Stratos Idreos is a Gordon McKay Professor of Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences. He leads DASlab, the Data Systems Laboratory at Harvard. His research focuses on building a grammar for systems with the goal of making it dramatically easier or even automating in many cases the design of workload and hardware-conscious systems for diverse applications including relational data analytics, NoSQL, machine learning, and Blockchain. For his doctoral work on Database Cracking, Stratos was awarded the 2011 ACM SIGMOD Jim Gray Doctoral Dissertation award and the 2011 ERCIM Cor Baayen award. In 2015 he was awarded the IEEE TCDE Rising Star Award from the IEEE Technical Committee on Data Engineering for his work on adaptive data systems. In 2020 he received the ACM SIGMOD Contributions award for his work on reproducible research and in 2022 he received the ACM SIGMOD Test of Time Award for his work on raw data processing. Stratos was PC Chair of ACM SIGMOD 2021 and IEEE ICDE 2022, he is the founding editor of the ACM/IMS Journal of Data Science and the chair of the ACM SoCC Steering Committee.

Abstract: TBD

Qiong Luo is a Professor of Computer Science and Engineering at the HongKong University of Science and Technology and a Professor of Data Science and Analytics at the Hong Kong University of Science and Technology (Guangzhou). Her research interests are in big data systems, parallel and distributed systems, and data-intensive applications. Her current focus is on data management on modern hardware, GPU acceleration for data analytics, and data processing support for e-science. Qiong has published over 160 research papers at international conferences and journals, and the number of citations on her work is more than 9,500 according to Google Scholar. She has served diligently on the program committees and organization committees of major conferences, is the Program Co-Chair of the EDBT 2024 conference, and is an Associate Editor of the Distributed and Parallel Databases Journal and the VLDB Journal. Qiong received her Ph.D. in Computer Sciences from the University of Wisconsin-Madison, her M.S. and B.S. in Computer Sciences from Beijing (Peking) University, China.

Minos Garofalakis is the Director of the Information Management Systems Institute (IMSI) at the ATHENA Research and Innovation Center and a Professor at the School of ECE at the Technical University of Crete. He received the MSc and PhD degrees from the University of Wisconsin-Madison in 1994 and 1998, respectively, and previously held senior/principal researcher positions at Bell Labs, Lucent Technologies (1998-2005), Intel Research Berkeley (2005-2007), and Yahoo! Research (2007-2008); in parallel, he held an Adjunct Associate Professor position at the EECS Department of UC Berkeley (2006-2008). He also worked as a research consultant for Amazon Web Services (AWS) during 2022-2023, and is the co-founder of Agora Labs, a startup company bringing state-of-the-art data privacy technologies to the medical domain.

Minos’s research interests lie in the broad area of Big Data Analytics. He has published over 170 scientific papers in refereed international conferences and journals, and has delivered several invited keynote talks and tutorials in major international events. His work has resulted in 36 US Patent filings (29 patents issued) for companies such as Lucent, Yahoo!, and AT&T. Google Scholar gives over 16,000 citations to his work, and an h-index value of 68. He is a Fellow of the ACM and IEEE, a Member of the Academia Europaea, and a recipient of the TUC “Excellence in Research” Award (2015) and the Bell Labs President’s Gold Award (2004).

Abstract: We shall consider four problems and their efficient solution when the dataset is very large. (1) A brief introduction to locality-sensitive hashing; e.g., finding similar records or plagiarized documents. (2) Counting distinct items; e.g., unique visitors to a Website. (3) Random sampling of relational queries on databases. (4) Counting triangles; an example of optimal join computation, with an application to finding communities in social networks.