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Data Reduction in Multifunction OLAP

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 11695)

Abstract

Multifunction OLAP allows to associate several types of aggregation functions to the same measure: general, dimensional for each analysis axis, hierarchical for each hierarchy and differentiated for each granularity level. These functions are generally non-commutative, so, an execution order between the functions is predefined. Pivot tables and several diagram types (bars, pies, etc.) are used to visualize interactively the result of an OLAP query. Unfortunately, no works investigate readability issues in multifunction OLAP. Therefore, we propose a post-processing method to reduce data size of the multifunction OLAP query result in order to improve the readability. This method aggregates data at higher granularity levels, i.e., doing a Rollup operation. It starts by studying the current query to find the functions that have already been executed. Then, it finds all possible Rollup operations, which respect the execution order and the aggregation constraints, and it calculates its data size. We propose several strategies to select a Rollup that gives a readable diagram and keeps as many details as possible: looking at the data size only, the number of implicated granularity levels and the number or the type of implicated dimensions. Once a Rollup is selected, we find the functions that realize it and we execute them in the right execution order.

Keywords

OLAP Multifunction aggregation Data reduction 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.R&D UmanisLevallois-PerretFrance

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