An exact method for shrinking pivot tables

Marco A. Boschetti, Matteo Golfarelli and Simone Graziani

Omega, 2020, vol. 93, issue C

Abstract: Pivot tables are one of the most popular tools for data visualization in both business and research applications. Although they are in general easy to use, their comprehensibility becomes progressively lower when the quantity of cells to be visualized increases (i.e., information flooding problem). Pivot tables are largely adopted in OLAP, the main approach to multidimensional data analysis. To cope with the information flooding problem in OLAP, the shrink operation enables users to balance the size of query results with their approximation, exploiting the presence of multidimensional hierarchies. The only implementation of the shrink operator proposed in the literature is based on a greedy heuristic that, in many cases, is far from reaching a desired level of effectiveness.

Keywords: OLAP; Integer linear programming; Set partitioning; Lagrangian relaxation; Pricing (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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DOI: 10.1016/j.omega.2019.03.002

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