Guoliang Li is a full professor and the deputy head of Department of Computer Science, Tsinghua University, Beijing, China. His research interests include database systems, machine learning for database, and database for machine learning. He is a general co-chair of SIGMOD 2021, demo co-chair of VLDB 2021, industry co-chair of ICDE 2022, and PC co-chair of DASFAA 2019. He is also an associate editor of VLDB journal and IEEE TKDE. He is a steering committee member of IEEE TCDE and DASFAA. He received best paper awards (candidates) of VLDB 2020, ICDE 2018, KDD 2018, CIKM 2017 and DASFAA 2014. He received Early Research Contribution Award of VLDB and Early Career Award of IEEE TCDE.

• Keynote Speech Abstract (Click)

  Speech Title: openGauss: An Autonomous Database System

  Abstract: In this talk, I will present how to build an autonomous database system. I discuss how to integrate effective learning-based models into database systems to build learned optimizers (including learned query rewrite, learned cost/cardinality estimation, learned join order selection and physical operator selection) and learned database advisors (including self-monitoring, self-diagnosis, self-configuration, and self- optimization). I also propose an effective validation model to validate the effectiveness of learned models. I discuss effective training data management and model management platforms to easily deploy learned models. Finally I will introduce our autonomous database system openGauss.

Geoff Webb is a leading data scientist. He is Research Director of the Monash University Data Futures Institute and a Technical Advisor to data science startups FROOMLE and BigML Inc. The latter have incorporated his best of class association discovery software, Magnum Opus, as a core component of their advanced Machine Learning service. He developed many of the key mechanisms of support-confidence association discovery in the late 1980s. His OPUS search algorithm remains the state-of-the-art in rule search. He pioneered multiple research areas as diverse as black-box user modelling, interactive data analytics and statistically-sound pattern discovery. He has developed many useful machine learning algorithms that are widely deployed. He has applied machine learning in a broad range of applications including manufacturing, finance, medicine, biology and geoscience. He was editor in chief of the premier data mining journal, Data Mining and Knowledge Discovery from 2005 to 2014. He has been Program Committee Chair of the two top data mining conferences, ACM SIGKDD and IEEE ICDM, as well as General Chair of ICDM. He is an IEEE Fellow. His many awards include the prestigious inaugural Australian Museum Eureka Prize for Excellence in Data Science. His 250+ academic papers have received over 14,000 citations with an h-index of 60. Seven of his recent papers have been recognised as Clarivate Web of Science High Cite papers (top 1% of citations for the discipline).

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  Speech Title: Recent Advances in Assessing Time Series Similarity Through Dynamic Time Warping

  Abstract: Time series are a ubiquitous data type that capture information as it evolves over time. Dynamic Time Warping is the classic technique for assessing degrees of similarity between time series. This talk outlines our impactful program of research that has transformed the state of the art in practical application of Dynamic Time Warping to big data tasks, including fast and effective lower bounds, fast dynamic programming methods for calculating Dynamic Time Warping, and an intuitive and effective variant of Dynamic Time Warping that moderates its sometimes-excessive flexibility.

Ling Liu is a full professor in the School of Computer Science at Georgia Institute of Technology. She directs the research programs in the Distributed Data Intensive Systems Lab (DiSL), examining various aspects of big data systems and analytics. Prof. Liu is an elected IEEE Fellow, a recipient of IEEE Computer Society Technical Achievement Award (2012), and a recipient of the best paper award from numerous top venues, including IEEE ICDCS, WWW, ACM/IEEE CCGrid, IEEE Cloud, IEEE ICWS. Prof. Liu served on editorial board of over a dozen international journals, including the editor in chief of IEEE Transactions on Service Computing (2013-2016). Prof. Liu is currently the editor in chief of ACM Transactions on Internet Computing (since 2019). Her current research is primarily supported by National Science Foundation, CISCO and IBM.

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  Speech Title: Data Systems in the New World of Device-Edge-Cloud Computing

  Abstract: The rapid growth of wireless mobile broadband communication networks has fueled new capabilities in scalable device-to-edge-to-cloud continuum, ranging from increased data rates of 1~10 Gbps, ultra-low latencies of 1ms or less, larger coverage with massive number of devices connected 24x7. These advances have enabled exciting new edge native applications, such as Augmented Reality/Virtual Reality (AR/VR) and video analytics. However, unlike Clouds, edge clients have little elasticity in computing and communication resources, are intermittently connected to the Internet, inherently heterogeneous in computing resource, and more exposed to privacy and security violations. In this keynote, I will use edge video analytics and federated learning as two emerging and complimentary distributed learning paradigms in navigating this device-edge-cloud continuum, while considering resilience, privacy, and multi-tenancy of shared and heterogeneous resources. I will describe alternative distributed learning architectures and optimization strategies, enabling edge system adaptability and robustness, while preserving good application fidelity (level of accuracy).

Jianliang Xu is the Head and Professor of the Department of Computer Science at Hong Kong Baptist University, where he leads the Database Research Group. He received his BEng degree from Zhejiang University and his PhD degree from Hong Kong University of Science and Technology. His current research interests include big data management, data security & privacy, and blockchain technology. With an h-index of 53, he has published more than 250 technical papers in these areas, most of which appeared in leading journals and conferences including SIGMOD, PVLDB, ICDE, TKDE, and VLDBJ. He is listed among the world's top 2% of the most-cited scientists by Stanford University. He has served as a conference co-chair for a number of international conferences and an Associate Editor for several top-tier international journals including IEEE Transactions on Knowledge & Data Engineering (TKDE, 2014-2020) and Proceedings of the VLDB Endowment (PVLDB, 2023-2024). More details can be found at: https://www.comp.hkbu.edu.hk/~xujl/.

• Keynote Speech Abstract (Click)

  Speech Title: Towards Searchable Blockchain Systems

  Abstract: Blockchain technology has gained wide attention due to the boom of Web3, DeFi, and other decentralized applications. With various applications built on top of blockchains, there has been an increasing demand of searching the data stored in blockchains. To ensure query integrity, users could maintain the entire blockchain database and search the data locally. However, this approach is not economic, if not infeasible, because of the blockchain's large storage overhead and considerable maintenance cost. In this talk, we will present some of our recent efforts towards developing searchable blockchain systems in support of efficient query processing with integrity assurance.