Measuring the Sky: On computing data cubes via skylining the measures

Man Lung Yiu, Eric Lo, Duncan Yung

Research output: Journal article publicationJournal articleAcademic researchpeer-review

13 Citations (Scopus)


Data cube is a key element in supporting fast OLAP. Traditionally, an aggregate function is used to compute the values in data cubes. In this paper, we extend the notion of data cubes with a new perspective. Instead of using an aggregate function, we propose to build data cubes using the skyline operation as the aggregate function. Data cubes built in this way are called group-by skyline cubes and can support a variety of analytical tasks. Nevertheless, there are several challenges in implementing group-by skyline cubes in data warehouses: 1) the skyline operation is computational intensive, 2) the skyline operation is holistic, and 3) a group-by skyline cube contains both grouping and skyline dimensions, rendering it infeasible to precompute all cuboids in advance. This paper gives details on how to store, materialize, and query such cubes.
Original languageEnglish
Article number5677514
Pages (from-to)492-505
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number3
Publication statusPublished - 6 Feb 2012


  • data warehouse and repository
  • Query processing

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics


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