A genetic approach for materialised skyline views selection problem
Samiha Brahimi and
Mohamed-Khireddine Kholladi
International Journal of Data Mining, Modelling and Management, 2016, vol. 8, issue 3, 223-243
Abstract:
The materialisation of views has been one of the most successful techniques in optimising OLAP queries. In this context, many works have been conducted aiming either at studying the relationships between the views or at solving the materialised views selection problem. For skyline queries, researchers investigated only the first phase of the materialisation whereas the second phase which is the materialised skyline views selection problem has never been studied because of the difficulty of creating a cost model. In this paper, we propose a genetic method for the materialised skyline views selection problem. In order to reduce the high cost produced by evaluating the skycube all over the selection process, we propose a time reducing heuristic called OnceVisited which avoids the computation of the same query from the same views many times. The conducted experiments have proven the efficiency of the genetic approach since high quality solutions have been found. In addition, the proposed time reducing heuristic OnceVisited has been shown to be very efficient.
Keywords: skyline queries; skycube; materialisation of views; cost reducing; genetic algorithms; data mining; Orion; Top-Sky; view selection; OLAP queries. (search for similar items in EconPapers)
Date: 2016
References: Add references at CitEc
Citations:
Downloads: (external link)
http://www.inderscience.com/link.php?id=79060 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:ids:ijdmmm:v:8:y:2016:i:3:p:223-243
Access Statistics for this article
More articles in International Journal of Data Mining, Modelling and Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().