Symposiums--SIGMOD China


General Co-Chairs:

Jianzhong Li (Harbin Institute of Technology, China)

Guoliang Li (Tsinghua University, China)

Hongzhi Wang (Harbin Institute of Technology, China)

TPC Co-Chairs:

Xike Xie (University of Science and Technology of China, China)

Xin Cao (University of New South Wales, Australia)

Yang Wang (University of Science and Technology of China, China)

Meeting schedule

Date(2021-07-31)Location(Meeting Room No.6)
Time Talks Moderator Speaker
14:00 - 14:10 Openning Remarks Xike Xie, University of Science and Technology of China
14:10 - 15:00 Keynote 1 Methods in Democratizing the Full Data Analytics Software Stack Xike Xie, University of Science and Technology of China X. Sean Wang,  Professor of Fudan University
15:00 - 15:50 Keynote 2 Hetu: A new distributed deep learning framework for huge model Xike Xie, University of Science and Technology of China Bin Cui, Professor of Peking University
15:50 - 16:00 Tea Break
16:00 - 16:30 Young Scientist Speech 1 Human-in-the-loop Data Preparation Xike Xie, University of Science and Technology of China Chengliang Chai, Tsinghua University
16:30 - 17:00 Young Scientist Speech 2 Results on View Propagation Xike Xie, University of Science and Technology of China Dongjing Miao, Harbin Institute of Technology
17:00 - 17:30 Young Scientist Speech 3 Truth discovery from text and tabular data Xike Xie, University of Science and Technology of China Chen Ye, Hangzhou Dianzi University
17:30 - 18:00 Young Scientist Speech 4 Incorporating Personalization into User-Generated Data Modeling Xike Xie, University of Science and Technology of China Wei Zhang, East Chian Normal University
Zoom Link:
20:30 - 21:30 Keynote 3 Trajectory Data Analytics Xin Cao, The University of New South Wales Christian S. Jensen, Professor of Aalborg University

Keynote: Trajectory Data Analytics

Christian S. Jensen

Professor,Aalborg University
ACM Fellow
IEEE Fellow
Members of the Academia Europaea

Abstract:The sweeping digitalization of societal processes generates massive volumes of data that, if harnessed properly, hold the potential to improve the processes. This talk considers the process of vehicular transportation and the use of vehicular trajectory data that captures detailed information about the underlying transportation system. Specifically, the increasing availability of such data holds the potential to enable smarter transportation. The talk covers recent advances in analytics that are capable of exploiting trajectory data to provide new or higher-resolution services related to transportation.

BIO: Christian S. Jensen is Professor of Computer Science at Aalborg University, Denmark. His research concerns analytics and data management, focusing on temporal and spatio-temporal analytics, including machine learning, data mining, and query processing. Christian is an ACM and 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 awards for his research, most recently the 2019 IEEE TCDE Impact Award. He is on the board of Villum Fonden, a major funder of research in Denmark. He is President of the steering committee of the Swiss National Research Program on Big Data. In Germany, he is on the scientific advisory board (SAB) of the Max Planck Institute for Informatics; and in Norway, he chairs the SAB of the Norwegian Research Center for AI Innovation. He recently finished a 6-year term as Editor-in-Chief of ACM TODS.

Keynote:Methods in Democratizing the Full Data Analytics Software Stack

X. Sean Wang

School of computer science and technology, Fudan University
Member of China Computer Federation

Abstract: Data analysis and machine learning is a complex task, involving quite a few software and hardware systems, including data collection systems, data storage and database systems, data mining and machine learning systems, data visualization and interaction systems, cloud computing and supercomputing. A realistic and highly efficient AI application often requires the smooth collaboration among the different systems, which becomes a big technical hurdle, especially to the non-computing professionals, towards their applications. The history of computing may be viewed as a technical democratizing processing, which in turn brings huge benefit to the society and its economy. The democratizing process for data analysis and machine learning has started to show up in various aspects, but it still needs research and development in multiple directions, including human-machine natural interaction, automated system selection and deployment, and automated workflow execution and optimization. It can be expected that this democratizing process will continue, and the research and development efforts by the computer scientists are much needed.

BIO:X. Sean Wang is Professor at the School of Compute Science, Fudan University, a CCF Fellow, ACM Member, and IEEE Senior Member. His research interests include data analytics and data security. He received his PhD degree in Computer Science from the University of Southern California, USA. Before joining Fudan University in 2011 to be the dean of its School of Computer Science and School of Software, he served as the Dorothean Chair Professor in Computer Science at the University of Vermont, USA, and as a Program Director at the National Science Foundation, USA. He has published widely in the general area of databases and information security, and was a recipient of the US National Science Foundation CAREER award. He’s currently chief editor of the Springer Journal of Data Science and Engineering. He’s also currently on the steering committees of the IEEE ICDE and IEEE BigComp conference series, and past Chair of WAIM Steering Committee.

Keynote: Hetu: A new distributed deep learning framework for huge model

Bin Cui

Deputy director of Computer Department of Peking University
Distinguished professor of Changjiang Scholars
Director, Institute of network and information systems
Deputy director of Database Committee of Chinese Computer Federation

Abstract: 机器学习系统是人工智能应用的重要基础,其核心包括数据组织形式、模型计算方法以及硬件使用方式等。日益增长的模型和数据规模对现有系统带来了严峻的挑战。本次报告介绍了课题组自主研发的面向超大模型的自动并行分布式深度学习框架--河图。报告首先介绍了河图的特性和设计理念,剖析了目前“大模型”发展情况,然后重点介绍了河图面向复杂模型和硬件环境的优化进展以及在自动化并行训练上的探索。最后,对机器学习系统的发展进行了展望。河图系统已在GitHub开源。

BIO: 崔斌,北京大学计算机系副主任、长江学者特聘教授,网络与信息系统研究所所长。研究方向包括数据库系统设计和性能优化、数据挖掘、大数据管理和分析等,在相关领域发表了100多篇学术论文。担任中国计算机学会数据库专委会副主任,VLDB理事会理事,IEEE TKDE、VLDB Journal等期刊编委,以及数十个国际会议的程序委员会委员。他是中国计算机学会杰出会员,于2008年获得微软亚洲研究院的“微软青年教授奖”,2009年获得中国计算机学会 “CCF 青年科学家奖”,2014年获教育部自然科学二等奖。