Panel

Title: Futuristic Data Interfaces
April 12, 2022, 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:

Panelists:

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).