Dr. Sihem Amer-Yahia
Towards AI-Powered Data-Informed Education
The Covid-19 health crisis has seen an increase in the use of digital work platforms from videoconferencing systems to MOOC-type educational platforms and crowdsourcing and freelancing marketplaces. These levers for sharing knowledge and learning constitute the premises of the future of work. Educational technologies coupled with AI hold the promise of helping learners and teachers. However, they are still limited in terms of social interactions, user experience and learning opportunities as they must address a tension between learner-centered and platform-centered goals. I will describe research at the intersection of data-informed recommendations and education theory and conclude with ethical considerations in building educational platforms.
Sihem Amer-Yahia is a Silver Medal CNRS Research Director and Deputy Director of the Lab of Informatics of Grenoble. She works on exploratory data analysis and fairness in job marketplaces. Before joining CNRS, she was Principal Scientist at QCRI, Senior Scientist at Yahoo! Research and Member of Technical Staff at at&t Labs. Sihem is PC chair for SIGMOD 2023 and vice president of the VLDB Endowment. She currently leads the Diversity, Equity and Inclusion initiative for the database community.
Prof. Kyuseok Shim
Seoul National University, Korea
Cardinality Estimation of Queries in Database Systems - Where are we now?
To process a query in database systems, the query optimizer selects the most efficient plan among the possible execution plans of the query. Without query optimization, database systems would be highly inefficient. Since the cost of a plan is estimated with the result size of each operator in the plan, the accurate cardinality estimation of subqueries is essential to produce an optimal execution plan of a query. Thus, there have been extensive works using histograms, wavelet synopses and locality sensitive hashing techniques for cardinality estimation of queries. Since deep learning models can reflect the underlying patterns and correlations of data well, deep learning models are recently investigated and shown to outperform the traditional methods for cardinality estimation of queries. In my talk, I will present an overview of the traditional as well as deep learning methods developed for cardinality estimation of queries in database systems.
Kyuseok Shim is a Professor at Department of Electrical and Computer Engineering at Seoul National University, Korea. He is currently an Editor-In-Chief of the VLDB Journal and was previously an Associate Editor for the IEEE TKDE, VLDB as well as PVLDB journals. He also served as a Program Co-chair for PAKDD 2003, WWW 2014, ICDE 2015, APWeb 2016, BigComp 2019 and ICDM 2019 conferences and have been serving on Program Committees of the leading database as well as data mining conferences including SIGMOD, SIGKDD, ICDE, ICDM, EDBT, VLDB, WWW and CIKM. He became an ACM fellow and an IEEE fellow for the contributions to scalable data mining and query processing in 2013 and 2019 respectively. He was previously a member of the VLDB Endowment Board of Trustees and is currently a steering committee member of PAKDD as well as DASFAA conferences. He served as the president of the Korean Information Scientist and Engineers (KIISE) in 2022 and became a member of Member of National Academy of Engineering of Korea in 2023. He has been working in the area of data mining, machine learning, privacy preservation, query processing, query optimization, data warehousing, semi-structured data (XML), stream data and histograms.
Prof. Angela Bonifati
Lyon 1 University, France
Property Graphs to Graph Ecosystems: A Round Trip
Property graphs are a widespread data model for representing interconnected multi-labeled data enhanced with properties as key/value pairs. These highly expressive graphs are used in a wide range of domains, such as social and transportation networks, biological networks, finance, cybersecurity, logistics and planning, to name a few. Property graphs are currently used in a variety of graph databases showing a fragmented landscape in terms of query and schema language support, indexing mechanisms and query evaluation and optimization strategies.
Motivated by our community-wide vision on future graph processing systems, in this talk I will present the underpinnings of unified data and query model abstractions as well as the principles of graph ecosystems. Many current graph query engines only support very limited subsets of graph data manipulation and data definition primitives. It becomes crucial to address efficient query evaluation for complex graph queries, in both static and streaming environments. I will conclude my talk by pinpointing several research directions and open problems for graph ecosystems.
Angela Bonifati (PhD, 2002) is a Professor of Computer Science at Lyon 1 University and at the CNRS Liris research lab, where she leads the Database Group. She is also an Adjunct Professor at the University of Waterloo in Canada since 2020. Her current research interests are on several aspects of graph databases, knowledge graphs and data integration. She has also co-authored more than 150 publications in top venues of the data management field, including two Best Paper awards (ICDE22, VLDB22 runner up). She is the recipient of the TCDE Impact Award 2023 and a co-recipient of an ACM Research Highlights Award 2022. She has co-authored two books (on Schema Matching and Mapping edited by Springer in 2011 and on Querying Graphs edited by Morgan & Claypool in 2018) and an invited paper in ACM Sigmod Record 2018 on Graph Queries. She was the Program Chair of ACM Sigmod 2022 and EDBT 2020 and she is currently an Associate Editor for both Proceedings of VLDB and IEEE ICDE. She is an Associate Editor for several journals, including the VLDB Journal and ACM TODS. She is currently the President of the EDBT Executive Board and a member of the Sigmod Executive Committee.
Prof. Jianliang Xu
Hong Kong Baptist University, Hong Kong China
Large-scale Geospatial Analytics: Challenges and Opportunities
Geospatial analytics has become a critical tool in various fields such as crime science, transportation science, epidemiology, ecology, and urban planning. However, with the exponential growth of big geospatial data, commonly used geospatial analytics tools face efficiency issues that make handling large-scale datasets challenging. This has raised concerns among domain experts, highlighting the need for more efficient approaches. In this talk, we will address the challenges and problems associated with large-scale geospatial analytics. We will present our recent efforts to develop efficient geospatial analytics tools, including kernel density visualization for hotspot detection and K-function for correlation analysis. We will also discuss future opportunities in this field.
Jianliang Xu is the Head and Professor of the Department of Computer Science at Hong Kong Baptist University, where he leads the Database Research Group. He received his BEng degree from Zhejiang University and his PhD degree from Hong Kong University of Science and Technology. His current research interests include big data management, data security & privacy, and blockchain technology. With an h-index of 54, he has published more than 250 technical papers in these areas, most of which appeared in leading journals and conferences including SIGMOD, PVLDB, ICDE, TKDE, and VLDBJ. He is listed among the world's top 2% of scientists by Stanford University. He has served as a conference co-chair for a number of international conferences and an Associate Editor for several top-tier international journals including IEEE Transactions on Knowledge & Data Engineering (TKDE, 2014-2020) and Proceedings of the VLDB Endowment (PVLDB, 2023-2024). More details can be found at: https://www.comp.hkbu.edu.hk/~xujl/.