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An OLAM Operator for Multi-Dimensional Shrink

An OLAM Operator for Multi-Dimensional Shrink

Stefano Rizzi, Matteo Golfarelli, Simone Graziani
Copyright: © 2015 |Volume: 11 |Issue: 3 |Pages: 30
ISSN: 1548-3924|EISSN: 1548-3932|EISBN13: 9781466675933|DOI: 10.4018/IJDWM.2015070104
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MLA

Rizzi, Stefano, et al. "An OLAM Operator for Multi-Dimensional Shrink." IJDWM vol.11, no.3 2015: pp.68-97. http://doi.org/10.4018/IJDWM.2015070104

APA

Rizzi, S., Golfarelli, M., & Graziani, S. (2015). An OLAM Operator for Multi-Dimensional Shrink. International Journal of Data Warehousing and Mining (IJDWM), 11(3), 68-97. http://doi.org/10.4018/IJDWM.2015070104

Chicago

Rizzi, Stefano, Matteo Golfarelli, and Simone Graziani. "An OLAM Operator for Multi-Dimensional Shrink," International Journal of Data Warehousing and Mining (IJDWM) 11, no.3: 68-97. http://doi.org/10.4018/IJDWM.2015070104

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Abstract

Shrink is an OLAM (On-Line Analytical Mining) operator based on hierarchical clustering, and it has been previously proposed in mono-dimensional form to balance precision with size in the visualization of cubes via pivot tables during OLAP analyses. It can be applied to the cube resulting from a query to decrease its size while controlling the approximation introduced; the idea is to fuse similar facts together and replace them with a single representative fact, respecting the bounds posed by dimension hierarchies. In this paper the authors propose a multi-dimensional generalization of the shrink operator, where facts are fused along multiple dimensions. Multi-dimensional shrink comes in two flavors: lazy and eager, where the bounds posed by hierarchies are respectively weaker and stricter. Greedy algorithms based on agglomerative clustering are presented for both lazy and eager shrink, and experimentally evaluated in terms of efficiency and effectiveness.

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