Keynote Speeches

Keynote Speech I: Exploring Change

Time: 9:20-10:40, August 2, 2019

Location: 2F Yinxing Hall

Divesh Srivastava

Head of Database Research, AT&T Labs-Research


Data and schema in datasets experience many different kinds of change. Values are inserted, deleted or updated; rows appear and disappear; columns are added or repurposed, and so on. In such a dynamic situation, users might wonder: How many changes have there been in the recent minutes, days or years? What kind of changes were made at which points of time? How dirty is the data? The fact that data changed can hint at different hidden processes: a frequently crowd-updated city name may be controversial; a person whose name has been recently changed may be the target of vandalism; and so on. To interactively explore such changes, we present our DBChEx (Database Change Explorer) prototype system. Using two real-world datasets, IMDB and Wikipedia infoboxes, we illustrate how users can gain valuable insights into data generation processes and data or schema evolution over time by a mix of serendipity and guided investigation using DBChEx. Finally, we identify a range of technical challenges that need to be addressed to fully realize our vision of change exploration.

This is joint work with T. Bleifuß, L. Bornemann, T. Johnson, D. Kalashnikov and F. Naumann.

Speaker Bio: Divesh Srivastava is the Head of Database Research at AT&T Labs-Research. He is a Fellow of the Association for Computing Machinery (ACM), the Vice President of the VLDB Endowment, on the ACM Publications Board and an associate editor of the ACM Transactions on Data Science (TDS). He has served as the managing editor of the Proceedings of the VLDB Endowment (PVLDB), as associate editor of the ACM Transactions on Database Systems (TODS), and as associate Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering (TKDE). 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.

Keynote Speech II: HAO Intelligence: Integrating Human Intelligence and Artificial Intelligence with Organizational Intelligence

Time: 11:10-12:30, August 2, 2019

Location: 2F Yinxing Hall

Xindong Wu

President of Mininglamp Academy of Sciences, Mininglamp Technology

Director of the Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology), Ministry of Education, China


We present a HAO Intelligence framework, which integrates human intelligence (HI), artificial intelligence (AI) and organizational intelligence, for domain-specific industrial applications. HAO Intelligence starts with Bigdata, discovers Big Knowledge, and facilitates human and machine synergism for complex problem solving. This talk discusses Bigdata, Big Knowledge and Big Wisdom, and instantiates HAO Intelligence with a Big Wisdom case study for intelligent catering services.

Speaker Bio: Dr. Xindong Wu is President of Mininglamp Academy of Sciences, Mininglamp Technology, Beijing, and Director of the Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology), Ministry of Education, China. He is a Fellow of the IEEE and the AAAS. He holds a PhD in Artificial Intelligence from the University of Edinburgh and Bachelor’s and Master’s degrees in Computer Science from the Hefei University of Technology, China. Dr. Wu’s research interests include data mining, Bigdata analytics, knowledge engineering, and Web systems.

Dr. Wu is the Steering Committee Chair of the IEEE International Conference on Data Mining (ICDM), the Editor-in-Chief of Knowledge and Information Systems (KAIS, by Springer), the Founding Chair (2002-2006) of the IEEE Computer Society Technical Committee on Intelligent Informatics (TCII), and an Editor-in-Chief of the Springer Book Series on Advanced Information and Knowledge Processing (AI&KP).  He was the Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering (TKDE, by the IEEE Computer Society) between January 1, 2005 and December 31, 2008, and is currently a Co-Editor-in-Chief of the ACM Transactions on Knowledge Discovery from Data (TKDD, by ACM). He has served as Program Committee Chair/Co-Chair for ICDM ’03 (the 3rd IEEE International Conference on Data Mining), KDD-07 (the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), CIKM 2010 (the 19th ACM Conference on Information and Knowledge Management), ASONAM 2014 (the 2014 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining), and ICBK 2017 (the 8th IEEE International Conference on Big Knowledge).

Professor Wu is the 2004 ACM SIGKDD Service Award winner and the 2006 IEEE ICDM Outstanding Service Award winner.  He received the 2012 IEEE Computer Society Technical Achievement Award “for pioneering contributions to data mining and applications”, and the 2014 IEEE ICDM 10-Year Highest-Impact Paper Award.  He won the Best Paper Awards from the 2005 and 2011 IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2005 & 2011) and the 2012 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2012), and also the IEEE ICDM-2007 Best Theory/Algorithms Paper Runner-Up Award.

Keynote Speech III: Using Massive Trajectory Data for Vehicle Routing

Time: 9:00-10:20, August 3, 2019

Location: 2F Yinxing Hall

Christian S. Jensen

Professor, Aalborg University


Massive vehicle trajectory data is becoming available that captures detailed information about vehicular transportation and holds the potential to transform vehicle routing profoundly. The availability of trajectory data renders the traditional routing paradigm obsolete. Instead, new and data-intensive paradigms that thrive on massive trajectory data are called for. The talk will cover three such paradigms, including so-called path-based routing, where costs are associated with paths and not just edges; on-the-fly routing, where all weights are not pre-computed, but are computed as needed during routing; and cost-oblivious routing, where no costs are associated with routes, but where historical trajectories are used directly for routing. These paradigms aim to yield better and more efficient routing.

Speaker Bio: Christian S. Jensen is Professor of Computer Science at Aalborg University, Denmark. His research concerns data management and data-intensive systems, and its focus is on temporal and spatio-temporal analytics. Christian is an ACM and an IEEE Fellow, and he is a member of Academia Europaea, the Royal Danish Academy of Sciences and Letters, and the Danish Academy of Technical Sciences. He has received several national and international awards for his research, most recently the 2019 IEEE TCDE Impact Award. He serves on the board of Villum Fonden, a major funder of technical and natural science research in Denmark; he is President of the steering committee of the Swiss National Research Program on Big Data; and in Germany, he serves on the scientific advisory board the Max Planck Institute for Informatics. He is Editor-in-Chief of ACM Transactions on Database Systems.

Keynote Speech IV: AI-Native Database

Time:10:40-12:00, August 3, 2019

Location: 2F Yinxing Hall

Guoliang Li

Professor, Tsinghua University


In big data era, database systems face three challenges. Firstly, the traditional heuristics-based optimization techniques (e.g., cost estimation, join order selection, knob tuning) cannot meet the high-performance requirement for large-scale data, various applications and diversified data. We can design learning-based techniques to make database more intelligent. Secondly, many database applications require to use AI algorithms, e.g., image search in database. We can embed AI algorithms into database, utilize database techniques to accelerate AI algorithms, and provide AI capability inside databases. Thirdly, traditional databases focus on using general hardware (e.g., CPU), but cannot fully utilize new hardware (e.g., ARM, AI chips). Moreover, besides relational model, we can utilize tensor model to accelerate AI operations. Thus, we need to design new techniques to make full use of new hardware.

To address these challenges, we design an AI-native database. On one hand, we integrate AI techniques into databases to provide self-configuring, self-optimizing, self-healing, self-protecting and self-inspecting capabilities for databases. On the other hand, we can enable databases to provide AI capabilities using declarative languages, in order to lower the barrier of using AI.

In this talk, I will introduce the five levels of AI-native databases and provide the open challenges of designing an AI-native database. I will also take automatic database knob tuning, deep reinforcement learning based optimizer, machine-learning based cardinality estimation, automatic index/view advisor as examples to showcase the superiority of AI-native databases.

Speaker Bio: Guoliang Li is a tenured full Professor of Department of Computer Science, Tsinghua University, Beijing, China. His research interests include AI-native database, big data analytics and mining, crowdsourced data management, big spatio-temporal data analytics, large-scale data cleaning and integration. He has published more than 100 papers in premier conferences and journals, such as SIGMOD, VLDB, ICDE, SIGKDD, SIGIR, TODS, VLDB Journal, and TKDE. He is a PC co-chair of DASFAA 2019, WAIM 2014, WebDB 2014, and NDBC 2016. He servers as associate editor for IEEE Transactions and Data Engineering, VLDB Journal, ACM Transaction on Data Science, IEEE Data Engineering Bulletin. He has regularly served as the (senior) PC members of many premier conferences, such as SIGMOD, VLDB, KDD, ICDE, WWW, IJCAI, and AAAI. His papers have been cited more than 6000 times. He got several best paper awards in top conferences, such as CIKM 2017 best paper award, ICDE 2018 best paper candidate, KDD 2018 best paper candidate, DASFAA 2014 best paper runner-up, APWeb 2014 best paper award, etc. He received VLDB Early Research Contribution Award 2017, IEEE TCDE Early Career Award 2014, The National Youth Talent Support Program 2017, ChangJiang Young Scholar 2016, NSFC Excellent Young Scholars Award 2014, CCF Young Scientist 2014.