Efficient approximate top-k query algorithm using cube index

  • Authors:

  • Dongqu Chen;Guang-Zhong Sun;Neil Zhenqiang Gong

  • Affiliations:

  • Key Laboratory on High Performance Computing, Anhui Province, School of Computer Science and Technology, University of Science and Technology of China;Key Laboratory on High Performance Computing, Anhui Province, School of Computer Science and Technology, University of Science and Technology of China;EECS Department, UC Berkeley and Key Laboratory on High Performance Computing, Anhui Province, School of Computer Science and Technology, University of Science and Technology of China

  • Venue:
  • APWeb'11 Proceedings of the 13th Asia-Pacific web conference on Web technologies and applications
  • Year:
  • 2011

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Abstract

Exact top-k query processing has attracted much attention recently because of its wide use in many research areas. Since missing the truly best answers is inherent and unavoidable due to the user's subjective judgment, and the cost of processing exact top-k queries is highly expensive for datasets with huge volume, it is intriguing to answer approximate top-k query instead. In this paper, we first define a novel kind of approximate top-k query, called µ-approximate top-k query. Then we introduce an efficient index structure, i.e. cube index, based on which, we propose our novel Cube Index Algorithm (CIA). We analyze the complexity of both constructing cube index and CIA algorithm. Moreover, extensive experiments show that CIA performs much better than the well-known approximate TAθ algorithm [3].