Abstract
Many models have been proposed for multidimensional data warehouse modeling and most consider a same function to determine how measure values are aggregated according to different data detail levels. We provide a conceptual model that supports (1) multiple aggregations, associating to the same measure a different aggregation function according to analysis axes, and (2) differentiated aggregation, allowing specific aggregations at each detail level. Our model is based on a graphical formalism that allows controlling the validity of aggregation functions (distributive, algebraic or holistic). We also show how conceptual modeling can be used, in an R-OLAP environment, for building lattices of pre-computed aggregates.
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Hassan, A., Ravat, F., Teste, O., Tournier, R., Zurfluh, G. (2012). Differentiated Multiple Aggregations in Multidimensional Databases. In: Cuzzocrea, A., Dayal, U. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2012. Lecture Notes in Computer Science, vol 7448. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32584-7_8
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DOI: https://doi.org/10.1007/978-3-642-32584-7_8
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