Abstract
Recently, in the area of big data, some popular applications such as web search engines and recommendation systems, face the problem to diversify results during query processing. In this sense, it is both significant and essential to propose methods to deal with big data in order to increase the diversity of the result set. In this paper, we firstly define the diversity of a set and the ability of an element to improve the overall diversity. Based on these definitions, we propose a diversification framework which has good performance in terms of effectiveness and efficiency. Also, this framework has theoretical guarantee on probability of success. Secondly, we design implementation algorithms based on this framework for both numerical and string data. Thirdly, for numerical and string data respectively, we carry out extensive experiments on real data to verify the performance of our proposed framework, and also perform scalability experiments on synthetic data.
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This paper was partially supported by NSFC (Grant Nos. U1509216, U1866602, 61602129) and Microsoft Research Asia.
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Meifan Zhang received the bachelor’s degree in computer science from Harbin Institute of Technology, China in 2014, where she is currently pursuing the PhD degree. Her research interest includes big data analystics, data quality and machine learning.
Hongzhi Wang received the PhD degree in computer science from Harbin Institute of Technology, China in 2008. From 2008 to 2010, he was an assistant professor in Harbin Institute of Technology, China. From 2010 to 2015, he was an associate professor. Since 2015, he has been a professor of Department of Computer Science and Technology, Harbin Institute of Technology, China. His research interest includes big data management, data quality, graph data management, Web data management. Prof. Wang was a recipient of the Microsoft fellowship, the Chinese Excellent database engineer, and the IBM PHD fellowship.
Jianzhong Li received the BS degree from Heilongjiang University, China in 1975. He worked in the University of California at Berkeley as a visiting scholar in 1985. He has also been a visiting professor at the University of Minnesota at Minneapolis, USA, from 1991 to 1992 and from 1998 to 1999. Since 1998, he has been a professor of Department of Computer Science and Technology, Harbin Institute of Technology, China. His current research interests include database management systems, data warehousing and data mining, sensor network, and data intensive super computing. Prof. Li was a recipient of awards and honors, including the Chairman of the ACM SIGMOD China and the Director of the China Computer Federation.
Hong Gao is a professor and doctoral supervisor of Harbin Institute of Technology, China. She received her PhD degree from Harbin Institute of Technology, China. She has long engaged in research work of massive data computation and quality management, wireless sensor networks and graphic data management and computation. She was a recipient of awards and honors, including the Assistant Director of the China Computer Federation Technical Committee on Databases, a member of the China Computer Federation Technical Committee on Sensor Network, and the Deputy Director of the Massive Data Computing Lab.
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Zhang, M., Wang, H., Li, J. et al. Diversification on big data in query processing. Front. Comput. Sci. 14, 144607 (2020). https://doi.org/10.1007/s11704-019-8324-9
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DOI: https://doi.org/10.1007/s11704-019-8324-9