›› 2014, Vol. 29 ›› Issue (5): 849-869.doi: 10.1007/s11390-014-1473-2

Special Issue: Data Management and Data Mining

• Data Management and Data Mining • Previous Articles     Next Articles

Querying Big Data: Bridging Theory and Practice

Wenfei Fan1,2(樊文飞), Fellow, ACM, Jin-Peng Huai2,3(怀进鹏), Fellow, CCF, Member, ACM, IEEE   

  1. 1. School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, U. K. ;
    2. International Research Center on Big Data, Beihang University, Beijing 100191, China;
    3. School of Computer Science and Engineering, Beihang University, Beijing 100191, China
  • Received:2014-02-22 Revised:2014-08-05 Online:2014-09-05 Published:2014-09-05
  • About author:Wenfei Fan is the Chair of Web Data Management in the School of Informatics, University of Edinburgh, UK, and the director of the International Research Center on Big Data, Beihang University, Beijing. He received his Ph.D. degree in computer science from the University of Pennsylvania, USA, and B.S. and M.S. degrees from Peking University, Beijing. Prof. Fan is a fellow of the Royal Society of Edinburgh, UK, a fellow of the ACM, USA, a national professor of the 1 000-Talent Program, and a Yangtze River Scholar, China. His current research interests include database theory and systems, in particular big data, data quality, data fusion, distributed query processing, and social networks.
  • Supported by:

    This work was supported in part by the National Basic Research 973 Program of China under Grant No. 2014CB340302. Fan is also supported in part by the National Natural Science Foundation of China under Grant No. 61133002, the Guangdong Innovative Research Team Program under Grant No. 2011D005 and Shenzhen Peacock Program under Grant No. 1105100030834361, the Engineering and Physical Sciences Research Council of UK under Grant No. EP/J015377/1, and the National Science Foundation of USA under Grant No. III-1302212.

Big data introduces challenges to query answering, from theory to practice. A number of questions arise. What queries are ``tractable'' on big data? How can we make big data ``small'' so that it is feasible to find exact query answers? When exact answers are beyond reach in practice, what approximation theory can help us strike a balance between the quality of approximate query answers and the costs of computing such answers? To get sensible query answers in big data, what else do we necessarily do in addition to coping with the size of the data? This position paper aims to provide an overview of recent advances in the study of querying big data. We propose approaches to tackling these challenging issues, and identify open problems for future research.

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