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Preference-Based Top-k Representative Skyline Queries on Uncertain Databases

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Advances in Knowledge Discovery and Data Mining (PAKDD 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9078))

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Abstract

Top-k representative skyline queries are important for multi-criteria decision making applications since they provide an intuitive way to identify the k most significant objects for data analysts. Despite their importance, top-k representative skyline queries have not received adequate attention from the research community. Existing work addressing the problem focuses only on certain data models. For this reason, in this paper, we present the first study on processing top-k representative skyline queries in uncertain databases, based on user-defined references, regarding the priority of individual dimensions. We also apply the odds ratio to restrict the cardinality of the result set, instead of using a threshold which might be difficult for an end-user to define. We then develop two novel algorithms for answering top-k representative skyline queries on uncertain data. In addition, several pruning conditions are proposed to enhance the efficiency of our proposed algorithms. Performance evaluations are conducted on both real-life and synthetic datasets to demonstrate the efficiency, effectiveness and scalability of our proposed approaches.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Information Technology, La Trobe University, 10 Macarthur Court, Mill Park, VIC, 3082, Australia

    Ha Thanh Huynh Nguyen & Jinli Cao

Authors
  1. Ha Thanh Huynh Nguyen
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  2. Jinli Cao
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Corresponding author

Correspondence to Ha Thanh Huynh Nguyen .

Editor information

Editors and Affiliations

  1. Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam

    Tru Cao

  2. Singapore Management University, Singapore, Singapore

    Ee-Peng Lim

  3. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  4. Japan Advanced Institute of Science and Technology, Nomi City, Japan

    Tu-Bao Ho

  5. The University of Hong Kong, Hong Kong, Hong Kong SAR

    David Cheung

  6. Osaka University, Osaka, Japan

    Hiroshi Motoda

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Nguyen, H.T.H., Cao, J. (2015). Preference-Based Top-k Representative Skyline Queries on Uncertain Databases. In: Cao, T., Lim, EP., Zhou, ZH., Ho, TB., Cheung, D., Motoda, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2015. Lecture Notes in Computer Science(), vol 9078. Springer, Cham. https://doi.org/10.1007/978-3-319-18032-8_22

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  • DOI: https://doi.org/10.1007/978-3-319-18032-8_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18031-1

  • Online ISBN: 978-3-319-18032-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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