9:10 - 10:00 Keynote 1
The Data Landscape: Trends and Directions
Speaker: C. Mohan (Distinguished Professor of Science at Hong Kong Baptist University, Distinguished Visiting Professor at Tsinghua University, Retired IBM Fellow at IBM Research)
Abstract: 50 years ago, the ACM SIGMOD and VLDB annual conferences began. It has been about four decades since the commercial emergence of relational database management systems happened. Starting from around that time, I began working on various database management topics at the birthplace of the relational model and the SQL language, until my retirement 4 years ago as an IBM Fellow at IBM Research in Silicon Valley. As someone who has been, and who continues to be, active as an individual contributor in the database area for over four and a half decades, in this talk, I will give a broad overview of the evolution of the data landscape. I will discuss not only research topics but also the trends in the commercial and standards arenas. I will also present market share numbers from data analysts. I intend for my observations to be of value to not only deeply technical people in the research and product spaces but also to management and administration type people who are responsible for data related topics.
Bio: Dr. C. Mohan is currently a Distinguished Professor of Science at Hong Kong Baptist University, a Distinguished Visiting Professor at Tsinghua University in China, a member of the inaugural Board of Governors of Digital University Kerala, and an Advisor of the Kerala Blockchain Academy (KBA) and the Tamil Nadu e-Governance Agency (TNeGA) in India. He retired in June 2020 from being an IBM Fellow at the IBM Almaden Research Center in Silicon Valley. He joined IBM Research (San Jose, California) in 1981 where he worked until May 2006 on several topics in the areas of database, workflow, and transaction management. From June 2006, he worked as the IBM India Chief Scientist, based in Bangalore, with responsibilities that relate to serving as the executive technical leader of IBM India within and outside IBM. In February 2009, at the end of his India assignment, Mohan resumed his research activities at IBM Almaden. Mohan is the primary inventor of the well-known ARIES family of database recovery and concurrency control methods, and the industry-standard Presumed Abort commit protocol. He was named an IBM Fellow, IBM's highest technical position, in 1997 for being recognized worldwide as a leading innovator in transaction management. In 2009, he was elected to the United States National Academy of Engineering (NAE) and the Indian National Academy of Engineering (INAE). He received the 1996 ACM SIGMOD Edgar F. Codd Innovations Award in recognition of his innovative contributions to the development and use of database systems. In 2002, he was named an ACM Fellow and an IEEE Fellow. At the 1999 International Conference on Very Large Data Bases (VLDB), he was honored with the 10 Year Best Paper Award for the widespread commercial, academic and research impact of his ARIES work, which has been extensively covered in textbooks and university courses. From IBM, Mohan received 2 Corporate and 8 Outstanding Innovation/Technical Achievement Awards. He is an inventor on 50 patents. He was named an IBM Master Inventor in 1997. Mohan worked very closely with numerous IBM product and research groups, and his research results are implemented in numerous IBM and non-IBM prototypes and products like DB2, MQSeries, WebSphere, Informix, Cloudscape, Lotus Notes, Microsoft SQLServer, Sybase and System Z Parallel Sysplex. During the last many years, he focused on Blockchain, AI, Big Data and Cloud technologies (https://bit.ly/sigBcP, https://bit.ly/CMoTalks). Since 2017, he has been an evangelist of permissioned blockchains and the myth buster of permissionless blockchains. During 1H2021, Mohan was the Shaw Visiting Professor at the National University of Singapore (NUS) where he taught a seminar course on distributed data and computing. In 2019, he became an Honorary Advisor to TNeGA of Chennai for its blockchain and other projects. In 2020, he joined the Advisory Board of KBA of India. Since 2016, he has been a Distinguished Visiting Professor of China’s prestigious Tsinghua University in Beijing. In 2023, he was named Distinguished Professor of Science of Hong Kong Baptist University. In 2021, he was inducted as a member of the inaugural Board of Governors of the new Indian university Digital University Kerala (DUK). Mohan launched his consulting career by becoming a Consultant to Microsoft's Data Team in October 2020. From March-December 2022, he was a non-employee consultant at Google with the title of Visiting Researcher. He has been on the advisory board of IEEE Spectrum and has been an editor of VLDB Journal, and the journal Distributed and Parallel Databases. In the past, he has been a member of the IBM Academy of Technology’s Leadership Team, IBM's Research Management Council, IBM's Technical Leadership Team, IBM India's Senior Leadership Team, the Bharti Technical Advisory Council, the Academic Senate of the International Institute of Information Technology in Bangalore, and the Steering Council of IBM's Software Group Architecture Board. Mohan received his PhD in computer science from the University of Texas at Austin in 1981. In 2003, he was named a Distinguished Alumnus of IIT Madras from which he received a B.Tech. in chemical engineering in 1977. Mohan is a frequent speaker in North America, Europe and Asia. He has given talks in 43 countries. He is highly active on social media and has a huge following. More information can be found in the Wikipedia page at https://bit.ly/CMwIkP and his homepage at https://bit.ly/CMoDUK.
10:20 - 11:10 Keynote 2
The Complexity Theory of Big Data Computing
Speaker: Jianzhong Li (Professor at Harbin Institute of Technology, Chair Professor at the School of Computer Science and Control Engineering, Shenzhen University of Advanced Technology)
Abstract: With the development of information technology, big data is becoming an important human resource. Big data has great value, and will greatly promote social progress. Big data has become an important research area. This talk discusses fundamental research issues of big data, in particular the theoretical complexity of big data computing. The talk will present the challenge of big data to traditional computer science theory, raise fundamental theoretical research issues for big data computing, and finally give an overview of the research progress and research results in the complexity theory of big data computing, which are achieved by the research group led by the speaker of this talk.
Bio: Jianzhong Li, doctoral supervisor, recipient of the National Outstanding Youth Fund, Chief Scientist of the National 973 Project, Professor at Harbin Institute of Technology, and Chair Professor at the School of Computer Science and Control Engineering, Shenzhen University of Technology. He has served as the Deputy Director of the Academic Committee of the Chinese Computer Society, Director of the Internet of Things Professional Committee, Director of the Sensor Network Professional Committee, Deputy Director of the Database Professional Committee, Deputy Director of the Big Data Expert Committee, Deputy Director of the Big Data Professional Committee of the Chinese Society of Automation, Chairman of ACM SIGMOD China, associate editors of IEEE Transactions on Knowledge and Data Engineering and other international important academic journals. He has also served as the Chairman of the Guidance Committee, Conference Chairman, and Program Committee Chairman of more than 50 international top and important academic conferences. Professor Jianzhong Li has completed more than 30 research projects, such as National Outstanding Youth Fund project, National 973 projects, NSFC key projects, National 863 projects, and NSFC general projects. He has achieved a series of high quality research results in the complexity theory of big data computing, design methods of big data algorithms, efficient algorithms for solving big data problems, wireless sensor networks, data usability, graph data computing, etc. He has published more than 200 academic papers in top international journals and conferences, and have been cited more than 20000 times. His H-index is 62. Many papers have won the Best Paper Award at international top and important academic conferences, the Excellent Journal Paper Award of the National Association for Science and Technology, and many papers have been included in academic works, manuals, and computer graduate courses published in the United States and the United Kingdom. He has been listed in the "Top 2% Global Scientists List" released by Stanford University multiple times. He also applied basic research results to develop many hardware and software systems that have been used in many applications and achieved good economic and social benefits. He has won the second prize of National Science and Technology Progress Award, first prize of Provincial Natural Science Award, first prize of Provincial Science and Technology Progress Award, and more than 10 other awards. He has also been awarded multiple honorary titles such as National May Day Labor Medal, Returned overseas scholars and students who have made outstanding contributions, CCF Wangxuan Award.
11:10 - 12:00 Keynote 3
High-Performance Graph Data Systems: Lessons Learned and Future Directions
Speaker: Bingsheng He (Professor and Vice-Dean (Research) at National University of Singapore)
Abstract: Graph data structures are fundamental to numerous data processing and learning applications. Over the past decades, the size, diversity, and complexity of graph data have grown significantly, presenting a wide array of challenges from processing to learning. This has spurred extensive research in the field, with performance emerging as a critical factor. Throughout this period, we have developed a variety of graph data systems, ranging from parallel graph processing systems on heterogeneous hardware like GPU, FPGA and new architectures to applications in cryptocurrency and e-commerce. In this talk, I will share our journey in building high-performance graph data systems, summarize the lessons we have learned, and outline future directions for this field. More details about our research can be found at http://www.comp.nus.edu.sg/~hebs/.
Bio: Dr. Bingsheng He is currently a Professor and Vice-Dean (Research) at School of Computing, National University of Singapore. Before that, he was a faculty member in Nanyang Technological University, Singapore (2010-2016), and held a research position in the System Research group of Microsoft Research Asia (2008-2010), where his major research was building high performance cloud computing systems for Microsoft. He got the Bachelor degree in Shanghai Jiao Tong University (1999-2003), and the Ph.D. degree in Hong Kong University of Science & Technology (2003-2008). His current research interests include cloud computing, database systems and high performance computing. He has been a winner for industry faculty awards from Microsoft/NVIDIA/Xilinx/Alibaba. His work also won multiple recognitions as “Best papers” collection or awards in top forums such as SIGMOD 2008, VLDB 2013 (demo), IEEE/ACM ICCAD 2017, PACT 2018, IEEE TPDS 2019, FPGA 2021 and VLDB 2023 (industry). Since 2010, he has (co-)chaired a number of international conferences and workshops, including IEEE CloudCom 2014/2015, BigData Congress 2018, ICDCS 2020 and ICDE 2024. He has served in editor board of international journals, including IEEE Transactions on Cloud Computing (IEEE TCC), IEEE Transactions on Parallel and Distributed Systems (IEEE TPDS), IEEE Transactions on Knowledge and Data Engineering (TKDE), Springer Journal of Distributed and Parallel Databases (DAPD) and ACM Computing Surveys (CSUR). He is an ACM Distinguished member (class of 2020).
9:00 - 9:50 Keynote 4
Decentralized Computing and AI Infrastructure for Web3
Speaker: Jiannong Cao (Otto Poon Charitable Foundation Professor in Data Science, the Chair Professor of Distributed and Mobile Computing and the director of Internet and Mobile Computing Lab at the Hong Kong Polytechnic University)
Abstract: Web 3.0 is built on decentralized computing and data infrastructures leveraging blockchain, smart contracts, virtual assets and other advanced technologies and applications. This talk introduces our recent research and development of decentralized computing and AI infrastructure poised to revolutionize the way we develop, deploy, and utilize large AI models. I will present a collaborative edge intelligence (CEAI) platform designed to harness the power of decentralized computing with a programming model to facilitate the development and deployment of edge-native contemporary AI applications. The web3 platform connects users, model developers, and device vendors of large AI models, forming an ecosystem. By decentralizing the computational resources required for large AI models, we enable more efficient, scalable, and cost-effective AI solutions. This ecosystem not only democratizes access to high-performance computing but also fosters innovation and collaboration across the AI community. I will also discuss the transformative impact of a web3-based open computing and AI ecosystem, highlighting real-world applications and future directions.
Bio: Dr. Cao is the Otto Poon Charitable Foundation Professor in Data Science, the Chair Professor of Distributed and Mobile Computing and the director of Internet and Mobile Computing Lab in the Department of Computing at the Hong Kong Polytechnic University. He served the department head from 2011 to 2017. Dr. Cao is also the Dean of Graduate School, director of Research Institute for AIoT, and director of Research Facility in Big Data Analytics at Hong Kong Polytechnic University.
Dr. Cao’s research interests include edge computing and distributed systems, wireless sensing and networking, big data and AI. He published 6 co-authored and 9 co-edited books, and over 500 papers in major international journals and conference proceedings. He also obtained 13 patents. He received many awards for his outstanding research achievements. Dr. Cao served the Chair of the Technical Committee on Distributed Computing of IEEE Computer Society 2012-2014. He is a member of Academia Europaea, a fellow of HK Academy of Engineering Sciences, a fellow of IEEE, a fellow of CCF and a distinguished member of ACM. In 2017, he received the Overseas Outstanding Contribution Award from China Computer Federation.
9:50 - 10:40 Keynote 5
Data Science for Deep Learning
Speaker:Lei Chen (Chair Professor in the Data Science and Analytic Thrust at HKUST (GZ))
Abstract: Deep learning (DL) has made significant progress and found wide application in various fields, like LLM for question answering. However, the success and efficiency of DL models depend on proper data management. Training deep learning-based image classifiers is challenging without labeled data, and efficiency is hindered by large datasets, complex models, and numerous hyperparameters. Lack of validation and explanation limits model applicability. In this talk, I will discuss three crucial issues in data management for deep learning: 1) effective data preparation for DL, including data selection; 2) DL training optimization, involving computation graph optimization; and 3) the importance of model explanation for robustness and transparency. I will conclude by highlighting future research directions.
Bio: Lei Chen, is a chair professor in the data science and analytic thrust at HKUST (GZ), Fellow of the IEEE, and a Distinguished Member of the ACM. Currently, Prof. Chen serves as the dean of information hub, the director of Big Data Institute at HKUST (GZ). Prof. Chen’s research interests include Data-driven AI, Big Data Analytics, Metaverse, knowledge graphs, blockchains, data privacy, crowdsourcing, spatial and temporal databases and probabilistic databases. He received his BS degree in computer science and engineering from Tianjin University, Tianjin, China, MA degree from Asian Institute of Technology, Bangkok, Thailand, and PhD in computer science from the University of Waterloo, Canada. Prof. Chen received the SIGMOD Test-of-Time Award in 2015, Best research paper award in VLDB 2022, .The system developed by Prof. Chen’s team won the excellent demonstration award in VLDB 2014. Prof. Chen had served as VLDB 2019 PC Co-chair. Currently, Prof. Chen serves as Editor-in-chief of IEEE Transaction on Data and Knowledge Engineering and General Co-Chair of VLDB 2024.