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Taking the Big Picture: Representative Skylines based on Significance and Diversity
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science. (UDBL)
2014 (English)In: The VLDB journal, ISSN 1066-8888, E-ISSN 0949-877X, Vol. 23, no 5, 795-815 p.Article in journal (Refereed) Published
Abstract [en]

The skyline is a popular operator to extract records from a database when a record scoring function is not available. However, the result of a skyline query can be very large. The problem addressed in this paper is the automatic selection of a small number of representative skyline records. Existing approaches have only focused on partial aspects of this problem. Some try to identify sets of diverse records giving an overall approximation of the skyline. These techniques, however, are sensitive to the scaling of attributes or to the insertion of non-skyline records into the database. Others exploit some knowledge of the record scoring function to identify the most significant record, but not sets of records representative of the whole skyline. In this paper, we introduce a novel approach taking both the significance of all the records and their diversity into account, adapting to available knowledge of the scoring function, but also working under complete ignorance. We show the intractability of the problem and present approximate algorithms. We experimentally show that our approach is efficient, scalable and that it improves existing works in terms of the significance and diversity of the results.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2014. Vol. 23, no 5, 795-815 p.
National Category
Computer Science
URN: urn:nbn:se:uu:diva-218371DOI: 10.1007/s00778-014-0352-3ISI: 000342339400006OAI: oai:DiVA.org:uu-218371DiVA: diva2:695512
Available from: 2014-02-11 Created: 2014-02-11 Last updated: 2014-10-31Bibliographically approved

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Magnani, Matteo
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