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Continuous ranking on uncertain streams

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Abstract

Data uncertainty widely exists in many web applications, financial applications and sensor networks. Ranking queries that return a number of tuples with maximal ranking scores are important in the field of database management. Most existing work focuses on proposing static solutions for various ranking semantics over uncertain data. Our focus is to handle continuous ranking queries on uncertain data streams: testing each new tuple to output highly-ranked tuples. The main challenge comes from not only the fact that the possible world space will grow exponentially when new tuples arrive, but also the requirement for low space- and time-complexity to adapt to the streaming environments. This paper aims at handling continuous ranking queries on uncertain data streams. We first study how to handle this issue exactly, then we propose a novel method (exponential sampling) to estimate the expected rank of a tuple with high quality. Analysis in theory and detailed experimental reports evaluate the proposed methods.

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Authors and Affiliations

  1. Shanghai Key Laboratory of Trustworthy Computing, Software Engineering Institute, East China Normal University, Shanghai, 200062, China

    Cheqing Jin, Jingwei Zhang & Aoying Zhou

Authors
  1. Cheqing Jin
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  2. Jingwei Zhang
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  3. Aoying Zhou
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Correspondence to Cheqing Jin.

Additional information

Cheqing Jin received his BS and MS in computer science from Zhejiang University, China, in 1999 and 2002, respectively. He received his PhD degree in computer science from Fudan University, China, in 2005. Currently, he is a professor of the Software Engineering Institute, East China Normal University, China. His current research interests include streaming data, uncertain databases, location-based services, and data quality.

Jingwei Zhang received his MS in computer science from Guilin University of Electronic Technology, Guilin, China, in 2004. He is currently a PhD candidate in computer science at East China Normal University, Shanghai. His research interests include web data management and analysis, massive data management, and data stream mining.

Aoying Zhou is a professor and deputy dean of the Software School at East China Normal University, Shanghai, where he also heads the Institute of Massive Computing. He is the winner of the National Science Fund for Distinguished Young Scholars supported by NSFC and also of the professorship appointment under Chang Jiang Scholars Program sponsored by the Ministry of Education. He acts as the vice-director of ACM SIGMOD China and the Database Technology Committee of China Computer Federation. He servs as the associate editor-in-chief of the China Journal of Computer, and member of the editorial boards of some prestigious academic journals, such as the VLDB Journal, www Journal, and FCS. His research interests include Web data management, data management for data-intensive computing, management of uncertain data, data mining and data streams, distributed storage, and P2P computing.

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Jin, C., Zhang, J. & Zhou, A. Continuous ranking on uncertain streams. Front. Comput. Sci. 6, 686–699 (2012). https://doi.org/10.1007/s11704-012-1227-7

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  • DOI: https://doi.org/10.1007/s11704-012-1227-7

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