Sliding-window top-k queries on uncertain streams

  • Authors:

  • Cheqing Jin;Ke Yi;Lei Chen;Jeffrey Xu Yu;Xuemin Lin

  • Affiliations:

  • East China Normal University, Shanghai, China;The Hong Kong University of Science and Technology, Hong Kong, China;The Hong Kong University of Science and Technology, Hong Kong, China;The Chinese University of Hong Kong, Hong Kong, China;The University of New South Wales and National ICT Australia, Sydney, Australia

  • Venue:
  • The VLDB Journal — The International Journal on Very Large Data Bases
  • Year:
  • 2010

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Abstract

Recently, due to the imprecise nature of the data generated from a variety of streaming applications, such as sensor networks, query processing on uncertain data streams has become an important problem. However, all the existing works on uncertain data streams study unbounded streams. In this paper, we take the first step towards the important and challenging problem of answering sliding-window queries on uncertain data streams, with a focus on one of the most important types of queries--top-k queries. It is nontrivial to find an efficient solution for answering sliding-window top-k queries on uncertain data streams, because challenges not only stem from the strict space and time requirements of processing both arriving and expiring tuples in high-speed streams, but also rise from the exponential blowup in the number of possible worlds induced by the uncertain data model. In this paper, we design a unified framework for processing sliding-window top-k queries on uncertain streams. We show that all the existing top-k definitions in the literature can be plugged into our framework, resulting in several succinct synopses that use space much smaller than the window size, while they are also highly efficient in terms of processing time. We also extend our framework to answering multiple top-k queries. In addition to the theoretical space and time bounds that we prove for these synopses, we present a thorough experimental report to verify their practical efficiency on both synthetic and real data.