Progressive Skyline Computation in Database Systems

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Progressive Skyline Computation in Database Systems (2005)

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by Dimitris Papadias , Yufei Tao , Greg Fu , Bernhard Seeger

Venue:	ACM TRANS. DATABASE SYST
Citations:	205 - 13 self

BibTeX

@ARTICLE{Papadias05progressiveskyline,
    author = {Dimitris Papadias and Yufei Tao and Greg Fu and Bernhard Seeger},
    title = {Progressive Skyline Computation in Database Systems},
    journal = {ACM TRANS. DATABASE SYST},
    year = {2005},
    volume = {30},
    number = {1},
    pages = {2005}
}

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Abstract

The skyline of a d-dimensional dataset contains the points that are not dominated by any other point on all dimensions. Skyline computation has recently received considerable attention in the database community, especially for progressive methods that can quickly return the initial results without reading the entire database. All the existing algorithms, however, have some serious shortcomings which limit their applicability in practice. In this article we develop branch-- skyline (BBS), an algorithm based on nearest-neighbor search, which is I/O optimal, that is, it performs a single access only to those nodes that may contain skyline points. BBS is simple to implement and supports all types of progressive processing (e.g., user preferences, arbitrary dimensionality, etc). Furthermore, we propose several interesting variations of skyline computation, and show how BBS can be applied for their efficient processing.

Keyphrases

progressive skyline computation database system skyline computation nearest-neighbor search entire database progressive method progressive processing single access initial result efficient processing branch skyline skyline point user preference database community several interesting variation considerable attention serious shortcoming arbitrary dimensionality d-dimensional dataset

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