Efficient Methods for Aggregate Reverse Rank Queries

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著者

    • DONG Yuyang
    • Department of Computer Science, Graduate School of Systems and Information Engineering, University of Tsukuba
    • CHEN Hanxiong
    • Department of Computer Science, Graduate School of Systems and Information Engineering, University of Tsukuba
    • FURUSE Kazutaka
    • Department of Computer Science, Graduate School of Systems and Information Engineering, University of Tsukuba

抄録

Given two data sets of user preferences and product attributes in addition to a set of query products, the aggregate reverse rank (ARR) query returns top-k users who regard the given query products as the highest aggregate rank than other users. ARR queries are designed to focus on product bundling in marketing. Manufacturers are mostly willing to bundle several products together for the purpose of maximizing benefits or inventory liquidation. This naturally leads to an increase in data on users and products. Thus, the problem of efficiently processing ARR queries become a big issue. In this paper, we reveal two limitations of the state-of-the-art solution to ARR query; that is, (a) It has poor efficiency when the distribution of the query set is dispersive. (b) It has to process a large portion user data. To address these limitations, we develop a cluster-and-process method and a sophisticated indexing strategy. From the theoretical analysis of the results and experimental comparisons, we conclude that our proposals have superior performance.

<p>Given two data sets of user preferences and product attributes in addition to a set of query products, the aggregate reverse rank (ARR) query returns top-k users who regard the given query products as the highest aggregate rank than other users. ARR queries are designed to focus on product bundling in marketing. Manufacturers are mostly willing to bundle several products together for the purpose of maximizing benefits or inventory liquidation. This naturally leads to an increase in data on users and products. Thus, the problem of efficiently processing ARR queries become a big issue. In this paper, we reveal two limitations of the state-of-the-art solution to ARR query; that is, (a) It has poor efficiency when the distribution of the query set is dispersive. (b) It has to process a large portion user data. To address these limitations, we develop a cluster-and-process method and a sophisticated indexing strategy. From the theoretical analysis of the results and experimental comparisons, we conclude that our proposals have superior performance.</p>

収録刊行物

  • IEICE Transactions on Information and Systems

    IEICE Transactions on Information and Systems E101.D(4), 1012-1020, 2018

    電子情報通信学会

各種コード

  • NII論文ID(NAID)
    130006602373
  • NII書誌ID(NCID)
    AA10826272
  • 本文言語コード
    ENG
  • 資料種別
    Journal Article
  • ISSN
    0916-8532
  • データ提供元
    IR  J-STAGE 
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