EDBT/ICDT 2023 Invited Talks

We are very pleased to announce the EDBT/ICDT 2023 invited speakers.

Keynote 1: A Researcher's Digest of GQL
by Leonid Libkin

Abstract: GQL (Graph Query Language) is being developed as a new ISO standard for graph query languages that will play the same role for graph databases as SQL plays for relational. In parallel, an extension of SQL for querying property graphs, SQL/PGQ, is added to the SQL standard; it shares the graph pattern matching functionality with GQL. With both standards being hard-to-understand specifications of hundreds of pages, and only available in 2023-2024, this talk will present clean formal calculi that underlie the main pattern matching features of GQL and SQL/PGQ, as well as querying facilities of GQL.

Leonid Libkin is a professor of computer science at the University of Edinburgh and query language researcher at RelationalAI; he is also affiliated with École Normale Supérieure in Paris. He was previously scientific advisor to Neo4j, professor at the University of Toronto, and member of research staff at Bell Laboratories in Murray Hill. His main research interests are in the areas of data management and logic in computer science. He has written five books and over 250 technical papers. His awards include a Marie Curie Chair Award, a Royal Society Wolfson Research Merit Award, and six Best Paper Awards. He has chaired program committees of major database and logic conferences (PODS, LICS, ICDT), served as chair of the 2010 Federated Logic Conference and general chair of PODS. He is an ACM fellow, a fellow of the Royal Society of Edinburgh, and a member of Academia Europaea.

Keynote 2: Robust Query Processing: Where Geometry Beats ML!
by Jayant Haritsa

Abstract: Over the past half-century, the design and implementation of declarative query processing techniques in relational database systems has been a foundational topic for the database research community. Despite this sustained study, the unfortunate reality is that the resulting solutions have largely remained a “black art” due to the well-documented complexities and challenges of database query processing. The situation is particularly problematic since the performance degradation faced by database queries can be huge, reaching orders of magnitude as compared to an oracular ideal. In recent times, there have been attempts to completely rethink this chronic problem from first principles. In particular, the following two lines of inquiry have emerged: (a) The development of learning techniques for characterizing and predicting query performance; and (b) The design of geometric search strategies for leveraging execution plan trajectories in the parameter space. Given the current passion for data-driven models, it is no surprise that learning methods have occupied center-stage in the literature. However, in this talk, we will attempt to make the case that, despite an apparent lack of sophistication, it is geometry which offers a potent and superior solution for robust query processing. Specifically, we will explain how basic properties, such as monotonicity, concavity and bounded slopes, which typically hold in practice, can lead to surprisingly strong and quantifiable guarantees on runtime performance. At the end, a suite of open design problems, such as dimensionality reduction, that could benefit from geometric perspectives will be outlined.

Jayant Haritsa is on the computer science faculty at the Indian Institute of Science, Bangalore, since 1993. He received a BTech degree from the Indian Institute of Technology (Madras), and the MS and PhD degrees from the University of Wisconsin (Madison). He is a Fellow of ACM and IEEE for his contributions to database engine design and analysis. He has served as the PC co-chair of ICDE 2010 and VLDB 2016, and as the President of ACM India during 2020-22. He has occupied sabbatical positions at Lucent Bell Labs, IBM India Research Lab, and Microsoft Research India.

Keynote 3: Dataset Search for Data Discovery, Augmentation, and Explanation
by Juliana Freire

Abstract: Recent years have seen an explosion in our ability to collect and catalog immense amounts of data about our environment, society, and populace. Moreover, with the push towards transparency and open data, scientists, governments, and organizations are increasingly making structured data available on the Web and in various repositories and data lakes. Combined with advances in analytics and machine learning, the availability of such data should in theory allow us to make progress on many of our most important scientific and societal questions. However, this opportunity is often missed due to a central technical barrier: it is currently nearly impossible for domain experts to weed through the vast amount of available information to discover datasets that are needed for their specific application. While search engines have addressed the discovery problem for Web documents, there are many new challenges involved in supporting the discovery of structured data---from crawling the Web in search of datasets, to the need for dataset-oriented queries and new strategies to rank and display results. I will discuss these challenges and present our recent work in this area. In particular, I will introduce a new class of data-relationship queries that, given a dataset, identifies related datasets; I will describe a collection of methods that efficiently support different kinds of relationships that can be used for data explanation and augmentation; and I will demonstrate Auctus, an open-source dataset search engine that we have developed at the NYU VIDA Center.

Juliana Freire is a Professor of Computer Science and Data Science at New York University. She was the elected chair of the ACM Special Interest Group on Management of Data (SIGMOD), served as a council member of the Computing Research Association’s Computing Community Consortium (CCC), and was the NYU lead investigator for the Moore-Sloan Data Science Environment. She develops methods and systems that enable a wide range of users to obtain trustworthy insights from data. This spans topics in large-scale data analysis and integration, visualization, machine learning, provenance management, and web information discovery, and different application areas, including urban analytics, predictive modeling, and computational reproducibility. Freire has co-authored over 200 technical papers (including 11 award-winning publications), several open-source systems, and is an inventor of 12 U.S. patents. She is an ACM Fellow, a AAAS Fellow, and a recipient of an NSF CAREER, two IBM Faculty awards, and a Google Faculty Research award. She received the ACM SIGMOD Contributions Award in 2020. Her research has been funded by the National Science Foundation, DARPA, Department of Energy, National Institutes of Health, Sloan Foundation, Gordon and Betty Moore Foundation, W. M. Keck Foundation, Google, Amazon, AT&T Research, Microsoft Research, Yahoo! and IBM. She received a B.S. degree in computer science from the Federal University of Ceara (Brazil), and M.Sc. and Ph.D. degrees in computer science from the State University of New York at Stony Brook.

Keynote 4: Compact Data Structures Meet Databases
by Gonzalo Navarro

Abstract: The recent trend initiated with the AGM bound and the discovery of worst-case-optimal (wco) multijoin algorithms has profoundly modified the way in which we understand the complexity of the most costly operation in database queries. Wco algorithms have evolved since then, from the first complicated ones to the relatively simple Leapfrog TrieJoin (LTJ). A persistent problem in all those algorithms, which has undermined their full adoption in real database systems, is their prohibitive space requirements. LTJ, for example, needs d! tries indexing the rows of each table with d columns. Even in the important application of graph databases, which tend to feature large multijoins, labeled graphs are modeled as a single table with 3 columns, and thus LTJ needs essentially 6 copies of the database. In this talk I will show how Compact Data Structures (CDS) can help solving the space problem. CDS are data representations that use space close to the entropy of the data while supporting the data structure operations as efficiently as possible. In particular, designing CDS involves removing as much redundancy as possible, both in the data itself and in its additional data structures. The problem of the redundant indexing introduced by wco multijoin algorithms is a natural candidate to apply CDS. I will present two recent success stories in this regard: Qdags, which build on a geometric representation of the data, and the Ring, a data structure based on string indexing. Both structures offer wco multijoins and require only one copy of the data on graph databases. In practice, they use an order of magnitude less space than current systems, and are competitive with them in query time, outperforming all the competitors in many cases. Their evolution for larger values of d, both in space and time, leads to fascinating questions we are just starting to uncover.

Gonzalo Navarro is a professor at the Department of Computer Science of the University of Chile. His areas of interest include algorithms and data structures, compression, graph databases, and text searching. He is an associate researcher at the Center for Biotechnology and Bioengineering (CeBiB) and at the Millennium Institute for Foundational Research on Data (IMFD). He has been PC (co-)chair of several conferences, including SPIRE 2001, SPIRE 2005, SIGIR 2005 Posters, SISAP 2008, SISAP 2012, LATIN 2016, SPIRE 2018, CPM 2018, ESA B 2022, and ALENEX 2023. He is the Editor in Chief of the ACM Journal of Experimental Algorithmics and a member of the Editorial Board of ACM Transactions on Algorithms and Information Systems. He has been guest editor of special issues in ACM SIGSPATIAL, Journal of Discrete Algorithmics, Information Systems, Information and Computation, and Algorithmica. He has coauthored two books, "Fast and Flexible Pattern Matching in Strings" and "Compact Data Structures", published by Cambridge University Press, and around 200 papers in international journals and 275 in international conferences. He is one of the most prolific and highly cited authors in Latin America. He has received 7 Best Paper Awards in conferences, 4 Google Research awards, a Highest Cited Paper Award from Elsevier, and an Award Scopus Chile. He is an ACM Distinguished Member.

Keynote 5: From a DB engine to a data management platform: On the design and making of HANA Cloud
by Anil Goel

Abstract: SAP HANA ventured out as a commercial database engine over a decade ago for supporting large-scale enterprise-critical applications. Available on-premises and as a hosted solution, SAP HANA quickly became an unrivaled and trendsetting database for processing HTAP enterprise workloads. With sophisticated In-Memory capabilities exploiting modern hardware, SAP HANA helped consolidate complex IT landscapes and allowed the development of a novel class of business applications. As SAP HANA engine matured, it quickly started to face the industry need for evolution towards a cloud-based data management platform targeting qualities beyond a high-performant database engine along several new dimensions including operability at scale, security and total cost for managing very large datasets. Some such qualities included low effort administration and availability, zero-downtime upgrades, multi-tenancy, full-stack encryption and key management, support of file-organized datasets residing in cloud storage, as well as multi-model capabilities to query graph, document, and relational data within the same database context. SAP HANA Cloud is now a cloud-based data management platform supporting traditional enterprise-scale applications as well as the interactive knowledge worker exploring large amounts of complex structured datasets. It is also the de-facto standard for hyper-scaler agnostic and multi-cloud data management scenarios. This talk will touch upon some of the challenges in designing and evolving towards SAP HANA Cloud and outline the steps ahead.

Anil K Goel is CTO / Head of Technology office for SAP HANA Database and Analytics, where he works with the globally distributed data management and analytics engineering teams to drive forward looking architectures, vision, strategy, research, and pathfinding. He also oversees data management and analytics related collaborative research and internship programs with many universities globally. His interests include database system architecture, in-memory and large-scale distributed computing, self-management of software systems and cost modelling. Anil earned a PhD in computer science from University of Waterloo. He holds MTech (CS) from Indian Institute of Technology, Delhi, and BE (Electronics and Communications Engineering) from University of Delhi.