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Data source selection for approximate query

Author

Listed:
  • Hongjie Guo

    (Harbin Institute of Technology)

  • Jianzhong Li

    (Harbin Institute of Technology)

  • Hong Gao

    (Harbin Institute of Technology)

Abstract

Exact query on big data is a challenging task due to the large numbers of autonomous data sources. In this paper, an efficient method is proposed to select sources on big data for approximate query. A gain model is presented for source selection by considering information coverage and quality provided by sources. Under this model, the source selection problem is formalized into two optimization problems. Because of the NP-hardness of proposed problems, two approximate algorithms are devised to solve them respectively, and their approximate ratios and complexities are analyzed. To further improve efficiency, a randomized method is developed for gain estimation. Based on it, the time complexities of improved algorithms are sub-linear in the number of data item. Experimental results show high efficiency and scalability of proposed algorithms.

Suggested Citation

  • Hongjie Guo & Jianzhong Li & Hong Gao, 2022. "Data source selection for approximate query," Journal of Combinatorial Optimization, Springer, vol. 44(4), pages 2443-2459, November.
    • Handle: RePEc:spr:jcomop:v:44:y:2022:i:4:d:10.1007_s10878-021-00760-y
    DOI: 10.1007/s10878-021-00760-y
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    References listed on IDEAS

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    1. Fisher, M.L. & Nemhauser, G.L. & Wolsey, L.A., 1978. "An analysis of approximations for maximizing submodular set functions," LIDAM Reprints CORE 341, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Fisher, M.L. & Nemhauser, G.L. & Wolsey, L.A., 1978. "An analysis of approximations for maximizing submodular set functions - 1," LIDAM Reprints CORE 334, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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