mittal, Ashoka University"
3:15 – 4:00 PM(IST) Panel Discussion moderated by Prof. Anirban Mondal: Collaboration across a range of STEM and social sciences disciplines towards air quality improvement
Dr. Jai Ganesh (Mphasis) Mr. Vikas Sahni (Ashoka University) Dr. Anant Maringanti (Hyderabad Urban Lab) Prof. Girish Agrawal (OP Jindal Global University) Prof. P. Krishna Reddy (IIIT Hyderabad)
4:00 – 4:15 PM(IST) Closing Remarks
1st Workshop on Pattern mining and Machine learning in Big complex Databases (PMBD 2022) https://philippe-fournier-viger.com/PMDB_2021/program.html
8:30 AM(IST) Opening of the workshop
8:45 AM(IST) Keynote Talk: Approximate Computing for Big Data Analysis by Distinguished Prof. Joshua Zhexue Huang
9:45 AM(IST) Paper #2 An Algorithm for Mining Fixed-Length High Utility Itemsets by "Le Wang"
10:05 AM(IST) Paper #3 A Novel Method to Create Synthetic Samples with Autoencoder Multi-layer Extreme Learning Machine by "Qihang Huang, Yulin He, Shengsheng Xu and Joshua Zhexue Huang"
10:25 AM(IST) Paper #4 Pattern Mining: Current Challenges and Opportunities by "Philippe Fournier-Viger, Wensheng Gan, Youxi Wu, Mourad Nouioua, Wei Song, Tin Truong and Hai Duong Van"
10:45 AM(IST) Paper #7 Why not to Trust Big Data: Identifying Existence of Simpson’s Paradox by "Rahul Sharma, Minakshi Kaushik, Sijo Arakkal Peious, Mahtab Shahin, Ankit Vidhyarthi and Dirk Draheim"
11:05 AM(IST) Paper #8 Localized Metric Learning for Large Multi-Class Extremely Imbalanced Face Database by "Seba Susan and Ashu Kaushik"
11:25 AM(IST) Paper #9 Top-k dominating queries on incremental datasets by "Jimmy Ming-Tai Wu, Ke Wang and Jerry Chun-Wei Lin"
11:45 AM(IST) Closing of the workshop
The 8th International Workshop on Big Data Management and Service (BDMS 2022) https://zheng-kai.com/cfp
9.00-12.30 AM(IST) Morning session
Session Chair: Kevin Zheng, An Liu, Xiaoling Wang
BDMS001: H-V:An Improved Coding Layout based on Erasure Coded Storage System (9:00-9:20)
BDMS002: An Autoencoder-based Model for Pedestrian Trajectory Prediction of Variable-Length (9:20-9:40)
BDMS003: A Survey on Spatiotemporal Data Processing Techniques in Smart Urban Rail (9:40-10:00)
BDMS004: Fast Vehicle Track Counting in Traffic Video (10:00-10:20)
BDMS005: TSummary A Traffic Summarization System using Semantic Words (10:20-10:40)
BDMS006: Attention Cooperated Reinforcement Learning for Multi agent Path Planning (10:40-11:00)
BDMS007: Big Data-driven Stable Task Allocation in Ride-hailing Services (11:00-11:20)
BDMS008: Weighted Mean Field Multi Agent Reinforcement Learning via Reward Attribution Decomposition (11:20- 11:40)
Keynotes: Zero-Shot Event Extraction Based on Contrastive Learning (11:40-12:30) by Wendi Ji
The 7th International Workshop on Big Data Quality Management (BDQM 2022) https://bdqm2022.github.io/
9:00-10:10 AM(IST) Invited Talk: Title: Big Data Management and Analytics via Direct Computing on Compression by "Feng Zhang, Renmin University of China".
10:10-11:20 AM(IST) Invited Talk: Title: Data Quality Management: Turn Waste into Wealth by "Shaoxu Song, Tsinghua University, China".
11:20-12:50 AM(IST) Paper presentations
Evaluating Presto and SparkSQL with TPC-DS by "Yinhao Hong, Sheng Du, Jianquan Leng (Renmin University of China, Beijing Kingbase Information Technology Co., Ltd, Beijing, China)"
Optimizing the Age of Sensed Information in Cyber-Physical Systems by "Yinlong Li,Siyao Cheng,Feng Li, Jie Liu, Hanling Wu (Harbin Institute of Technology, Harbin Institute of Technology(shenzen), Beijing Institute of Astronautical Systems Engineering, China)"
Aggregate Query Result Correctness using Pattern Tables by "Nitish Yadav, Ayushi Malhotra, Sakshee Patel, Minal Bhise (Distributed Databases Group, DAIICT, Gandhinagar, India)"
Time Series Data Quality Enhancing based on Pattern Alignment by "Jianping Huang, Hao Chen, Hongkai Wang, Jun Feng, Liangying Peng, Zheng Liang, Hongzhi Wang, Tianlan Fan, Tianren Yu (State Grid Zhejiang Electric Power Co., Ltd, State Grid Zhejiang Information and Telecommunication Branch, China, Harbin Institute of Technology)"
Research on feature extraction method of data quality intelligent detection by "Weiwei Liu,Shuya Lei, Xiaokun Zheng, Xiao Liang (Artificial Intelligence on Electric Power System State Grid Corporation Joint Laboratory(GEIRI) China)"
The 6th International Workshop on Graph Data Management and Analysis (GDMA 2022) https://gdma2022.github.io/
14:00-14:35 (IST) Keynote by Tieyun Qian (Wuhan University) Title: Diversity and Novelty Oriented Recommendation for Long-tailed Items
14:35-15:10 (IST) Keynote by Peng Peng (Hunan University) Title: Distributed RDF Graph Management
15:20-16:45 (IST) (Oral Paper) Chinese Spelling Error Detection and Correction Based on Knowledge Graph
16:45-17:10 (IST) (Oral Paper) Construction and Application of Event Logic Graph: A Survey
17:10-17:35 (IST) (Oral Paper) Enhancing Low-resource Languages Question Answering with Syntactic Graph
17:35-18:00 (IST) (Oral Paper) Profile Consistency Discrimination

5. Research Sessions

Research Session Id Session Name Paper ID Paper Title
(Date, Time, and Hall)
1 Research Session #1 (Queries)

(April 12 (Tuesday), 2022, 10.15-12.30, Hall 1)
20 Approximate Continuous Top-K Queries Over Memory Limitation-based Streaming Data
[Abstract]
Rui Zhu (School of Computer Science, Shenyang Aerospace University, China); Liu Meng (School of Computer Science, Shenyang Aerospace University, China); Bin Wang (College of Computer Science and Engineering, Northeastern University, China); Xiaochun Yang (College of Computer Science and Engineering, Northeastern University, China); Xiufeng Xia (School of Computer Science, Shenyang Aerospace University, China);
22 Cross-Model Conjunctive Queries over Relation and Tree-structured Data
[Abstract]
Yuxing Chen (University of Helsinki); Valter Uotila (University of Helsinki); Jiaheng Lu (University of Helsinki); Zhen Hua Liu (Oracle); Souripriya Das (Oracle);
178 Leveraging Search History for Improving Person-Job Fit
[Abstract]
Yupeng Hou (Gaoling School of Artificial Intelligence, Renmin University of China, China); Xingyu Pan (School of Information, Renmin University of China, China); Wayne Xin Zhao (Gaoling School of Artificial Intelligence, Renmin University of China, China); Shuqing Bian (School of Information, Renmin University of China, China); Yang Song (BOSS Zhipin, Beijing, China); Tao Zhang (BOSS Zhipin, Beijing, China); Ji-Rong Wen (Gaoling School of Artificial Intelligence, Renmin University of China, China);
503 Efficient In-Memory Evaluation of Reachability Graph Pattern Queries on Data Graphs
[Abstract]
Xiaoying Wu (Wuhan University, China); Dimitri Theodoratos (New Jersey Institute of Technology, USA); Dimitrios Skoutas (R.C. Athena, Athens, Greece); Michael Lan (New Jersey Institute of Technology, USA);
523 Revisiting Approximate Query Processing and Bootstrap Error Estimation on GPU
[Abstract]
Hang Zhao (Fudan University, Shanghai, China); Hanbing Zhang (Fudan University, Shanghai, China); Yinan Jing (Fudan University, Shanghai, China); Kai Zhang (Fudan University, Shanghai, China); Zhenying He (Fudan University, Shanghai, China); X. Sean Wang (Fudan University, Shanghai, China);
257 μ-join: Efficient Join with Versioned Dimension Tables
[Abstract]
Mika Takata (The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, Japan); Kazuo Goda (The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, Japan); Masaru Kitsuregawa (The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, Japan);
532 Learning-based Optimization for Online Approximate Query Processing
[Abstract]
Wenyuan Bi (Fudan University, Shanghai, China); Hanbing Zhang (Fudan University, Shanghai, China); Yinan Jing (Fudan University, Shanghai, China); Zhenying He (Fudan University, Shanghai, China); Kai Zhang (Fudan University, Shanghai, China); X. Sean Wang (Fudan University, Shanghai, China);
2 Research Session #2 (Text and Image-I),

(April 12 (Tuesday), 2022, 10.15-12.30, Hall 2)
65 Emotion-aware Multimodal Pre-training for Image-grounded Emotional Response Generation
[Abstract]
Zhiliang Tian (The Hong Kong University of Science and Technology, Hong Kong SAR, China); Zhihua Wen (Science and Technology on Parallel and Distributed Laboratory, National University of Defense Technology, Hunan, China); Zhenghao Wu (The Hong Kong University of Science and Technology, Hong Kong SAR, China); Yiping Song (National University of Defense Technology, Hunan, China); Jintao Tang (National University of Defense Technology, Hunan, China); Dongsheng Li (Science and Technology on Parallel and Distributed Laboratory, National University of Defense Technology, Hunan, China); Nevin L. Zhang (The Hong Kong University of Science and Technology, Hong Kong SAR, China);
416 KdTNet: Medical Image Report Generation via Knowledge-driven Transformer
[Abstract]
Yiming Cao (School of Software, Shandong University, China); Lizhen Cui (Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, China); Fuqiang Yu (School of Software, Shandong University, China); Lei Zhang (School of Software, Shandong University, China); Zhen Li (Department of Gastroenterology, Qilu Hospital of Shandong University, China); Ning Liu (School of Software, Shandong University, China); Yonghui Xu (Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, China);
464 AdCSE: An Adversarial Method for Contrastive Learning of Sentence Embeddings
[Abstract]
Renhao Li (School of Computer Science, Sichuan University, Chengdu, China); Lei Duan (School of Computer Science, Sichuan University, Chengdu, China); Guicai Xie (School of Computer Science, Sichuan University, Chengdu, China); Shan Xiao (School of Computer Science, Sichuan University, Chengdu, China); Weipeng Jiang (School of Computer Science, Sichuan University, Chengdu, China);
502 HRG: A Hybrid Retrieval and Generation model in Multi-turn Dialogue Generation
[Abstract]
Deji Zhao (School of Information Science and Technology, Dalian Maritime University, Dalian, China); Xinyi Liu (School of Information Science and Technology, Dalian Maritime University, Dalian, China); Bo Ning (School of Information Science and Technology, Dalian Maritime University, Dalian, China); Chengfei Liu (Swinburne University of Technology, Australia);
122 SimEmotion: A Simple Knowledgeable Prompt Tuning Method for Image Emotion Classification
[Abstract]
Sinuo Deng (Faculty of Information Technology, Beijing University of Technology, Beijing, China); Ge Shi (Faculty of Information Technology, Beijing University of Technology, Beijing, China); Lifang Wu( (Faculty of Information Technology, Beijing University of Technology, Beijing, China); Lehao Xing (Faculty of Information Technology, Beijing University of Technology, Beijing, China); Wenjin Hu (Faculty of Information Technology, Beijing University of Technology, Beijing, China); Heng Zhang (Faculty of Information Technology, Beijing University of Technology, Beijing, China); Ye Xiang (Faculty of Information Technology, Beijing University of Technology, Beijing, China);
196 E-commerce Knowledge Extraction via Multi-modal Machine Reading Comprehension
[Abstract]
Chaoyu Bai (School of Cyber Science and Engineering, Southeast University, Nanjing, China);
403 Concurrent Transformer for Spatial-Temporal Graph Modeling
[Abstract]
Yi Xie (School of Computer Science, Fudan University, Shanghai, China); Yun Xiong (School of Computer Science, Fudan University, Shanghai, China); Yangyong Zhu (School of Computer Science, Fudan University, Shanghai, China); Philip S. Yu (University of Illinois at Chicago, Chicago, U.S.A); Cheng Jin (Shanghai Meteorological Disaster Prevention Technology Center, Shanghai, China); Qiang Wang (Shanghai Meteorological Disaster Prevention Technology Center, Shanghai, China); Haihong Li (Shanghai Meteorological Disaster Prevention Technology Center, Shanghai, China);
519 Modeling Uncertainty in Neural Relation Extraction
[Abstract]
Hang Zhao (School of Computer Science, Fudan University, Shanghai, China); Hanbing Zhang (School of Computer Science, Fudan University, Shanghai, China); Yinan Jing (School of Computer Science, Fudan University, Shanghai, China); Kai Zhang (School of Computer Science, Fudan University, Shanghai, China); Zhenying He (School of Computer Science, Fudan University, Shanghai, China); X. Sean Wang (School of Computer Science, Fudan University, Shanghai, China);
3 Research Session #3 (Applications of ML-I),

(April 12 (Tuesday), 2022, 10.15-12.30, Hall 3)
174 Similarity-Aware Collaborative Learning for Patient Outcome Predcition
[Abstract]
Fuqiang Yu (School of Software, Shandong University, China); Lizhen Cui (Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, China); Yiming Cao (School of Software, Shandong University, China); Ning Liu (School of Software, Shandong University, China); Weiming Huang (School of Software, Shandong University, China); Yonghui Xu (Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, China);
252 Heterogeneous Federated Learning via Grouped Sequential-to-Parallel Training
[Abstract]
Shenglai Zeng (Sch of Info & Comm Engin, University of Electronic Science and Technology of China, Chengdu, China); Zonghang Li (Sch of Info & Comm Engin, University of Electronic Science and Technology of China, Chengdu, China); Hongfang Yu (Sch of Info & Comm Engin, University of Electronic Science and Technology of China, Chengdu, China); Yihong He (Sch of Info & Comm Engin, University of Electronic Science and Technology of China, Chengdu, China); Zenglin Xu (Sch of Comp Sci & Tech, Harbin Institute of Technology, Shenzhen, China); Dusit Niyato (Sch of Comp Sci & Engin, Nanyang Technological University, Singapore); Han Yu (Sch of Comp Sci & Engin, Nanyang Technological University, Singapore);
335 Peripheral Instance Augmentation for End-to-End Anomaly Detection using Weighted Adversarial Learning
[Abstract]
Weixian Zong (School of Data Science and Engineering, East China Normal University, Shanghai, China); Fang Zhou (School of Data Science and Engineering, East China Normal University, Shanghai, China); Martin Pavlovski (Temple University, Philadelphia, United States); Weining Qian (School of Data Science and Engineering, East China Normal University, Shanghai, China);
342 HieNet: Bidirectional Hierarchy Framework for Automating ICD Coding
[Abstract]
Shi Wang (Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, China); Daniel Tang (University of Luxembourg, Interdisciplinary Centre for Security, Reliability and Trust (SNT), TruX, Luxembourg); Luchen Zhang (National Computer Network Emergency Response Technical Team/Coordination Center of China); Huilin Li (Department of Civil Engineering, Technical University of Denmark, 2800 Lyngby, Denmark); Ding Han (Huazhong University of Science and Technology, China);
2 Data Source Selection in Federated Learning: A Submodular Optimization Approach
[Abstract]
Ruisheng Zhang (State Key Laboratory of Software Development Environment, Beihang University, China); Yansheng Wang (State Key Laboratory of Software Development Environment, Beihang University, China); Zimu Zhou (Singapore Management University, Singapore, Singapore); Ziyao Ren (State Key Laboratory of Software Development Environment, Beihang University, China); Yongxin Tong (State Key Laboratory of Software Development Environment, Beihang University, China); Ke Xu (State Key Laboratory of Software Development Environment, Beihang University, China);
41 CLZT:A Contrastive Learning Based Framework for Zero-Shot Text Classification
[Abstract]
Kun Li (School of Cyber Security, University of Chinese Academy of Sciences, China); Meng Lin (School of Cyber Security, University of Chinese Academy of Sciences, China); Songlin Hu (School of Cyber Security, University of Chinese Academy of Sciences, China); Ruixuan Li (Institute of Information Engineering, Chinese Academy of Sciences, China);
248 An Interpretable Time Series Classification Approach Based on Feature Clustering
[Abstract]
Fan Qiao (Fudan University, Shanghai, China); Peng Wang (Fudan University, Shanghai, China); Wei Wang (Fudan University, Shanghai, China); Binjie Wang (Beijing Jiaotong University, Beijing, China);
332 Supervised Multi-view Latent Space Learning by Jointly Preserving Similarities across Views and Samples
[Abstract]
Xiaoyang Li (School of Data Science and Engineering, East China Normal University, Shanghai, China); Martin Pavlovski (Temple University, Philadelphia, Pennsylvania, United States); Fang Zhou (School of Data Science and Engineering, East China Normal University, Shanghai, China); Qiwen Dong (School of Data Science and Engineering, East China Normal University, Shanghai, China); Weining Qian (School of Data Science and Engineering, East China Normal University, Shanghai, China); Zoran Obradovic (Temple University, Philadelphia, Pennsylvania, United States);
4 Research Session #4 (Graphs-I),

(April 12 (Tuesday), 2022, 14.45-16.30 , Hall 1)
11 Cascade-Enhanced Graph Convolutional Network for Information Diffusion Prediction
[Abstract]
Ding Wang (School of Cyber Security, University of Chinese Academy of Sciences); Lingwei Wei (School of Cyber Security, University of Chinese Academy of Sciences); Chunyuan Yuan (JD.com, Beijing, China); Yinan Bao (School of Cyber Security, University of Chinese Academy of Sciences); Wei Zhou (School of Cyber Security, University of Chinese Academy of Sciences); Xian Zhu (School of Cyber Security, University of Chinese Academy of Sciences); Songlin hu (School of Cyber Security, University of Chinese Academy of Sciences);
15 Diversify Search Results through Graph Attentive Document Interaction
[Abstract]
Xianghong Xu (Shenzhen International Graduate School, Tsinghua University, China); Kai Ouyang (Shenzhen International Graduate School, Tsinghua University, China); Yin Zheng (Department of Search and Application, Weixin Group, Tencent, China); Yanxiong Lu (Department of Search and Application, Weixin Group, Tencent, China); Hai-Tao Zheng (Shenzhen International Graduate School, Tsinghua University, China); Hong-Gee Kim (Seoul National University, South Korea);
365 Learning Robust Representation through Graph Adversarial Contrastive Learning
[Abstract]
Jiayan Guo (School of Artificial Intelligence, Peking University, Beijing, China); , Shangyang Li (Peking-Tsinghua Center for Life Sciences, IDG/McGovern Institute for Brain Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China); , Yue Zhao (Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China); Yan Zhang (School of Artificial Intelligence, Peking University, Beijing, China);
197 Contrastive Disentangled Graph Convolutional Network for Weakly-supervised Node Classification
[Abstract]
Xiaokai Chu (Institute of Computing Technology, Chinese Academy of Sciences, China); Jiashu Zhao (Wilfrid Laurier University, Canada); Xinxin Fan (Institute of Computing Technology, Chinese Academy of Sciences, China); Di Yao (Institute of Computing Technology, Chinese Academy of Sciences, China); Zhihua Zhu (Institute of Computing Technology, Chinese Academy of Sciences, China); Lixin Zou (Baidu Inc., China); Dawei Yin (Baidu Inc., China); Jingping Bi (Institute of Computing Technology, Chinese Academy of Sciences, China);
353 CSGNN: Improving Graph Neural Networks with Contrastive Semi-Supervised Learning
[Abstract]
Yumeng Song (School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China); Yu Gu (School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China); Xiaohua Li (School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China); Chuanwen Li (School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China); Ge Yu (School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China);
5 Research Session #5 (Text and Image-II),

(April 12 (Tuesday), 2022, 14.45-16.30 , Hall 2)
271 Tracking the Evolution: Discovering and Visualizing the Evolution of Literature
[Abstract]
Siyuan Wu (State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau, Macau SAR, China); Leong Hou U (State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau, Macau SAR, China);
323 Incorporating Commonsense Knowledge into Story Ending Generation via Heterogeneous Graph Networks
[Abstract]
Jiaan Wang (School of Computer Science and Technology, Soochow University, Suzhou, China); Beiqi Zou (Department of Computer Science, Princeton University, USA); Zhixu Li (Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China); Jianfeng Qu (School of Computer Science and Technology, Soochow University, Suzhou, China); Pengpeng Zhao (School of Computer Science and Technology, Soochow University, Suzhou, China); An Liu (School of Computer Science and Technology, Soochow University, Suzhou, China); Lei Zhao (School of Computer Science and Technology, Soochow University, Suzhou, China);
400 Open-domain Dialogue Generation Grounded with Dynamic Multi-form Knowledge Fusion
[Abstract]
Feifei Xu (School of Computer Science and Technology, Shanghai University of Electric Power 201306, China); Shanlin Zhou (School of Computer Science and Technology, Shanghai University of Electric Power 201306, China); Yunpu Ma (Chair of Database Systems and Data Mining, University of Munich 80538, Munich); Xinpeng Wang (School of Electronics and Information Engineering, Tongji University 201804, China); Wenkai Zhang (School of Computer Science and Technology, Shanghai University of Electric Power 201306, China); Zhisong Li (Data Intelligence Center, Alibaba Local Life Service Co., Ltd 200062, China);
158 Knowing What I Don’t Know: A Generation Assisted Rejection Framework in Knowledge Base Question Answering
[Abstract]
Junyang Huang (Fudan University, Shanghai, China); Xuantao Lu (Fudan University, Shanghai, China); Jiaqing Liang (Fudan University, Shanghai, China); Qiaoben Bao (Fudan University, Shanghai, China); Chen Huang (CES Finance Co., Ltd., Shanghai, China); Yanghua Xiao (Fudan University, Shanghai, China); Bang Liu (Universit´e de Montr´eal, Montr´eal, Qu´ebec, Canada); Yunwen Chen (DataGrand Inc., Shanghai, China);
348 PromptMNER: Prompt-based Entity-related Visual Clue Extraction and Integration for Multimodal Named Entity Recognition
[Abstract]
Xuwu Wang (Shanghai Key Lab. of Data Science, School of Computer Science, Fudan University); Junfeng Tian (Alibaba DAMO Academy, Hangzhou, China); Min Gui (Shopee, Singapore, Singapore); Zhixu Li (Shanghai Key Lab. of Data Science, School of Computer Science, Fudan University); Jiabo Ye (East China Normal University, Shanghai, China); Ming Yan (Alibaba DAMO Academy, Hangzhou, China); Yanghua Xiao (Shanghai Key Lab. of Data Science, School of Computer Science, Fudan University);
423 Towards Personalized Review Generation with Gated Multi-source Fusion Network
[Abstract]
Hongtao Liu (State Key Laboratory of Communication Content Cognition, Beijing, China); Wenjun Wang (State Key Laboratory of Communication Content Cognition, Beijing, China); Hongyan Xu (State Key Laboratory of Communication Content Cognition, Beijing, China); Qiyao Peng (State Key Laboratory of Communication Content Cognition, Beijing, China); Pengfei Jiao (School of Cyberspace, Hangzhou Dianzi University, Hangzhou, China); Yueheng Sun (State Key Laboratory of Communication Content Cognition, Beijing, China);
506 Definition-Augmented Jointly Training Framework for Intention Phrase Mining
[Abstract]
Denghao Ma (Meituan, Beijing, China); Yueguo Chen (DEKE Lab, Renmin University of China, Beijing, China); Changyu Wang (JD.com, Inc., Beijing, China); Hongbin Pei (Xi’an Jiaotong University, Xian, China); Yitao Zhai (Meituan, Beijing, China); Gang Zheng (Meituan, Beijing, China); Qi Chen (Meituan, Beijing, China);
6 Research Session #6 (Text and Image-II),

(April 12 (Tuesday), 2022, 14.45-16.30 , Hall 3)
99 Inter- and Intra-Domain Relation-aware Heterogeneous Graph Convolutional Networks for Cross-Domain Recommendation
[Abstract]
Ke Wang (Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China); Yanmin Zhu (Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China); Haobing Liu (Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China); Tianzi Zang (Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China); Chunyang Wang (Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China); Kuan Liu (Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China);
294 Knowledge-Enhanced Multi-task Learning for Course Recommendation
[Abstract]
Qimin Ban (School of Computer Science and Technology, East China Normal University, Shanghai, China); Wen Wu (School of Computer Science and Technology, East China Normal University, Shanghai, China); Wenxin Hu (School of Data Science and Engineering, East China Normal University, Shanghai, China); Hui Lin (Shanghai Liulishuo Information Technology Co., Ltd., Shanghai, China); Wei Zheng (Information Technology Services, East China Normal University, Shanghai, China); Liang He (School of Computer Science and Technology, East China Normal University, Shanghai, China);
406 Joint Locality Preservation and Adaptive Combination for Graph Collaborative Filtering
[Abstract]
Zhiqiang Guo (School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China); Chaoyang Wang (School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China); Zhi Li (School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China); Jianjun Li (School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China); Guohui Li ( School of Software Engineering, Huazhong University of Science and Technology, Wuhan, China);
447 Hyperbolic Personalized Tag Recommendation
[Abstract]
Weibin Zhao (State Key Lab for Novel Software Technology, Nanjing University, Nanjing, China); Aoran Zhang (Tongda college, Nanjing University of Posts and Telecommunications, Yangzhou, China); Lin Shang (State Key Lab for Novel Software Technology, Nanjing University, Nanjing, China); Yonghong Yu (Tongda college, Nanjing University of Posts and Telecommunications, Yangzhou, China); Li Zhang (Department of Computer Science, Royal Holloway, University of London, Surrey, UK); Can Wang (School of Information and Communication Technology, Griffith University, Brisbane, Australia); Jiajun Chen (State Key Lab for Novel Software Technology, Nanjing University, Nanjing, China); Hongzhi Yin (School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia);
14 Collaborative Filtering for Recommendation in Geometric Algebra
[Abstract]
Longcan Wu (Northeastern University, Shenyang, China); Daling Wang (Northeastern University, Shenyang, China); Shi Feng (Northeastern University, Shenyang, China); Kaisong Song (Northeastern University, Shenyang, China); Yifei Zhang (Northeastern University, Shenyang, China); Ge Yu (Northeastern University, Shenyang, China);
397 SAER: Sentiment-opinion Alignment Explainable Recommendation
[Abstract]
Xiaoning Zong (School of Software, Shandong University, Jinan, China); Yong Liu (Alibaba-NTU Singapore Joint Research Institute, Nanyang Technological University, Singapore); Yonghui Xu (Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China); Yixin Zhang (School of Software, Shandong University, Jinan, China); Zhiqi Shen (School of Computer Science and Engineering, Nanyang Technological University, Singapore); Yonghua Yang (Alibaba Group, Hangzhou, China); Lizhen Cui (School of Software, Shandong University, Jinan, China);
437 Toward Auto-learning Hyperparameters for Deep Learning based Recommender Systems
[Abstract]
Bo Sun (Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China); Di Wu (Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China); Mingsheng Shang (Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China); Yi He (Department of Computer Science, Old Dominion University, Norfolk, Virginia 23462, USA);
7 Research Session #7 (Spatio-temporal Data),

(April 13 (Wednesday), 2022, 10.15-12.30, Hall 1)
200 JS-STDGN: A Spatial-Temporal Dynamic Graph Network using JS-Graph for Traffic Prediction
[Abstract]
Pengfei Li (Department of Computer Science and Technology, Soochow University, China); Junhua Fang (Department of Computer Science and Technology, Soochow University, China); Pingfu Chao (Department of Computer Science and Technology, Soochow University, China); Pengpeng Zhao (Department of Computer Science and Technology, Soochow University, China); An Liu (Department of Computer Science and Technology, Soochow University, China); Lei Zhao (Department of Computer Science and Technology, Soochow University, China);
311 When Multitask Learning Make a Difference: Spatio-temporal Joint Prediction for Cellular Trajectories
[Abstract]
Yuan Xu (Soochow University, Suzhou, China); Jiajie Xu (Soochow University, Suzhou, China); Junhua Fang (Soochow University, Suzhou, China); An Liu (Soochow University, Suzhou, China); Lei Zhao (Soochow University, Suzhou, China);
7 Efficient Retrieval of Top-k Weighted Spatial Triangles
[Abstract]
Ryosuke Taniguchi (Osaka University, Osaka, Japan); Daichi Amagata (Osaka University, Osaka, Japan); Takahiro Hara (Osaka University, Osaka, Japan);
47 DIOT: Detecting Implicit Obstacles from Trajectories
[Abstract]
Yifan Lei (School of Computing, National University of Singapore, Singapore); Qiang Huang (School of Computing, National University of Singapore, Singapore); Mohan Kankanhalli (School of Computing, National University of Singapore, Singapore); Anthony Tung (School of Computing, National University of Singapore, Singapore);
267 Exploring Sub-skeleton Trajectories for Interpretable Recognition of Sign Language
[Abstract]
Joachim Gudmundsson (University of Sydney, Australia); Martin P. Seybold (University of Sydney, Australia); John Pfeifer (University of Sydney, Australia);
494 Significant Engagement Community Search on Temporal Networks
[Abstract]
Yifei Zhang (National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China); Longlong Lin (National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China); Pingpeng Yuan (National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China); Hai Jin (National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China);
510 Influence Computation for Indoor Spatial Objects
[Abstract]
Yue Li (East China Normal University, Shanghai, China 51194501052@stu.ecnu.edu.cn); Guojie Ma (East China Normal University, Shanghai, China gjma@fem.ecnu.edu.cn); Shiyu Yang (Guangzhou University, Guangzhou, China syyang@gzhu.edu.cn; Liping Wang (East China Normal University, Shanghai, China lipingwang@sei.ecnu.edu.cn); Jiujing Zhang (Guangzhou University, Guangzhou, China jiujingzhang@e.gzhu.edu.cn);
516 NavFPNet: A Unified Framework for Network Localization in GPS-free Navigation Scenarios
[Abstract]
Jiazhi Ni (Localization Technology Department, Tencent Inc, Beijing, China); Xin Zhang (Localization Technology Department, Tencent Inc, Beijing, China); Beihong Jin (State Key Laboratory of Computer Sciences, Institute of Software, Chinese Academy of Sciences, Beijing, China); Fusang Zhang (State Key Laboratory of Computer Sciences, Institute of Software, Chinese Academy of Sciences, Beijing, China); Xin Li (Localization Technology Department, Tencent Inc, Beijing, China); Qiang Huang (Localization Technology Department, Tencent Inc, Beijing, China); Pengsen Wang (Localization Technology Department, Tencent Inc, Beijing, China); Xiang Li (Localization Technology Department, Tencent Inc, Beijing, China); Ning Xiao (Localization Technology Department, Tencent Inc, Beijing, China); Youchen Wang (Localization Technology Department, Tencent Inc, Beijing, China); Chang Liu (Localization Technology Department, Tencent Inc, Beijing, China);
8 Research Session #8 (Recommendation-II),

(April 13 (Wednesday), 2022, 10.15-12.30, Hall 2)
64 M^3-IB: A Memory-augment Multi-modal Information Bottleneck Model for Next-item Recommendation
[Abstract]
Yingpeng Du (School of Software and Microelectronics, Peking University, Beijing, PR China); Hongzhi Liu (School of Software and Microelectronics, Peking University, Beijing, PR China); Zhonghai Wu (National Engineering Research Center of Software Engineering, and Key Lab of High Confidence Software Technologies (MOE), Peking University, Beijing, PR China)
75 Fully Utilizing Neighbors for Session-based Recommendation with Graph Neural Networks
[Abstract]
Xingyu Zhang (School of Computer Science and Shanghai Key Laboratory of Data Science, Fudan University, Shanghai, China); Chaofeng Sha (School of Computer Science and Shanghai Key Laboratory of Data Science, Fudan University, Shanghai, China);
258 Enhancing Graph Convolution Network for Novel Recommendation
[Abstract]
Xuan Ma (School of Computer Science, Wuhan University, Hubei, China); Tieyun Qian (School of Computer Science, Wuhan University, Hubei, China); Yile Liang (School of Computer Science, Wuhan University, Hubei, China); Ke Sun (School of Computer Science, Wuhan University, Hubei, China); Hang Yun (School of Computer Science, Wuhan University, Hubei, China); Mi Zhang (School of Computer Science, Wuhan University, Hubei, China)
301 PMAR: Multi-Aspect Recommendation Based on Psychological Gap
[Abstract]
Liye Shi (School of Computer Science and Technology, East China Normal University, Shanghai, China); Wen Wu (School of Computer Science and Technology, Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China); Yu Ji (School of Computer Science and Technology, East China Normal University, Shanghai, China); Luping Feng (School of Data Science and Engineering, East China Normal University, Shanghai, China); Liang He (School of Computer Science and Technology, East China Normal University, Shanghai, China);
133 Graph Neural Networks with Dynamic and Static Representations for Social Recommendation
[Abstract]
Junfa Lin (School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China); Siyuan Chen (School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China); Jiahai Wang (School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China);
228 GISDCN: A Graph-based Interpolation Sequential Recommender with Deformable Convolutional Network
[Abstract]
Yalei Zang (Nanjing University of Aeronautics and Astronautics, Nanjing, China); Yi Liu (Nanjing University of Aeronautics and Astronautics, Nanjing, China); Weitong Chen (The University of Queensland, Brisbane, Australia); Bohan Li (Nanjing University of Aeronautics and Astronautics, Nanjing, China); Aoran Li (Nanjing University of Aeronautics and Astronautics, Nanjing, China); Lin Yue (The University of Queensland, Brisbane, Australia); Weihua Ma (Nanjing University of Aeronautics and Astronautics, Nanjing, China);
359 Core Interests Focused Self-Attention for Sequential Recommendation
[Abstract]
Zhengyang Ai (Institute of Information Engineering and School of Cyber Security, Chinese Academy of Sciences, Beijing, China); Shupeng Wang (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China); Siyu Jia (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China); Shu Guo (National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing, China);
9 Research Session #9 (Applications of ML-II),

(April 13 (Wednesday), 2022, 10.15-12.30, Hall 3)
51 CaSS: A Channel-aware Self-supervised Representation Learning Framework for Multivariate Time Series Classification
[Abstract]
Yijiang Chen (School of Computer Science, Fudan University, Shanghai, China); Xiangdong Zhou (School of Computer Science, Fudan University, Shanghai, China); Zhen Xing (School of Computer Science, Fudan University, Shanghai, China); Zhidan Liu (School of Computer Science, Fudan University, Shanghai, China); Minyang Xu (School of Computer Science, Fudan University, Shanghai, China and Arcplus Group PLC, Shanghai, China);
312 Transportation-mode Aware Travel Time Estimation via Meta-learning
[Abstract]
Yu Fan (School of Computer Science and Technology, Soochow University, Suzhou, China); Jiajie Xu (School of Computer Science and Technology, Soochow University, Suzhou, China); Rui Zhou (Swinburne University of Technology, Melbourne, Australia); Chengfei Liu (Swinburne University of Technology, Melbourne, Australia);
380 Efficient Consensus Motif Discovery of All Lengths in Multiple Time Series
[Abstract]
Mingming Zhang (School of Software, Fudan University, Shanghai, China); Peng Wang (School of Computer Science, Fudan University, Shanghai, China); Wei Wang (School of Computer Science, Fudan University, Shanghai, China);
459 LiteWSC: a Lightweight Framework for Web-Scale Spectral Clustering
[Abstract]
Geping Yang (Guangdong University of Technology, Faculty of Computer, China); Sucheng Deng (University of Macau, State Key Laboratory of Internet of Things for Smart City and Department of Computer and Information Science, Macau, China); Yiyang Yang (Guangdong University of Technology, Faculty of Computer, China); Zhiguo Gong (University of Macau, State Key Laboratory of Internet of Things for Smart City and Department of Computer and Information Science, Macau, China); Xiang Chen (Sun Yat-Sen University, School of Electronics and Information Technology, China); Zhifeng Hao (Shantou University, College of Engineering, China)
40 MetisRL: A Reinforcement Learning Approach for Dynamic Routing in Data Center Networks
[Abstract]
Yuanning Gao (MoE Key Lab of Artificial Intelligence, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China); Xiaofeng Gao (MoE Key Lab of Artificial Intelligence, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China); Guihai Chen (MoE Key Lab of Artificial Intelligence, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China)
128 Learning Evolving Concepts with Online Class Posterior Probability
[Abstract]
Junming Shao (Yangtze Delta Region Institute (Huzhou) and Data Mining Lab, University of Electronic Science and Technology of China, China); Kai Wang (Yangtze Delta Region Institute (Huzhou) and Data Mining Lab, University of Electronic Science and Technology of China, China); Jianyun Lu (Data Mining Lab, University of Electronic Science and Technology of China, China and School of Artificial Intelligence and Big Data, Chongqing College of Electronic Engineering, China); Zhili Qin (Yangtze Delta Region Institute (Huzhou) and Data Mining Lab, University of Electronic Science and Technology of China, China); Qiming Wangyang (Yangtze Delta Region Institute (Huzhou) and Data Mining Lab, University of Electronic Science and Technology of China, China); Qinli Yang (Yangtze Delta Region Institute (Huzhou) and Data Mining Lab, University of Electronic Science and Technology of China, China);
290 A Trace Ratio Maximization Method for Parameter Free Multiple Kernel Clustering
[Abstract]
Yan Chen (School of Computer Science, Sichuan University, Chengdu, China); Lei Wang (School of Computer and Information Technology, Shanxi Univiersity, Taiyuan, China); Liang Du (School of Computer and Information Technology, Shanxi Univiersity, Taiyuan, China and Institute of Big Data Science and Industry, Shanxi Univiersity, Taiyuan, China); Lei Duan (School of Computer Science, Sichuan University, Chengdu, China);
10 Research Session #10 (Algorithms-I),

(April 13 (Wednesday), 2022, 14.30-16.15, Hall 1)
181 Improving Information Cascade Modeling by Social Topology and Dual Role User Dependency
[Abstract]
Baichuan Liu (School of Data Science, Fudan University, Shanghai 200433, China); Deqing Yang (School of Data Science, Fudan University, Shanghai 200433, China); Yuchen Shi (School of Data Science, Fudan University, Shanghai 200433, China); Yueyi Wang (School of Data Science, Fudan University, Shanghai 200433, China);
277 Mining Negative Sequential Rules from Negative Sequential Patterns
[Abstract]
Chuanhou Sun (Department of Computer Science and Technology, Qilu University of Technology(Shandong Academy of Sciences), Jinan, China); Xiaoqi Jiang (Department of Computer Science and Technology, Qilu University of Technology(Shandong Academy of Sciences), Jinan, China); Xiangjun Dong (Department of Computer Science and Technology, Qilu University of Technology(Shandong Academy of Sciences), Jinan, China); Tiantian Xu (Department of Computer Science and Technology, Qilu University of Technology(Shandong Academy of Sciences), Jinan, China); Long Zhao (Department of Computer Science and Technology, Qilu University of Technology(Shandong Academy of Sciences), Jinan, China); Zhao Li (Department of Computer Science and Technology, Qilu University of Technology(Shandong Academy of Sciences), Jinan, China); Yuhai Zhao (Northeastern University, Shenyang, China);
287 CrossIndex: Memory-Friendly And Session-Aware Index for Supporting Crossfilter in Interactive Data Exploration
[Abstract]
Tianyu Xia (School of Computer Science, Fudan University and Shanghai Key Laboratory of Data Science, Shanghai, China); Hanbing Zhang (School of Computer Science, Fudan University and Shanghai Key Laboratory of Data Science, Shanghai, China); Yinan Jing (School of Computer Science, Fudan University and Shanghai Key Laboratory of Data Science, Shanghai, China); Zhenying He (School of Computer Science, Fudan University and Shanghai Key Laboratory of Data Science, Shanghai, China); Kai Zhang (School of Computer Science, Fudan University and Shanghai Key Laboratory of Data Science, Shanghai, China); X. Sean Wang (School of Computer Science, Fudan University and Shanghai Key Laboratory of Data Science and Shanghai Institute of Intelligent Electronics and Systems, Shanghai, China);
489 Hierarchical Bitmap Indexing for Range Queries on Multidimensional Arrays
[Abstract]
Luboš Krčál (Dept. of Computer Science, Czech Technical University in Prague, Czech Republic); Shen-Shyang Ho (Dept. of Computer Science, Rowan University, Glassboro, NJ, USA); Jan Holub (Dept. of Computer Science, Czech Technical University in Prague, Czech Republic);
261 A Novel Null-invariant Temporal Measure to Discover Partial Periodic Patterns in Non-uniform Temporal Databases
[Abstract]
R. Uday Kiran (The University of Aizu, Fukushima, Japan); Vipul Chhabra (IIIT-Hyderabad, Hyderabad, Telangana, India); Saideep Chennupati (IIIT-Hyderabad, Hyderabad, Telangana, India); P. Krishna Reddy (IIIT-Hyderabad, Hyderabad, Telangana, India); Minh-Son Dao (NICT, Tokyo, Japan); Koji Zettsu (NICT, Tokyo, Japan);
418 A Two-phase Approach for Recognizing Tables with Complex Structures
[Abstract]
Huichao Li (Beijing Institute of Technology, Beijing, China); Lingze Zeng (Beijing Institute of Technology, Beijing, China); Weiyu Zhang (Beijing Institute of Technology, Beijing, China); Jianing Zhang (Beijing Institute of Technology, Beijing, China); Ju Fan (Renmin University of China, Beijing, China); Meihui Zhang (Beijing Institute of Technology, Beijing, China);
467 A Dynamic Heterogeneous Graph Perception Network with Time-Based Mini-Batch for Information Diffusion Prediction
[Abstract]
Wei Fan (Heilongjiang University, Harbin, China); Meng Liu (Heilongjiang University, Harbin, China); Yong Liu (Heilongjiang University, Harbin, China);
11 Research Session #11 (Systems),

(April 13 (Wednesday), 2022, 14.30-16.15, Hall 2)
310 HEM: A Hardware-aware Event Matching Algorithm for Content-based Pub/Sub Systems
[Abstract]
Wanghua Shi (Shanghai Jiao Tong University, Shanghai, China); Shiyou Qian (Shanghai Jiao Tong University, Shanghai, China);
396 RotorcRaft: Scalable Follower-Driven Raft on RDMA
[Abstract]
Xuecheng Qi (East China Normal University, Shanghai, China); Huiqi Hu (East China Normal University, Shanghai, China); Xing Wei (East China Normal University, Shanghai, China); Aoying Zhou (East China Normal University, Shanghai, China);
545 Efficient Matrix Computation for SGD-based Algorithms on Apache Spark
[Abstract]
Baokun Han (East China Normal University, Shanghai, China); Zihao Chen (East China Normal University, Shanghai, China); Chen Xu (East China Normal University, Shanghai, China); Aoying Zhou (East China Normal University, Shanghai, China);
204 Parallel Pivoted Subgraph Filtering with Partial Coding Trees on GPU
[Abstract]
Yang Wang (Northeastern University, China); Yu Gu (Northeastern University, China); Chuanwen Li (Northeastern University, China);
317 Txchain: Scaling Sharded Decentralized Ledger via Chained Transaction Sequences
[Abstract]
Zheng Xu (Fudan University, Shanghai, China); Rui Jiang (Fudan University, Shanghai, China); Peng Zhang (Fudan University, Shanghai, China); Tun Lu (Fudan University, Shanghai, China); Ning Gu (Fudan University, Shanghai, China);
471 Zebra: An Efficient, RDMA-Enabled Distributed Persistent Memory File System
[Abstract]
Jingyu Wang (Shanghai Jiao Tong University, Shanghai, China); Shengan Zheng (Shanghai Jiao Tong University, Shanghai, China); Ziyi Lin (Alibaba Group, Hangzhou, China); Yuting Chen (Shanghai Jiao Tong University, Shanghai, China); Linpeng Huang (Shanghai Jiao Tong University, Shanghai, China);
12 Research Session #12 (Applications of ML-III),

(April 13 (Wednesday), 2022, 14.30-16.15, Hall 3)
6 Hierarchical Attention Factorization Machine for CTR Prediction
[Abstract]
Lianjie Long (Chongqing University, Chongqing 400044, China); Yunfei Yin (Chongqing University, Chongqing 400044, China); Faliang Huang (Chongqing University, Chongqing 400044, China);
35 MCRF: Enhancing CTR Prediction Models via Multi-Channel Feature Refinement Framework
[Abstract]
Fangye Wang (Fudan University, Shanghai, China); Hansu Gu (Seattle, United States); Dongsheng Li (Microsoft Research Asia, Shanghai, China); Tun Lu (Fudan University, Shanghai, China); Peng Zhang (Fudan University, Shanghai, China); Ning Gu (Fudan University, Shanghai, China);
185 Semi-Supervised Graph Learning with Few Labeled Nodes
[Abstract]
Cong Zhang (Beijing University of Posts and Telecommunications, China); Ting Bai (Beijing University of Posts and Telecommunications, China); Bin Wu (Beijing University of Posts and Telecommunications, China);
328 A Deep Reinforcement Learning Based Dynamic Pricing Algorithm in Ride-hailing
[Abstract]
Bing Shi (Wuhan University of Technology, China); Zhi Cao (Wuhan University of Technology, China); Yikai Luo (Wuhan University of Technology, China);
87 InDISP: An Interpretable Model for Dynamic Illness Severity Prediction
[Abstract]
Xinyu Ma (Southeast University, China); Meng Wang (Southeast University, China); Xing Liu (Central South University, China); Yifan Yang (Tencent, China); Yefeng Zheng (Tencent Jarvis Lab, China); Sen Wang (University of Queensland, Australia);
249 Generative Adversarial Imitation Learning to Search in Branch-and-Bound Algorithms
[Abstract]
Qi Wang (Fudan University, China); Suzanne V. Blackley (Harvard Medical School, Boston, USA); Chunlei Tang (Harvard Medical School, Boston, USA);
373 Market-aware Dynamic Person-Job Fit with Hierarchical Reinforcement Learning
[Abstract]
Bin Fu (Peking University, Beijing, China); Hongzhi Liu (Peking University, Beijing, China); Hui Zhao (Peking University, Beijing, China); Yao Zhu (Peking University, Beijing, China); Yang Song (BOSS Zhipin, China); Tao Zhang (BOSS Zhipin, China); Zhonghai Wu (Peking University, Beijing, China);
13 Research Session #13 (Security),

(April 13 (Wednesday), 2022, 16.30-18.00, Hall 1)
21 ADAPT: Adversarial Domain Adaptation with Purifier Training for Cross-domain Credit Risk Forecasting
[Abstract]
Guanxiong Zeng (Alibaba Group, China.); Jianfeng Chi (Alibaba Group, China.); Rui Ma (Alibaba Group, China.); Jinghua Feng (Alibaba Group, China.); Xiang Ao (University of Chinese Academy of Sciences, Beijing, China.); Hao Yang (Alibaba Group, China.);
345 Poisoning Attacks on Fair Machine Learning
[Abstract]
Minh-Hao Van (University of Arkansas, Fayetteville, USA); Wei Du (University of Arkansas, Fayetteville, USA); Xintao Wu (University of Arkansas, Fayetteville, USA); Aidong Lu (University of North Carolina at Charlotte, USA);
284 Bi-Level Selection via Meta Gradient for Graph-based Fraud Detection
[Abstract]
Linfeng Dong (Chinese Academy of Sciences (CAS), Beijing, China); Yang Liu (Chinese Academy of Sciences (CAS), Beijing, China); Xiang Ao (Chinese Academy of Sciences (CAS), Beijing, China); Jianfeng Chi (Alibaba Group, Hangzhou, China); Jinghua Feng (Alibaba Group, Hangzhou, China); Hao Yang (Alibaba Group, Hangzhou, China); Qing He (Chinese Academy of Sciences (CAS), Beijing, China);
344 Contrastive Learning for Insider Threat Detection
[Abstract]
Vinay M.S. (University of Arkansas, Fayetteville, USA); Shuhan Yuan (University of Arkansas, Fayetteville, USA); Xintao Wu (Utah State University, Logan, USA);
355 Metadata Privacy Preservation for Blockchain-based Healthcare Systems
[Abstract]
Lixin Liu (Renmin University of China, Beijing, China); Xinyu Li (The University of Hong Kong, Hong Kong, China); Man Ho AU (The University of Hong Kong, Hong Kong, China); Zhuoya Fan (Renmin University of China, Beijing, China); Xiaofeng Meng (Renmin University of China, Beijing, China);
384 Blockchain-Based Encrypted Image Storage and Search in Cloud Computing
[Abstract]
Yingying Li (Xidian University, Xi’an, China); Jianfeng Ma (Xidian University, Xi’an, China); Yinbin Miao (Xidian University, Xi’an, China); Ximeng Liu (Fuzhou University, Fuzhou, China); Qi Jiang (Xidian University, Xi’an, China);
14 Research Session #14 (Text and Image-III),

(April 13 (Wednesday), 2022, 16.30-18.00, Hall 2)
223 Semantic-based Data Augmentation for Math Word Problems
[Abstract]
Ailisi Li (Fudan University, Shanghai, China); Yanghua Xiao (Fudan University, Shanghai, China); Jiaqing Liang (Fudan University, Shanghai, China); Yunwen Chen (DataGrand Inc., Shanghai, China);
455 Aligning Internal Regularity and External Influence of Multi-Granularity for Temporal Knowledge Graph Embedding
[Abstract]
Tingyi Zhang (Soochow University, Suzhou, China); Zhixu Li (Fudan University, China); Jiaan Wang (Soochow University, Suzhou, China); Jianfeng Qu (Soochow University, Suzhou, China); Lin Yuan (Soochow University, Suzhou, China); An Liu (Soochow University, Suzhou, China); Lei Zhao (Soochow University, Suzhou, China); Zhigang Chen (iFLYTEK Research, Suzhou, China);
518 FalCon: A Faithful Contrastive Framework for Response Generation in TableQA Systems
[Abstract]
Shineng Fang (Fudan University, Shanghai, China); Jiangjie Chen (Fudan University, Shanghai, China); Xinyao Shen (Fudan University, Shanghai, China); Yunwen Chen (DataGrand Inc., Shanghai, China); Yanghua Xiao (Fudan University, Shanghai, China);
80 Tipster: A Topic-Guided Language Model for Topic-Aware Text Segmentation
[Abstract]
Zheng Gong (University of Science and Technology of China, Hefei, China); Shiwei Tong (University of Science and Technology of China, Hefei, China); Han Wu (University of Science and Technology of China, Hefei, China); Qi Liu (University of Science and Technology of China, Hefei, China); Hanqing Tao (University of Science and Technology of China, Hefei, China); Wei Huang (University of Science and Technology of China, Hefei, China); Runlong Yu (University of Science and Technology of China, Hefei, China);
155 Predicting Rumor Veracity on Social Media with Graph Structured Multi-task Learning
[Abstract]
Yudong Liu (Beijing University of Posts and Telecommunications,Beijing, China); Xiaoyu Yang (Beijing University of Posts and Telecommunications,Beijing, China); Xi Zhang (Beijing University of Posts and Telecommunications,Beijing, China); Zhihao Tang (Beijing University of Posts and Telecommunications,Beijing, China); Zongyi Chen (Beijing University of Posts and Telecommunications,Beijing, China); Liwen Zhang (Beijing University of Posts and Telecommunications,Beijing, China);
188 KAAS: A Keyword-Aware Attention Abstractive Summarization Model for Scientific Articles
[Abstract]
Shuaimin Li (University of Chinese Academy of Sciences, Beijing, China); Jungang Xu (University of Chinese Academy of Sciences, Beijing, China);
198 PERM: Pre-training Question Embeddings via Relation Map for Improving Knowledge Tracing
[Abstract]
Wentao Wang (Northwest Normal University, China); Huifang Ma (Northwest Normal University, China); Yan Zhao (Northwest Normal University, China); Fanyi Yang (Northwest Normal University, China); Liang Chang (Guilin University of Electronic Technology, China);
15 Research Session #15 (Graphs-II),

(April 13 (Wednesday), 2022, 16.30-18.00, Hall 3)
98 On Glocal Explainability of Graph Neural Networks
[Abstract]
Ge Lv (The Hong Kong University of Science and Technology, Hong Kong, China); Lei Chen (The Hong Kong University of Science and Technology, Hong Kong, China); Caleb Chen Cao (Huawei Technologies, Hong Kong, China);
337 Temporal Network Embedding with Motif Structural Features
[Abstract]
Zhi Qiao (Beijing Key Lab of Network Technology, School of Computer Science and Engineering, Beihang University, Beijing, China); Wei Li (Beijing Key Lab of Network Technology, School of Computer Science and Engineering, Beihang University, Beijing, China); Yunchun Li (Beijing Key Lab of Network Technology, School of Computer Science and Engineering, Beihang University, Beijing, China);
392 What affects the performance of models? Sensitivity Analysis of Knowledge graph embedding
[Abstract]
Han Yang (Peking University, Beijing, China); Leilei Zhang (Peking University, Beijing, China); Fenglong Su(National University of Defense Technology, Hunan, China); Jinhui Pang (Beijing Institute of Technology, Beijing, China);
123 CollaborateCas: Popularity Prediction of Information Cascades based on Collaborative Graph Attention Networks
[Abstract]
Xianren Zhang (CQU-UC Joint Co-op Institute, Chongqing University, Chongqing, China and Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing University, Chongqing, China); Jiaxing Shang (College of Computer Science, Chongqing University, Chongqing, China and Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing University, Chongqing, China); Xueqi Jia (College of Computer Science, Chongqing University, Chongqing, China and Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing University, Chongqing, China); Dajiang Liu (College of Computer Science, Chongqing University, Chongqing, China and Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing University, Chongqing, China); Fei Hao (School of Computer Science, Shaanxi Normal University, Xian, China); Zhiqing Zhang (CQU-UC Joint Co-op Institute, Chongqing University, Chongqing, China);
356 IncreGNN: Incremental Graph Neural Network Learning by Considering Node and Parameter Importance
[Abstract]
Di Wei (School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China); Yu Gu (School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China); Yumeng Song (School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China); Zhen Song (School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China); Fangfang Li (School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China); and Ge Yu (School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China);
427 Representation Learning in Heterogeneous Information Networks Based on Hyper Adjacency Matrix
[Abstract]
Bin Yang (School of Software, Fudan University, Shanghai, China); Yitong Wang (School of Software, Fudan University, Shanghai, China);
16 Research Session #16 (Algorithms-II),

(April 14 (Thursday), 2022, 10.15-12.30, Hall 1)
194 Discovering Bursting Patterns over Streaming Graphs
[Abstract]
Qianzhen Zhang (Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha, China) , Deke Guo (Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha, China) , and Xiang Zhao (Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha, China)
327 GHStore: A High Performance Global Hash based key-value store
[Abstract]
Jiaoyang Li (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China and School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China) , Yinliang Yue (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China and School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China) , and Weiping Wang (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China and School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China)
533 Membership Algorithm for Single-Occurrence Regular Expressions with Shuffle and Counting
[Abstract]
Xiaofan Wang (State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China)
10 (p, n)-core: core decomposition in signed networks
[Abstract]
Junghoon Kim (Nanyang Technological University, 639798, Singapore) and Sungsu Lim (Chungnam National University, 34134, South Korea)
120 TROP: Task Ranking Optimization Problem on Crowdsourcing Service Platform
[Abstract]
Jiale Zhang (MoE Key Lab of Artificial Intelligence, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China), Haozhen Lu (MoE Key Lab of Artificial Intelligence, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China) , Xiaofeng Gao (MoE Key Lab of Artificial Intelligence, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China), Ailun Song (Tencent Inc., Shenzhen, China), and Guihai Chen (Tencent Inc., Shenzhen, China)
240 HATree: A Hotness-Aware Tree Index with In-Node Hotspot Cache for NVM/DRAM-Based Hybrid Memory Architecture
[Abstract]
Gaocong Liu (University of Science and Technology of China, Hefei, China) , Yongping Luo (University of Science and Technology of China, Hefei, China) , Peiquan Jin (University of Science and Technology of China, Hefei, China and Key Laboratory of Electromagnetic Space Information, CAS, Hefei, China)
349 Utilizing Expert Knowledge and Contextual Information for Sample-limited Causal Graph Construction
[Abstract]
Xuwu Wang (Shanghai Key Lab. of Data Science, School of Computer Science, Fudan University) , Xueyao Jiang (Shanghai Key Lab. of Data Science, School of Computer Science, Fudan University) , Sihang Jiang (Shanghai Key Lab. of Data Science, School of Computer Science, Fudan University), Zhixu Li (Shanghai Key Lab. of Data Science, School of Computer Science, Fudan University), and Yanghua Xiao (Shanghai Key Lab. of Data Science, School of Computer Science, Fudan University and Fudan-Aishu Cognitive Intelligence Joint Research Center, Shanghai, China)
463 Towards Unification of Statistical Reasoning, OLAP and Association Rule Mining: Semantics and Pragmatics
[Abstract]
Rahul Sharma (Information Systems Group, Tallinn University of Technology, Tallinn, Estonia) , Minakshi Kaushik (Information Systems Group, Tallinn University of Technology, Tallinn, Estonia) , Sijo Arakkal Peious (Information Systems Group, Tallinn University of Technology, Tallinn, Estonia) , Mahtab Shahin (Information Systems Group, Tallinn University of Technology, Tallinn, Estonia), Amrendra Singh Yadav (Vellore Institute of Technology - VIT Bhopal, India), and Dirk Draheim (Information Systems Group, Tallinn University of Technology, Tallinn, Estonia)
17 Research Session #17 (Recommendation-III),

(April 14 (Thursday), 2022, 10.15-12.30, Hall 2)
300 Learning Social Influence from Network Structure for Recommender Systems
[Abstract]
Ting Bai (Beijing University of Posts and Telecommunications and Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education), Yanlong Huang (Beijing University of Posts), and Bin Wu (Beijing University of Posts and Telecommunications and Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education)
398 Meta-path Enhanced Lightweight Graph Neural Network for Social Recommendation
[Abstract]
Hang Miao (Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China and College of Computer Science and Technology, Jilin University, Changchun,China), Anchen Li (Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China and College of Computer Science and Technology, Jilin University, Changchun,China) and Bo Yang (Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China and College of Computer Science and Technology, Jilin University, Changchun,China)
402 Multi-view Multi-behavior Contrastive Learning in Recommendation
[Abstract]
Yiqing Wu (Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190 and China University of Chinese Academy of Sciences, Beijing 100049, China and WeChat Search Application Department, Tencent, China) , Ruobing Xie (WeChat Search Application Department, Tencent, China), Yongchun Zhu (Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) and Institute of Computing Technology, CAS, Beijing 100190) , Xiang Ao(Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) and Institute of Computing Technology, CAS, Beijing 100190) , Xin Chen(WeChat Search Application Department, Tencent, China) , Xu Zhang (WeChat Search Application Department, Tencent, China) , Fuzhen Zhuang (Institute of Artificial Intelligence, Beihang University, Beijing 100191, China and Xiamen Institute of Data Intelligence, Xiamen, China) , Leyu Lin (WeChat Search Application Department, Tencent, China) , and Qing He (Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) and Institute of Computing Technology, CAS, Beijing 100190)
452 Diffusion-based Graph Contrastive Learning for Recommendation with Implicit Feedback
[Abstract]
Lingzi Zhang, Yong Liu, Xin Zhou, Chunyan Miao, Guoxin Wang, and Haihong Tang
354 Deep Graph Mutual Learning for Cross-domain Recommendation
[Abstract]
Yifan Wang (School of Computer Science, Peking University, Beijing, China) , Yongkang Li (School of Computer Science, Peking University, Beijing, China), Shuai Li (School of Computer Science, Peking University, Beijing, China), Weiping Song (School of Computer Science, Peking University, Beijing, China) , Jiangke Fan (Meituan) , Shan Gao (Meituan), Ling Ma (Meituan), Bing Cheng (Meituan), Xunliang Cai (Meituan), Sheng Wang (Paul G. Allen School of Computer Science, University of Washington), Ming Zhang (School of Computer Science, Peking University, Beijing, China)
453 GELibRec: Third-Party Libraries Recommendation Using Graph Neural Network
[Abstract]
Chengming Zou (Hubei Key Laboratory of Transportation Internet of Things, Wuhan University of Technology, Wuhan, China and Peng Cheng National Laboratory, Shenzhen, China),Zhenfeng Fan (School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China)
18 Research Session #18 (Applications of ML-IV),

(April 14 (Thursday), 2022, 10.15-12.30, Hall 3)
135 Temporal Knowledge Graph Entity Alignment via Representation Learning
[Abstract]
Xiuting Song (School of Computer and Communication Engineering, Northeastern University (Qinhuangdao), Qinhuangdao 066004, China), Luyi Bai (School of Computer and Communication Engineering, Northeastern University (Qinhuangdao), Qinhuangdao 066004, China), Rongke Liu (School of Computer and Communication Engineering, Northeastern University (Qinhuangdao), Qinhuangdao 066004, China), Han Zhang (School of Computer and Communication Engineering, Northeastern University (Qinhuangdao), Qinhuangdao 066004, China)
208 Human Mobility Identification by Deep Behavior Relevant Location Representation
[Abstract]
Tao Sun (Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China and School of Computing Science and Technology, University of Chinese Academy of Sciences, Beijing, China), Fei Wang (Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China) , Zhao Zhang (Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China), Lin Wu (Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China), and Yongjun Xu (Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China)
469 Dual Confidence Learning Network for Open-World Time Series Classification
[Abstract]
Junwei Lv (Key Laboratory of Knowledge Engineering with Big Data, Ministry of Education, Hefei University of Technology, Hefei 230009, China and School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China), Ying He (Key Laboratory of Knowledge Engineering with Big Data, Ministry of Education, Hefei University of Technology, Hefei 230009, China and School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China), Xuegang Hu (Key Laboratory of Knowledge Engineering with Big Data, Ministry of Education, Hefei University of Technology, Hefei 230009, China and School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China), Desheng Cai (School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China), Yuqi Chu (School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China) and Jun Hu (National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China)
530 Port Container Throughput Prediction Based on Variational AutoEncoder
[Abstract]
Jingze Li (School of Computer Science, Fudan University, Shanghai, China, Shanghai Key Laboratory of Data Science, Fudan University, Shanghai, China and Shanghai Institute of Intelligent Electronics & Systems, Fudan University, Shanghai, China) , Shengmin Shi ( School of Computer Science, Fudan University, Shanghai, China, Shanghai Key Laboratory of Data Science, Fudan University, Shanghai, China and Shanghai Institute of Intelligent Electronics & Systems, Fudan University, Shanghai, China) , Tongbing Chen (School of Computer Science, Fudan University, Shanghai, China, Shanghai Key Laboratory of Data Science, Fudan University, Shanghai, China and Shanghai Institute of Intelligent Electronics & Systems, Fudan University, Shanghai, China), Yu Tian (Shanghai International Port (Group) Co., Ltd., Shanghai, China) ,Yihua Ding (Shanghai Harbor e-logistics Software Co., Ltd., Shanghai, China), Yiyong Xiao (NeZha Smart Port&Shipping Technology (Shanghai) Co., Ltd., Shanghai, China), and Weiwei Sun (School of Computer Science, Fudan University, Shanghai, China, Shanghai Key Laboratory of Data Science, Fudan University, Shanghai, China and Shanghai Institute of Intelligent Electronics & Systems, Fudan University, Shanghai, China)
150 Robust Dynamic Pricing in Online Markets with Reinforcement Learning
[Abstract]
Bolei Zhang (School of Computer, Nanjing University of Posts and Telecommunications Nanjing, PR China and State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, PR China) and Fu Xiao (School of Computer, Nanjing University of Posts and Telecommunications Nanjing, PR China)
246 Multi-Memory enhanced Separation Network for Indoor Temperature Prediction
[Abstract]
Zhewen Duan (Xidian University, Xi’an, JD Intelligent Cities Research and JD iCity, JD Technology, Beijing, China) , Xiuwen Yi (Xidian University, Xi’an and JD Intelligent Cities Research) , Peng Li , Dekang Qi (Xidian University, Xi’an and JD Intelligent Cities Research and Southwest Jiaotong University, Chengdu), Yexin Li (Xidian University, Xi’an and JD Intelligent Cities Research), Haoran Xu (Xidian University, Xi’an and JD Intelligent Cities Research), Yanyong Huang (Southwestern University of Finance and Economics, Chengdu), Junbo Zhang (Xidian University, Xi’an and JD Intelligent Cities Research) , and Yu Zheng (Xidian University, Xi’an, JD Intelligent Cities Research and JD iCity, JD Technology, Beijing, China)
422 TEALED: A Multi-Step Workload Forecasting Approach Using Time-Sensitive EMD and Auto LSTM Encoder-Decoder
[Abstract]
Xiuqi Huang (MoE Key Lab of Artificial Intelligence, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China), Yunlong Cheng (MoE Key Lab of Artificial Intelligence, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China), Xiaofeng Gao (MoE Key Lab of Artificial Intelligence, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China), and Guihai Chen (MoE Key Lab of Artificial Intelligence, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China)
19 Research Session #19 (Knowledge Bases),

(April 14 (Thursday), 2022, 14.30-16.15, Hall 1)
44 Triple-as-Node Knowledge Graph and Its Embeddings
[Abstract]
Xin Lv (Department of Computer Science and Technology, BNRist and KIRC, Institute for Artificial Intelligence, Tsinghua University, Beijing 100084, China), Jiaxin Shi (Department of Computer Science and Technology, BNRist and KIRC, Institute for Artificial Intelligence, Tsinghua University, Beijing 100084, China), Shulin Cao (Department of Computer Science and Technology, BNRist and KIRC, Institute for Artificial Intelligence, Tsinghua University, Beijing 100084, China), Lei Hou (Department of Computer Science and Technology, BNRist and KIRC, Institute for Artificial Intelligence, Tsinghua University, Beijing 100084, China), and Juanzi Li (Department of Computer Science and Technology, BNRist and KIRC, Institute for Artificial Intelligence, Tsinghua University, Beijing 100084, China)
170 LeKAN: Extracting Long-tail Relations via Layer-Enhanced Knowledge-Aggregation Networks
[Abstract]
Xiaokai Liu (National Engineering Research Center for Big Data Technology and System,Services Computing Technology and System Lab, Cluster and Grid Computing Lab and School of Cyber Science and Engineering, Huazhong University of Science and Technology, China), Feng Zhao (National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab and School of Computer Science and Technology, Huazhong University of Science and Technology, China), Xiangyu Gui (National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab and School of Computer Science and Technology, Huazhong University of Science and Technology, China), Hai Jin (National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab and School of Computer Science and Technology, Huazhong University of Science and Technology, China)
264 TRHyTE: Temporal Knowledge Graph Embedding based on Temporal-Relational Hyperplanes
[Abstract]
Lin Yuan (School of Computer Science and Technology, Soochow University, China), Zhixu Li (Shanghai Key Lab of Data Science, School of Computer Science, Fudan University), Jianfeng Qu (School of Computer Science and Technology, Soochow University, China), Tingyi Zhang (School of Computer Science and Technology, Soochow University, China),An Liu (School of Computer Science and Technology, Soochow University, China), Lei Zhao (School of Computer Science and Technology, Soochow University, China), and Zhigang Chen (State Key Laboratory of Cognitive Intelligence, iFLYTEK, China and iFLYTEK Research, Suzhou, China)
168 ExKGR: Explainable Multi-hop Reasoning for Evolving Knowledge Graph
[Abstract]
Cheng Yan (National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab andSchool of Computer Science and Technology, Huazhong University of Science and Technology, China), Feng Zhao (National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab andSchool of Computer Science and Technology, Huazhong University of Science and Technology, China), Hai Jin (National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab andSchool of Computer Science and Technology, Huazhong University of Science and Technology, China)
243 Improving Core Path Reasoning for the Weakly Supervised Knowledge Base Question Answering
[Abstract]
Nan Hu (School of Computer Science and Engineering, Southeast University, China), Sheng Bi (School of Computer Science and Engineering, Southeast University, China), Guilin Qi (School of Computer Science and Engineering, Southeast University, China), Meng Wang (School of Computer Science and Engineering, Southeast University, China), Yuncheng Hua (School of Computer Science and Engineering, Southeast University, China), and Shirong Shen (School of Computer Science and Engineering, Southeast University, China)
360 Counterfactual-guided and Curiosity-driven Multi-Hop Reasoning over Knowledge Graph
[Abstract]
Dan Shi (Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China and College of Computer Science and Technology, Jilin University, Changchun, China), Anchen Li (Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China and College of Computer Science and Technology, Jilin University, Changchun, China), and Bo Yang (Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China and College of Computer Science and Technology, Jilin University, Changchun, China)
388 Visualizable or Non-visualizable? Exploring the Visualizability of Concepts in Multi-modal Knowledge Graph
[Abstract]
Xueyao Jiang (Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China), Ailisi Li (Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China), Jiaqing Liang (Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China), Bang Liu (Mila & DIRO, Universit´e de Montr´eal, Montr´eal, Qu´ebec, Canada), Rui Xie (Meituan, Shanghai, China), Wei Wu (Meituan, Shanghai, China),Zhixu Li (Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China), and Yanghua Xiao (Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China and Fudan-Aishu Cognitive Intelligence Joint Research Center, Shanghai, China)
20 Research Session #20 (Text and Image-IV),

(April 14 (Thursday), 2022, 14.30-16.15, Hall 2)
90 Information Networks based Multi-semantic Data Embedding for Entity Resolution
[Abstract]
Chenchen Sun (School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, China,Engineering Research Center of Learning-Based Intelligent System (Ministry of Education),Tianjin University of Technology, Tianjin, China and College of Intelligence and Computing, Tianjin University, Tianjin, China), Derong Shen (School of Computer Science and Engineering, Northeastern University, Shenyang, China) and Tiezheng Nie (School of Computer Science and Engineering, Northeastern University, Shenyang, China)
269 Empowering Transformer with Hybrid Matching Knowledge for Entity Matching
[Abstract]
Wenzhou Dou (School of Computer Science and Engineering, Northeastern University,Shenyang, China), Derong Shen (School of Computer Science and Engineering, Northeastern University, Shenyang, China), Tiezheng Nie (School of Computer Science and Engineering, Northeastern University,Shenyang, China), Yue Kou (School of Computer Science and Engineering, Northeastern University,Shenyang, China), Chenchen Sun (School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, China),Hang Cui (University of Illinois at Urbana-Champaign, United States), and Ge Yu (School of Computer Science and Engineering, Northeastern University,Shenyang, China)
445 Fake Restaurant Review Detection Using Deep Neural Networks with Hybrid Feature Fusion Method
[Abstract]
Yifei Jian (School of Cyber Science and Engineering, Sichuan University, Chengdu, China), Xingshu Chen (School of Cyber Science and Engineering, Sichuan University, Chengdu, China), and Haizhou Wang (School of Cyber Science and Engineering, Sichuan University, Chengdu, China)
179 Medical image fusion based on pixel-level nonlocal self-similarity prior and optimization
[Abstract]
Rui Zhu (Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun, 130012, China and College of Computer Science and Technology, Jilin University, Changchun, 130012, China), Xiongfei Li (Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun, 130012, China and College of Computer Science and Technology, Jilin University, Changchun, 130012, China), Yu Wang (Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun, 130012, China and College of Computer Science and Technology, Jilin University, Changchun, 130012, China), and Xiaoli Zhang (Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun, 130012, China and College of Computer Science and Technology, Jilin University, Changchun, 130012, China)
187 Knowledge-enhanced Interactive Matching Network for Multi-turn Response Selection in Medical Dialogue Systems
[Abstract]
Ying Zhu (School of Computer Science and Engineering, Northeastern University, Shenyang, China), Shi Feng (School of Computer Science and Engineering, Northeastern University, Shenyang, China), Daling Wang (School of Computer Science and Engineering, Northeastern University, Shenyang, China), Yifei Zhang (School of Computer Science and Engineering, Northeastern University, Shenyang, China), Donghong Han (School of Computer Science and Engineering, Northeastern University, Shenyang, China)
268 A Three-Stage Curriculum Learning Framework with Hierarchical Label Smoothing for Fine-Grained Entity Typing
[Abstract]
Bo Xu (School of Computer Science and Technology, Donghua University, Shanghai, China), Zhengqi Zhang (School of Computer Science and Technology, Donghua University, Shanghai, China), Chaofeng Sha (School of Computer Science, Fudan University, Shanghai, China and Shanghai Key Laboratory of Intelligence Processing, Shanghai, China), Ming Du (School of Computer Science and Technology, Donghua University, Shanghai, China), Hui Song (School of Computer Science and Technology, Donghua University, Shanghai, China), and Hongya Wang (School of Computer Science and Technology, Donghua University, Shanghai, China)
395 TaskSum: Task-Driven Extractive Text Summarization for Long News Documents Based on Reinforcement Learning
[Abstract]
Moming Tang (East China Normal University), Dawei Cheng (Tongji University), Cen Chen (East China Normal University), Yuqi Liang (Seek Data Inc), Yifeng Luo (East China Normal University), and Weining Qian (East China Normal University)
21 Research Session #21 (Recommendation-IV),

(April 14 (Thursday), 2022, 14.30-16.15, Hall 3)
12 MDKE: Multi-level Disentangled Knowledge-based Embedding for Recommender Systems
[Abstract]
Haolin Zhou (MoE Key Lab of Artificial Intelligence,Department of Computer Science and Engineering,Shanghai Jiao Tong University, Shanghai, China), Qingmin Liu (MoE Key Lab of Artificial Intelligence,Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China), Xiaofeng Gao (MoE Key Lab of Artificial Intelligence,Department of Computer Science and Engineering,Shanghai Jiao Tong University, Shanghai, China), and Guihai Chen (MoE Key Lab of Artificial Intelligence, Department of Computer Science and Engineering,Shanghai Jiao Tong University, Shanghai, China)
401 Intention Adaptive Graph Neural Network for Category-aware Session-based Recommendation
[Abstract]
Chuan Cui (Tongji University, Shanghai, China), Qi Shen (Tongji University, Shanghai, China), Shixuan Zhu (Tongji University, Shanghai, China), Yitong Pang (Tongji University, Shanghai, China), Yiming Zhang (Tongji University, Shanghai, China),Hanning Gao (Tongji University, Shanghai, China), and Zhihua Wei (Tongji University, Shanghai, China)
430 Gated Hypergraph Neural Network for Scene-aware Recommendation
[Abstract]
Tianchi Yang (Beijing University of Posts and Telecommunications, China), Luhao Zhang (Meituan, China), Chuan Shi (Beijing University of Posts and Telecommunications, China), Cheng Yang (Beijing University of Posts and Telecommunications, China), Siyong Xu (Beijing University of Posts and Telecommunications, China), Ruiyu Fang (Meituan, China), Maodi Hu (Meituan, China), Huaijun Liu (Meituan, China), Tao Li (Meituan, China), Dong Wang (Meituan, China)
13 Multi-Behavior Recommendation with Two-Level Graph Attentional Networks
[Abstract]
Yunhe Wei (College of Computer Science and Engineering, Northwest Normal University, Lanzhou Gansu 730070, China), Huifang Ma (College of Computer Science and Engineering, Northwest Normal University, Lanzhou Gansu 730070, China, Guangxi Key Lab of Multi-source Information Mining and Security, and Guangxi Normal University, Guilin Guangxi, 541004, China Guangxi Key Lab of Trusted Software, Guilin University of Electronic Technology, Guilin Guangxi, 541004, China), Yike Wang (College of Computer Science and Engineering, Northwest Normal University, Lanzhou Gansu 730070, China), Zhixin Li (Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin Guangxi, 541004, China), and Liang Chang (Guangxi Key Lab of Trusted Software, Guilin University of Electronic Technology, Guilin Guangxi, 541004, China)
157 Toward Paper Recommendation by Jointly Exploiting Diversity and Dynamics in Heterogeneous Information Networks
[Abstract]
Jie Wang (School of Computer Science and Technology, Soochoow University, Suzhou, China), Jinya Zhou (School of Computer Science and Technology, Soochoow University, Suzhou, China), Zhen Wu (School of Computer Science and Technology, Soochoow University, Suzhou, China), and Xigang Sun (School of Computer Science and Technology, Soochoow University, Suzhou, China)
166 Enhancing Session-based Recommendation with Global Context Information and Knowledge Graph
[Abstract]
Xiaohui Zhang (College of Computer Science and Engineering, Northwest Normal University, Lanzhou Gansu 730070, China), Huifang Ma (College of Computer Science and Engineering, Northwest Normal University, Lanzhou Gansu 730070, China and Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin Guangxi 541004, China), Zihao Gao (College of Computer Science and Engineering, Northwest Normal University, Lanzhou Gansu 730070, China), Zhixin Li (Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin Guangxi 541004, China), and Liang Chang (Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin Guangxi 541004, China)

6. Industry Sessions

Industry Session Id Session Name Paper ID Paper Title
(Date, Time, and Hall)
1 Industry Session # 1
Advancing Recommendation Systems,
(April 12 (Tuesday), 2022, 10.15-12.30 , Hall 4)
553 A Joint Framework for Explainable Recommendation with Knowledge Reasoning and Graph Representation
[Abstract]
Luhao Zhang (Meituan, China); Ruiyu Fang (Beijing University of Posts and Telecommunications, China); Tianchi Yang (Meituan, China); Maodi Hu (Meituan, China); Tao Li (Meituan, China); Chuan Shi (Beijing University of Posts and Telecommunications, China); Dong Wang (Meituan, China);
568 XDM: Improving Sequential Deep Matching with Unclicked User Behaviors for Recommender System
[Abstract]
Fuyu Lv (Alibaba Group, Hangzhou, China); Mengxue Li (Alibaba Group, Hangzhou, China); Tonglei Guo (Alibaba Group, Hangzhou, China); Changlong Yu (The Hong Kong University of Science and Technology, Hong Kong, China); Fei Sun (Alibaba Group, Hangzhou, China); Taiwei Jin (Alibaba Group, Hangzhou, China); Wilfred Ng (The Hong Kong University of Science and Technology, Hong Kong, China);
584 Mitigating Popularity Bias in Recommendation via Counterfactual Inference
[Abstract]
Ming He (Beijing University of Technology, Beijing, China); Changshu Li (Beijing University of Technology, Beijing, China); Xinlei Hu (Beijing University of Technology, Beijing, China); Xin Chen (Beijing University of Technology, Beijing, China); Jiwen Wang (Beijing University of Technology, Beijing, China);
594 Efficient Dual-process Cognitive Recommender Balancing Accuracy and Diversity
[Abstract]
Yixu Gao (Fudan University, Shanghai, China); Kun Shao (Huawei Noah’s Ark Lab, Beijing, China); Zhijian Duan (Peking University, Beijing, China); Zhongyu Wei (Fudan University, Shanghai, China); Dong Li (Huawei Noah’s Ark Lab, Beijing, China); Bin Wang (Huawei Noah’s Ark Lab, Beijing, China); Mengchen Zhao (Huawei Noah’s Ark Lab, Beijing, China); Jianye Hao (Huawei Noah’s Ark Lab, Beijing, China);
629 Learning and Fusing Multiple User Interest Representations for Sequential Recommendation
[Abstract]
Ming He (Beijing University of Technology, Beijing, China); Tianshuo Han (Beijing University of Technology, Beijing, China); Tianyu Ding (Beijing University of Technology, Beijing, China);
2 Industry Session # 2
Data Management and Search,
(April 13 (Wednesday), 2022, 10.15-12.30 , Hall 4)
Invited Talk / Panel: DocLens: Digiizing healthcare documents [Abstract]
Gaurav Aggarwal (Google); Sujoy Paul (Google); Narayan Hegde (Google);
627 Query-Document Topic Mismatch Detection
[Abstract]
Sahil Chelaramani (Microsoft); Ankush Chatterjee (Microsoft); Sonam Damani (Microsoft); Kedhar Nath Narahari (Microsoft); Meghana Joshi (Microsoft); Manish Gupta (Microsoft); Puneet Agrawal (Microsoft);
561 Beyond QA: ‘Heuristic QA’ Strategies in JIMI
[Abstract]
Shuangyong Song (JD AI Research, Beijing, China); Bo Zou (JD AI Research, Beijing, China); Jianghua Lin (JD AI Research, Beijing, China); Xiaoguang Yu (JD AI Research, Beijing, China); Xiaodong He (JD AI Research, Beijing, China);
571 SQLG+: Efficient 𝑘-hop Query Processing on RDBMS
[Abstract]
Li Zeng (Huawei Technologies Co., Ltd, China); Jinhua Zhou (Huawei Technologies Co., Ltd, China); Shijun Qin (Huawei Technologies Co., Ltd, China); Haoran Cai (Huawei Technologies Co., Ltd, China); Rongqian Zhao (Huawei Technologies Co., Ltd, China); Xin Chen (Huawei Technologies Co., Ltd, China);
3 Industry Session # 3
Industrial Machine Learning Applications,
(April 14 (Thursday), 2022, 10.15-12.30 , Hall 4)
570 Modeling Long-Range Travelling Times with Big Railway Data
[Abstract]
Wenya Sun (The University of Hong Kong); Tobias Grubenmann (University of Bonn, Germany); Reynold Cheng (The University of Hong Kong); Ben Kao (The University of Hong Kong); Waiki Ching (The University of Hong Kong);
579 Multi-scale Time Based Stock Appreciation Ranking Prediction via Price Co-movement Discrimination
[Abstract]
Ruyao Xu (East China Normal University, Shanghai, China); Dawei Cheng (Tongji University, Shanghai, China); Cen Chen (East China Normal University, Shanghai, China); Siqiang Luo (Nanyang Technological University, Singapore); Yifeng Luo (East China Normal University, Shanghai, China); Weining Qian (East China Normal University, Shanghai, China);
591 RShield: A Refined Shield for Complex Multi-Step Attack Detection based on Temporal Graph Network
[Abstract]
Weiyong Yang (Nanjing University, Nanjing, China); Peng Gao (NARI Information & Communication Technology co., ltd, Nanjing, China); Hao Huang (Nanjing University, Nanjing, 210008, China); Xingshen Wei (NARI Information & Communication Technology co., ltd, Nanjing, China); Wei Liu (NARI Information & Communication Technology co., ltd, Nanjing, China); Shishun Zhu (NARI Information & Communication Technology co., ltd, Nanjing, China); Wang Luo (NARI Information & Communication Technology co., ltd, Nanjing, China);
592 Inter-and-Intra Domain Attention Relational Inference for Rack Temperature Prediction in Data Center
[Abstract]
Fang Shen (Alibaba Group, China); Zhan Li (Alibaba Group, China); Bing Pan (Alibaba Group, China); Ziwei Zhang (Tsinghua University, Beijing, China); Jialong Wang (Alibaba Group, China); Wendy Zhao (Alibaba Group, China); Xin Wang (Tsinghua University, Beijing, China); Wenwu Zhu (Tsinghua University, Beijing, China);

7. Demo and PhD Consortium Session

Date, Time, and Hall Session Name Paper ID Paper Title
April 12 (Tuesday), 2022, 13.30-14.45, Hall 1 Demo presentations 598 An Interactive Data Imputation System; [Abstract]
Yangyang Wu (Zhejiang University); Xiaoye Miao (Zhejiang University)*; Yuchen Peng (Zhejiang University); Lu Chen (Zhejiang University); Yunjun Gao (Zhejiang University); Jianwei Yin (Zhejiang University).
599 FoodChain: A Food Delivery Platform Based on Blockchain for Keeping Data Privacy; [Abstract]
Rodrigo B Folha (Federal University of Pernambuco)*; Valéria Times (Federal University of Pernambuco); Arthur Carvalho (Miami University); André Araújo (Federal University of Alagoas); Henrique Couto (Federal University of Alagoas); Flaviano Viana (Federal University of Pernambuco).
601 A scalable lightweight RDF knowledge retrieval system; [Abstract]
Yuming Lin (Guilin University of Electronic Techonlogy)*; Chuangxin Fang (Guilin University of Electronic Technology).
603 CO-AutoML: An Optimizable Automated Machine Learning System; [Abstract]
Chunnan Wang (HIT); Hongzhi Wang (Harbin Institute of Technology)*; Xu Bo (HIT); Xintong Song (Harbin Institute of Technology); Xiangyu Shi (Harbin Institute of Technology); Yuhao Bao (Harbin Institute of Technology).
604 OIIKM: A System for Discovering Implied Knowledge from Spatial Datasets Using Ontology; [Abstract]
Long Wang (Guilin University Of Electronic Technology)*; Xuguang Bao (Guilin University of Electronic Technology); Liang Chang (Guilin University of Electronic Technology); Tianlong Gu (Guilin University Of Electronic Technology).
608 IDMBS: An Interactive System to Find Interesting Co-location Patterns Using SVM; [Abstract]
Yuxiang Zhang (Guilin University of Electronic Technology)*; Xuguang Bao (Guilin University of Electronic Technology); Liang Chang (Guilin University of Electronic Technology); Tianlong Gu (Guilin University Of Electronic Technology).
609 A Secure Trajectory Similarity Search System; [Abstract]
Yiping Teng (Shenyang Aerospace University)*; Fanyou Zhao (Shenyang Aerospace University); Jiayv Liu (Shenyang Aerospace University); Mengfan Zhang (Shenyang Aerospace University); Jihang Duan (Shenyang Aerospace University); Zhan Shi (Shenyang Aerospace University).
612 Data-based Insights for the Masses: Scaling Natural Language Querying to Middleware Data; [Abstract]
Kausik Lakkaraju (University of South Carolina)*; Vinamra Palaiya (Tantiv4); Sai Teja Paladi (University of South Carolina); Chinmayi Appajigowda (Tantiv4); Biplav Srivastava (AI Institute, University of South Carolina); Lokesh Johri (tantiv4).
615 Identifying relevant sentences for travel blogs from Wikipedia articles; [Abstract]
Arnav Kapoor (International Institute of Information Technology, Hyderabad)*; Manish Gupta (Microsoft).
PhD Consortium presentations 630 Neuro-Symbolic XAI for Computational Drug Repurposing; [Abstract]
Martin Drancé Martin Drancé (University of Bordeaux)
639 Leveraging Non-negative Matrix Factorization for Document Summarization; [Abstract]
Alka Khurana (Department of Computer Science, University of Delhi).