Databases Theory and Applications

Graph reachability is a fundamental problem in database theory and many other areas of computer science. In this talk, we consider quantum graph reachability problem, which originally arose in verification and analysis of quantum programs and model-checking quantum systems, but may also interest database community. We will discuss the following issues: 1. How we can naturally define a graph structure in the state Hilbert space of a quantum system from its (discrete-time) dynamics? 2. Why the approaches to classical graph reachability problem do not work for quantum reachability problem? 3. Strongly connected component decomposition theorem for quantum graphs. At the end of the talk, a series of open problems will be pointed out, including possible applications to database search in future quantum computers. Short Biography. Mingsheng Ying was Cheung Kong Chair Professor at Tsinghua University and Director of the Scientific Committee, the National Key Laboratory of Intelligent Technology and Systems, China. Since 2008, he is Distinguished Professor and Research Director of the Centre for Quantum Software and Information, University of Technology Sydney, Australia. He is also Deputy Director for Research of the Institute of Software, Chinese Academy of Sciences. Ying’s research interests include quantum computation and quantum information, programming theory, and logical foundations of artificial intelligence. In particular, he developed Hoare logic for quantum programs and proved its (relative) completeness (TOPLAS’11). He defined the notion of invariants for quantum programs (POPL’17). He initiated the research line of model checking quantum Markov chains (CONCUR’12–14, TOCL’14). Ying is the author of Foundations of Quantum Programming (Morgan Kaufmann 2016). Big Data Integration Divesh Srivastava and Masaru Kitsuregawa 1 AT&T Labs Research, USA 2 National Institute of Informatics, University of Tokyo, Japan Abstract. The Big Data era is upon us: data are being generated, collected and analyzed at an unprecedented scale, and data-driven decision making is sweeping through all aspects of society. Since the value of data explodes when it can be linked and fused with other data, addressing the big data integration (BDI) challenge is critical to realizing the promise of Big Data. BDI differs from traditional data integration in many dimensions: (i) the number of data sources, even for a single domain, has grown to be in the tens of thousands, (ii) many of the data sources are very dynamic, as a huge amount of newly collected data are continuously made available, (iii) the data sources are extremely heterogeneous in their structure, with considerable variety even for substantially similar entities, and (iv) the data sources are of widely differing qualities, with significant differences in the coverage, accuracy and timeliness of data provided. This talk presents techniques to address these novel challenges faced by big data integration, and identifies a range of open problems for the community. The Big Data era is upon us: data are being generated, collected and analyzed at an unprecedented scale, and data-driven decision making is sweeping through all aspects of society. Since the value of data explodes when it can be linked and fused with other data, addressing the big data integration (BDI) challenge is critical to realizing the promise of Big Data. BDI differs from traditional data integration in many dimensions: (i) the number of data sources, even for a single domain, has grown to be in the tens of thousands, (ii) many of the data sources are very dynamic, as a huge amount of newly collected data are continuously made available, (iii) the data sources are extremely heterogeneous in their structure, with considerable variety even for substantially similar entities, and (iv) the data sources are of widely differing qualities, with significant differences in the coverage, accuracy and timeliness of data provided. This talk presents techniques to address these novel challenges faced by big data integration, and identifies a range of open problems for the community. Short Biography. Divesh Srivastava is the head of Database Research at AT&T Labs-Research. He is a Fellow of the Association for Computing Machinery (ACM) and the managing editor of the Proceedings of the VLDB Endowment (PVLDB). He has served as a trustee of the VLDB Endowment, as an associate editor of the ACM Transactions on Database Systems (TODS), as an associate Editorin-Chief of the IEEE Transactions on Knowledge and Data Engineering (TKDE), and as a general or program committee co-chair of many conferences. He has presented keynote talks at several international conferences, and his research interests and publications span a variety of topics in data management. He received his Ph.D. from the University of Wisconsin, Madison, USA, and his Bachelor of Technology from the Indian Institute of Technology, Bombay, India. Short Biography. Masaru Kitsuregawa received his Information Engineering Ph.D. degree from the University of Tokyo in 1983. Since then he joined the Institute of Industrial Science, the University of Tokyo, and is currently a professor. He is also a professor at Earth Observation Data Integration & Fusion Research Initiative of the University of Tokyo since 2010. He also serves Director General of National Institute of Informatics since 2013. Dr. Kitsuregawa's research interests include Database Engineering, and he had been principal researcher of Funding Program for World-Leading Innovative R&D on Science and Technology, MEXT Grant-in-Aids Program for “Info-Plosion”, and METI's Information Grand Voyage Project. He had served President of Information Processing Society of Japan from 2013 to 2015. He served as a committee member for a number of international conferences, including ICDE Steering Committee Chair. He is an IEEE Fellow, ACM Fellow, IEICE Fellow and IPSJ Fellow, and he won ACM SIGMOD E.F.Codd Contributions Award, Medal with Purple Ribbon, 21st Century Invention Award, and C&C Prize. Big Data Integration XVII