<i>k</i>-Dominant Skyline Query Computation in MapReduce Environment

• SIDDIQUE Md. Anisuzzaman

  Hiroshima University

• TIAN Hao

  Hiroshima University

• MORIMOTO Yasuhiko

  Hiroshima University

Abstract

Filtering uninteresting data is important to utilize “big data”. Skyline query is popular technique to filter uninteresting data, in which it selects a set of objects that are not dominated by another from a given large database. However, a skyline query often retrieves too many objects to analyze intensively especially for high-dimensional dataset. To solve the problem, k-dominant skyline queries have been introduced. The size of databases sometimes become too large to compute in a centralized environment. Conventional algorithms for computing k-dominant skyline queries are not well suited for parallel and distributed environments, such as the MapReduce framework. In this paper, we consider an efficient parallel algorithm to process k-dominant skyline query in MapReduce framework. Extensive experiments demonstrate the scalability of proposed algorithm for synthetic big datasets under different settings of data distribution, dimensionality, and cardinality.

Journal

• IEICE Transactions on Information and Systems

  IEICE Transactions on Information and Systems E98.D (5), 1027-1034, 2015

  The Institute of Electronics, Information and Communication Engineers

Keywords

• skyline query
• <i>k</i>-dominant skyline query
• MapReduce
• big data

Details

• CRID: 1390282679354134016
• NII Article ID: 130005067747
• DOI: 10.1587/transinf.2014dap0010
• ISSN: 17451361
• ISSN: 09168532
• Web Site: https://www.jstage.jst.go.jp/article/transinf/E98.D/5/E98.D_2014DAP0010/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed