Abstract
Multidimensional data analysis, as supported by OLAP (online analytical processing), requires the computation of many aggregate functions over a large volume of historically collected data. Meanwhile, a recent trend in data communities has been the presence of dynamic, interdisciplinary data communities in which users can find and select data from a wide range of data providers. Using this approach, we have designed the Cubing service platform, which allows rapid retrieval of warehouse cubes in a way that would be familiar to any online shopper. In such an open marketplace, cubing services play a role as a metadata layer that maps cube definitions to the underlying schema and defines how the published cubes will be queried. The proposed platform couples an efficient cube selection mechanism with semantic reasoning capabilities, capable of processing large data sources, which expressed in a variety of formalisms, into a collection of warehouse datasets that expose the native metadata in a uniform manner. Thus, the platform is easily extensible and robust to updates of both data and metadata in the warehouse datasets.
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Hoang, D.T.A., Ngo, N.S., Nguyen, B.T. (2012). Collective Cubing Platform towards Definition and Analysis of Warehouse Cubes. In: Nguyen, NT., Hoang, K., Jȩdrzejowicz, P. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2012. Lecture Notes in Computer Science(), vol 7654. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34707-8_2
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DOI: https://doi.org/10.1007/978-3-642-34707-8_2
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