Honey, I Shrunk the Cube

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Provided by: Springer Healthcare
Topic: Big Data
Format: PDF
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 the authors 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.
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