Abstract
Information flooding may occur during an OLAP session when the user drills down her cube up to a very fine-grained level, because the huge number of facts returned makes it very hard to analyze them using a pivot table. To overcome this problem we propose a novel OLAP operation, called shrink, aimed at balancing data precision with data size in cube visualization via pivot tables. The shrink operation fuses slices of similar data and replaces them with a single representative slice, respecting the constraints posed by dimension hierarchies, until the result is smaller than a given threshold. We present a greedy agglomerative clustering algorithm that at each step fuses the two slices yielding the minimum increase in the total approximation, and discuss some experimental results that show its efficiency and effectiveness.
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Golfarelli, M., Rizzi, S. (2013). Honey, I Shrunk the Cube. In: Catania, B., Guerrini, G., Pokorný, J. (eds) Advances in Databases and Information Systems. ADBIS 2013. Lecture Notes in Computer Science, vol 8133. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40683-6_14
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DOI: https://doi.org/10.1007/978-3-642-40683-6_14
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