Symposium on Data Markets (SDM)

Abstract: While it is widely acknowledged that data is one of the most valuable commodities of the 21st century,  the development of market platforms for data is still in its early stages.   This talk will look at several different types of data markets to see if we can identify some common requirements and functions and use those as the motivation for the design of an architectural framework for such platforms.   Particular attention will be paid to areas where data management technologies such as those of interest to the VLDB community can play a role.    The goal is to identify new opportunities for data systems research as well as to develop ways to help users and organizations unlock the value and potential of their data resources to enable data-driven discovery.

Bio: MICHAEL J. FRANKLIN is the Morton D. Hull Distinguished Service Professor of Computer Science and Sr. Advisor to the Provost for Computing and Data Science at the University of Chicago.   At Chicago he was Liew Family Chair of the Computer Science Department and is a co-founder of the Data Science Institute.   Previously he was Thomas M. Siebel Professor of Computer Science at the University of California, Berkeley where he also served a term as Chair of the Computer Science Division.  As Co-Director of the Algorithms, Machines and People Laboratory (AMPLab) he was one of the original creators of Apache Spark, a leading open source platform for advanced data analytics and machine learning that was initially developed at the lab.  He is a Member of the American Academy of Arts and Sciences and is a Fellow of the ACM and the American Association for the Advancement of Science.  He received the 2022 ACM SIGMOD Systems Award with the team that developed Spark, and is a two-time recipient of the ACM SIGMOD “Test of Time” award. He holds a Ph.D. in Computer Sciences from the Univ. of Wisconsin (1993).

Abstract: Data is considered the 21st century's most valuable commodity. Nevertheless, existing systems are falling behind in bridging the gap between data and humans, making data accessible and useful only to the few. Data marketplaces are promising to help bridge this gap by offering access to a multitude of data. However, potential data consumers come to confront crude search interfaces that impede rather than promote data discoverability. In this talk, we will scratch the surface of the data search problem and give a flavor of an exciting research territory that can help unleash the potential of data markets.

Bio: Georgia Koutrika is Research Director at Athena Research Center in Greece. Previously, she has worked in multiple roles at HP Labs, IBM Almaden, and Stanford. Her work focuses on interactive and intelligent data exploration and data management systems, and combines advanced data analysis, deep learning, and natural language processing techniques. 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 110 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, and the PVLDB Advisory board. She is co-EiC for VLDB Journal, PC co-chair for VLDB 2023, co-EiC of Proceedings of the VLDB (PVLDB).  Georgia has recently received the EDBT 2023 Test-of-Time award. She was general chair for ACM SIGMOD 2016, and she has served in various other organization roles, including EDBT 2023 and ICDE 2021 sponsorship chair. She is the chair of ACM Europe Working Group on Seasonal Schools.

Abstract: Unlike most physical goods, data has some interesting properties. For example, someone may have a privacy interest in the data. For another example, replication is possible essentially for free. Whereas there is a “law of diminishing returns” for the utility we ascribe to most material possessions, data appears to follow a reverse “law of increasing returns”. As we develop markets for data, and technologies to support these markets, it is important that we keep in mind these surprising properties. Otherwise, we may see undesirable behaviors in data markets.

Bio: H. V. Jagadish is Edgar F Codd Distinguished University Professor and Bernard A Galler Collegiate Professor of Electrical Engineering and Computer Science at the University of Michigan in Ann Arbor, and Director of the Michigan Institute for Data Science.  Prior to 1999, he was Head of the Database Research Department at AT&T Labs, Florham Park, NJ.

Professor Jagadish is well known for his broad-ranging research on information management, and has over 200 major papers and 38 patents, with an H-index of 101.Ê He is a fellow of the ACM, "The First Society in Computing," (since 2003) and of AAAS (since 2018).  He currently chairs the board of the Academic Data Science Alliance and previously served on the board of the Computing Research Association (2009-2018).Ê He has been an Associate Editor for the ACM Transactions on Database Systems (1992-1995), Program Chair of the ACM SIGMOD annual conference (1996), Program Chair of the ISMB conference (2005), a trustee of the VLDB (Very Large DataBase) foundation (2004-2009), Founding Editor-in-Chief of the Proceedings of the VLDB Endowment (2008-2014), and Program Chair of the VLDB Conference (2014).Ê Since 2016, he is Editor of the Springer (previously Morgan & Claypool) ÒSynthesisÓ Lecture Series on Data Management.  Among his many awards, he won the David E Liddle Research Excellence Award (at the University of Michigan) in 2008, the ACM SIGMOD Contributions Award in 2013, and the Distinguished Faculty Achievement Award (at the University of Michigan) in 2019.  His popular MOOC on Data Science Ethics is available on both EdX and Coursera.