NDLI: Efficient computation of trade-off skylines

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Efficient computation of trade-off skylines

Content Provider	ACM Digital Library
Author	Balke, Wolf-Tilo Güntzer, Ulrich Lofi, Christoph
Abstract	When selecting alternatives from large amounts of data, trade-offs play a vital role in everyday decision making. In databases this is primarily reflected by the top-k retrieval paradigm. But recently it has been convincingly argued that it is almost impossible for users to provide meaningful scoring functions for top-k retrieval, subsequently leading to the adoption of the skyline paradigm. Here users just specify the relevant attributes in a query and all suboptimal alternatives are filtered following the Pareto semantics. Up to now the intuitive concept of compensation, however, cannot be used in skyline queries, which also contributes to the often unmanageably large result set sizes. In this paper we discuss an innovative and efficient method for computing skylines allowing the use of qualitative trade-offs. Such trade-offs compare examples from the database on a focused subset of attributes. Thus, users can provide information on how much they are willing to sacrifice to gain an improvement in some other attribute(s). Our contribution is the design of the first skyline algorithm allowing for qualitative compensation across attributes. Moreover, we also provide an novel trade-off representation structure to speed up retrieval. Indeed our experiments show efficient performance allowing for focused skyline sets in practical applications. Moreover, we show that the necessary amount of object comparisons can be sped up by an order of magnitude using our indexing techniques.
Starting Page	597
Ending Page	608
Page Count	12
File Format	PDF
ISBN	9781605589459
DOI	10.1145/1739041.1739112
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-03-22
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Trade-off management Skyline queries Preferences
Content Type	Text
Resource Type	Article

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