Statistically-driven generation of multidimensional analytical schemas from linked data
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comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/8634
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Title
Statistically-driven generation of multidimensional analytical schemas from linked dataDate
2016Publisher
ElsevierISSN
0950-7051; 1872-7409Bibliographic citation
NEBOT, Victoria; BERLANGA, Rafael. Statistically-driven generation of multidimensional analytical schemas from linked data. Knowledge-Based Systems, 2016, vol. 110, p. 15-29.Type
info:eu-repo/semantics/articlePublisher version
http://www.sciencedirect.com/science/article/pii/S0950705116302143Version
info:eu-repo/semantics/submittedVersionSubject
Abstract
The ever-increasing Linked Data (LD) initiative has given place to open, large amounts of semi-structured and rich data published on the Web. However, effective analytical tools that aid the user in his/her analysis ... [+]
The ever-increasing Linked Data (LD) initiative has given place to open, large amounts of semi-structured and rich data published on the Web. However, effective analytical tools that aid the user in his/her analysis and go beyond browsing and querying are still lacking. To address this issue, we propose the automatic generation of multidimensional analytical stars (MDAS). The success of the multidimensional (MD) model for data analysis has been in great part due to its simplicity. Therefore, in this paper we aim at automatically discovering MD conceptual patterns that summarize LD. These patterns resemble the MD star schema typical of relational data warehousing. The underlying foundations of our method is a statistical framework that takes into account both concept and instance data. We present an implementation that makes use of the statistical framework to generate the MDAS. We have performed several experiments that assess and validate the statistical approach with two well-known and large LD sets. [-]
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Knowledge-Based Systems, 2016, vol. 110Rights
© 2016 Elsevier B.V. All rights reserved.
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