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.
All aspects of the submission and notification process will be handled electronically. Submissions must adhere to the paper formatting instructions. Research papers will be judged for quality and relevance through double-blind reviewing, where the identities of the authors are withheld from the reviewers. Author names and affiliations must not appear in the papers, and bibliographic references must be adjusted to preserve author anonymity. Submissions should be uploaded at: https://easychair.org/conferences/?conf=acac2017sigmodchina.
The conference will have two tracks of regular papers, research track and vision track. Submissions to research track should be up to 10 pages, and submissions to vision track should be up to 6 pages. The conference will also have a poster track. Submissions to the poster track should be up to 2 pages. Please note that posters are not required to be anonymous. All submissions should follow the ACM proceedings format, as described in https://www.acm.org/publications/proceedings-template.
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.
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.
Guoliang Li (Tsinghua University)
Hongzhi Wang (Harbin Institute of Technology)
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)