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ACM TURC 2017 (SIGMOD China)

The ACM TURC 2017 (SIGMOD China) conference is a new leading international forum for database researchers, practitioners, developers, and users to explore cutting-edge ideas and results, and to exchange techniques, tools, and experiences. We invite the submission of original research contributions relating to all aspects of data management defined broadly, and particularly encourage submissions on topics of emerging interest in the research and development communities.

Previous SIGMOD China Call for Papers and Posters.


2017-05-13 (Day 1):  SIGMOD

Keynote Speech 1


Christian S. Jensen (Aalborg University, Denmark)
Data-Intensive Routing in Spatial Networks

Keynote Speech 2


Xuemin Lin (University of New South Wales, Australia)
Towards Processing of Big Graphs

Tea Break

Keynote Speech 3


Jianzhong Li (Harbin Institute of Technology)

Big Data Theory and Algorithms



Panelists: Yaxiao Liu (Senior Vice President of UCAR Inc.), Bohua Wen (President of Continuum China), Yifan Li (CEO of HesaiTech)

Big Data Powers Intelligent Driving


2017-05-14 (Day 2):  SIGMOD

Research Session 1


Yun Ma, Qing Li, Zhenguo Yang, Wenyin Liu and Antoni Chan

Learning Word Embeddings via Context Grouping


Chuan Li, Zhiheng Jiang, Yijie Li, Yangfan Miao, Daiyan Hu and Guangming Liu

NANI: An Efficient Community Detection Algorithm Based on Nested Aggregation of Node Influences


Haiquan Wang, Yaoqiang Xu, Kai Wang and Jianhua Feng

An APCA-Enhanced Compression and Query Method on Large-scale Time Series Data


Xiaoou Ding, Hongzhi Wang, Yitong Gao, Jianzhong Li and Hong Gao

Determining the Currency of Dynamic Data


Bing Xiao, Jinwei Guo, Weining Qian, Huiqi Hu and Aoying Zhou

NIOSIT: Efficient Data Access for Log-Structured Merge-Tree Style Storage Systems

Tea Break

Research Session 2


Yaoqiang Xu, Yongqing Xie and Jianhua Feng

Correlation Analysis between Electricity Consumption and Economic Development


Amina Belhasna and Hongzhi Wang

Distributed Skyline Trajectory Query Processing


Ning Yin, Hongyan Li and Hanchen Su

CLR: Coupled Logistic Regression Model for CTR Prediction


Huiying Wang and Jianhua Feng

FlashSkipList: Indexing on Flash Devices



Christian S. Jensen (Aalborg University, Denmark)

Editor-in-Chief of ACM Trans. on Database Systems

Title: Data-Intensive Routing in Spatial Networks

Abstract: Aspects of our lives are increasingly being captured digitally. In particular, data is increasingly available that enables us to capture the states of important societal processes at an unprecedented level of detail, in turn enabling us to better understand and improve the processes. Road transportation is one such process. The proliferation of data, most notably vehicle trajectory data, enables the accurate capture of the time-varying state of the traffic in road networks. The speaker argues that when the underlying data is in the form of trajectories, the traditional vehicle routing paradigm, where a road network is modeled as a graph and weights such as travel times are assigned to edges, should be replaced by a new data-intensive paradigm, where weights are associated with arbitrary graph paths. This setting presents new challenges and opportunities to routing. Further, the proliferation of data enables higher-resolution routing, including personalized routing.

Bio: Christian S. Jensen is Obel Professor of Computer Science at Aalborg University, Denmark, and he was recently with Aarhus University for three years and spent a one-year sabbatical at Google Inc., Mountain View. His research concerns data management and data-intensive systems, and its focus is on temporal and spatio-temporal data management. 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. He is Editor-in-Chief of ACM Transactions on Database Systems.



Xuemin Lin (University of New South Wales, Australia)

IEEE Fellow
Editor-in-Chief of IEEE Trans. on Knowledge and
Data Engineering

Title: Towards Processing of Big Graphs

Abstract: Graphs are very important parts of Big Data and widely used for modelling complex structured data with a broad spectrum of applications such as bioinformatics, web search, social network, road network, etc.  Over the last decade, tremendous research efforts have been devoted to many fundamental problems in managing and analysing graph data.  In this talk, I will present some of our recent research efforts in processing big graphs including scalable processing theory and techniques, distributed computation, and system framework.

Bio: Xuemin Lin is a UNSW Scientia Professor and the head of database group in the school of computer science and engineering at UNSW, Australia.  Xuemin's research interests lie in databases, algorithms, and complexities. Specifically, he is working in the area of scalable data processing covering graph data, spatial-temporal data, streaming data, uncertain data, text data, etc. Xuemin was an associate editor of ACM TODS (2008-2014), IEEE TKDE (Feb 2013- Jan 2015),  and an associate editor-in-Chief of IEEE TKDE (2015-2016). He is currently the Editor-in-Chief  of IEEE TKDE (2017 Jan – Now).  Xuemin has published over 130 papers in the top venues such as SIGMOD, SIGIR, SIGKDD, ACM MM, VLDB, PODS, ICDE, IJAIC, IEEE TKDE, VLDB J, and ACM TODS.  Xuemin co-authored 16 best papers in the international conferences, including the best paper award in ICDE2016 and best student paper award in ICDE2007. Xuemin Lin was selected as one of the National Thousand Distinguished Overseas Scholars in China in 2010. He is an IEEE Fellow.



Jianzhong Li (Harbin Institute of Technology)

Chairman of ACM SIGMOD China
The winner of Outstanding Young Investigator Award and “WangXuan Award”

Title: Data Usability: An Aspect of Big Data Research

Abstract: The rapid development of information technology gives rise to the big data era. Big data has become an important wealth of information society, and has provided unprecedented rich information for people to further perceive, understand and control the physical world. However, with the growth of the data scale, dirty data comes along. Dirty data leads to the low usability of big data, and seriously harms the information society. In recent years, the data usability problems have aroused the attentions of both the academia and industry. In-depth studies have been conducted, and a series of research results have been provided. This talk introduces the concept of data usability, discusses the challenges and research issues of data usability, reviews the research results on data usability, as well as exploring the future research direction of data usability.

Bio: Jianzhong Li, Professor, PHD supervisor, the winner of Outstanding Young Investigator Award and “WangXuan Award”, CCF the highest achievement award, the chief scientist of National 973 Project. He is the executive director and fellow of CCF, the director of China Computer Federation Technical Committee on Internet of Things, the vice director of Chinese Association of Automation Technical Committee on Big Data, the Chairman of ACM SIGMOD China, the chief editor of Data Science and Engineering. He was the vice director of China Computer Federation Technical Committee on Big Data, the vice director of China Computer Federation Technical Committee on Database, and the vice chief editor of IEEE Transactions on Knowledge and Data Engineering, a top international journal. His research focuses on massive data computing and wireless sensor network. As a PI, he was supported by many national key projects. He solved many scientific and technical problems, and gained a series of achievement. He has published 4 monographs, 300 papers on refereed journals and conferences, among which more than 100 papers was published on top journals or conferences such as IEEE Trans and SIGMOD. His papers were cited by more than 15000 times by other researchers. Single paper was cited by more than 2000 times by other researchers. His multiple papers were awarded best papers by multiple important conferences such as VLDB. He is the first Chinese Mainland Researcher who has published papers on top database conferences such as VLDB and ICDE. His papers were listed in monographs and handbooks published in UK and USA, as well as graduated course in USA universities. He also developed the operation system for DJS-100 series computer, the first computer cluster system and the first database for computer cluster in China. These systems have been applied in many areas and achieve great economics and social benefits. He has gained national and provincial awards for more than 10 times. He was also serve as Program Committee Chair, General Chair and Program Committee member on major conferences such ICDE, CIKM for more than 30 times.




PC Co-Chairs:
Guoliang Li (Tsinghua University)
Hongzhi Wang (Harbin Institute of Technology)

PC Members:
Gang Chen (Zhejiang University)
Lei Chen (HKUST)
Shimin Chen (ICT CAS)
Qun Chen (NWPU)
Yunjun Gao (Zhejiang University)
Jun Gao (Peking University)
Zhenying He (Fudan University)
Zhixu Li (Soochow University)
Cuiping Li (Renmin University)
Chuan Li (Sichuan University)
Qing Li (City University of Hong Kong)
Hailong Liu (NWPU)
Shuai Ma (Beihang University)
Rui Mao (Shenzhen University)
Yuwei Peng (Wuhan University)
Shaojie Qiao (Southwest Jiaotong University)
Ryan U (University of Macau)
Chaokun Wang (Tsinghua University)
Xiaoling Wang (ECNU)
Peng Wang (Fudan University)
Ying Yan (MSRA)
Xiaochun Yang (Northeast University)
Yajun Yang (Tianjin University)
Ye Yuan (Northeast University)
Yuanyuan Zhu (Wuhan University)
Zhaonian Zou (HIT)