Keynote Speech 1: The tragedy of the (Data) Commons
Time: 9:00-10:20, September 18, 2020
Prof. James Hendler, Rensselaer Polytechnic Institute
Abstract: The tragedy of the commons, first proposed by William Lloyd in 1833, is an economic problem in which every individual has an incentive to consume a resource at the expense of every other individual with no way to exclude anyone from consuming. It results in overconsumption, under investment, and ultimately depletion of the resource. While the direct application of these principles to data seems like a bit of a reach, it becomes clear that data sharing risks much of the same problem - people wishing to protect their own data while having access to other people’s. Motivation for sharing is thus weak, until incentives and policies are in place. However, now that these incentives and policies are coming into practice, the implementation will have high impact on the benefits. Thus, as we work to make the world a FAIRer place, we must consider how the way data, and especially metadata, is represented and shared.
Biography: James Hendler is the Director of the Institute for Data Exploration and Applications and the Tetherless World Professor of Computer, Web and Cognitive Sciences at RPI. He also is acting director of the RPI-IBM Artificial Intelligence Research Collaboration. Hendler has authored over 400 books, technical papers and articles in the areas of Semantic Web, artificial intelligence, agent-based computing and high-performance processing. Hendler is a Fellow of the AAAI, BCS, the IEEE, the AAAS and the ACM. He was the first computer scientist to serve on the Board of Reviewing editors for Science. In 2010, Hendler wwas selected as an “Internet Web Expert” by the US government. In 2013, he was appointed as the Open Data Advisor to New York State. In 2016, became a member of the National Academies Board on Research Data and Information and in 2018 became chair of the ACM’s US technology policy committee and was elected a Fellow of the National Academy of Public Administration.
Keynote Speech 2: Towards efficient computation of network structural stability
Time: 10:40-12:00, September 18, 2020
Prof. Xuemin Lin, The University of New South Wales
Abstract: With the emergence of large-scale networks, the study of network structural stability has recently received a great deal of attention in different areas such as social networks, the world wide web, and biology. The stability of a network indicates the ability of the network to maintain an acceptable level of service and/or to defend the attacks from the competitors. In this talk, we first introduce the stability models in different domains, their applications, and unique challenges that need to be addressed. We focus on three fundamental problems: (a) efficiently computing the stability of a given network, (b) motivating critical nodes and edges to enhance the network stability, and (c) defending critical nodes and edges against the attacks to network stability. Due to the fast evolvement of real-life networks, we also discuss the stability problems and their computation on dynamic graphs. We will explore the nature and lay the scientific foundation of these problems. Subsequently, we introduce novel computing paradigms and algorithms, indexing techniques, batch processing techniques, and distributed solutions. Finally, we discuss the future research directions in this important and growing research area.
Biography: Xuemin Lin is a UNSW distinguished Professor - Scientia Professor, and the head of database and knowledge research group in the school of computer science and engineering at UNSW. Xuemin is a distinguished visiting Professor at Tsinghua University and visiting Chair Professor at Fudan University. He is a fellow of IEEE. Xuemin's research interests lie in databases, data mining, algorithms, and complexities. Specifically, he is working in the areas of scalable processing and mining of large scale data, including graph, spatial-temporal, streaming, text and uncertain data.
Xuemin currently serves as the editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering (Jan 2017 - now). He was an associate editor of ACM Transactions Database Systems (2008-2014) and IEEE Transactions on Knowledge and Data Engineering (Feb 2013- Jan 2015), and an associate editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering (2015-2016), respectively. He has been regularly serving as a PC member and area chairs/SPC in SIGMOD, VLDB, ICDE, ICDM, KDD, CIKM, and EDBT. He is a PC co-chair of ICDE2019 and VLDB2022.
Keynote Speech 3: Design and implementation of new database engine, OoODE
Time: 9:00-10:20, September 19, 2020
Prof. Masaru Kitsuregawa, The University of Tokyo
Abstract: I will talk about the database engine which we have developed for more than 10 years. Out of order execution will be explained. After the end of Moore's law, performance of single processor core will stop. This talk will cover how we can design new type database engine which can handle many many core machines.
Biography: Director General of National Institute of Informatics and Professor at Institute of Industrial Science, the University of Tokyo. Received Ph.D. degree from the University of Tokyo in 1983. Served in various positions such as President of Information Processing Society of Japan (2013–2015) and Chairman of Committee for Informatics, Science Council of Japan（2014-2016）. He has wide research interests, especially in database engineering. He has received many awards including ACM SIGMOD E. F. Codd Innovations Award, IEICE Contribution Award, IPSJ Contribution Award, 21st Century Invention Award of National Commendation for Invention, Japan and C&C Prize, IEICE Contribution Award, IEEE Innovation in Societal Infrastructure Award and Japan Academy Award. In 2013, he awarded Medal with Purple Ribbon and in 2016, the Chevalier de la Legion D’Honneur. He is a fellow of ACM, IEEE, IEICE, IPSJ and honorary member of CCF.
Keynote Speech 4: Entity Linking and Data Privacy Protection for Spatiotemporal Data
Prof. Xiaofang Zhou, The University of Queensland
Abstract: Spatial trajectory analytics involves a wide range of research topics including data management, query processing, data mining and recommendation systems. It can find many applications in intelligent transport systems, social media analysis, location-based systems, urban planning and smart city. New opportunities arise with massive and rapidly increasing volumes of high-quality spatiotemporal data from many sources such as GPS devices, mobile phones and social network applications. Integrating trajectory data is a fundamental step for making sense of spatiotemporal data. In this talk we will discuss our recent work on spatiotemporal entity linking and privacy protection for moving objects data.
Biography: Professor Xiaofang Zhou is a Professor of Computer Science at The University of Queensland. His research focus is to find effective and efficient solutions for managing, integrating and analyzing very large amount of complex data for business, scientific and personal applications. He has been working in the area of spatial and multimedia databases, data quality, high performance database systems, data mining, streaming data analytics and recommendation systems. He is a Program Committee Chair for PVLDB 2020, SSTD 2017, CIKM 2016, ICDE 2013, and a General Chair of MDM 2018 and ACM Multimedia 2015. He has been an Associate Editor of The VLDB Journal, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Cloud Computing, World Wide Web Journal, Distributed and Parallel Databases, and IEEE Data Engineering Bulletin. He was the Chair of IEEE Technical Committee on Data Engineering (2015-2018), and a Fellow of IEEE.