Conceptual Object Modeling for OLAP Cubes in a Data Warehousing Environment

Datacubes are efficient structures used to represent multidimensional aggregates at various levels. Quite often multiple datacubes are predefined and computed in order to assist analytical queries in Decision support systems. However being statically defined structures, they suffer from some inherent problems. Firstly, conventional datacubes are highly inefficient when created over sparse data. Next, datacubes in traditional approaches address "fixed" or predefined queries, and are useless with truly adhoc queries that would require expensive join operations with the fact table. Furthermore, traditional cubes are defined based on nonrecursive SPJ views, hence incapable of accommodating "recursive" OLAP queries. In order to address these issues, we adopt and extend an Object-Oriented Data Warehousing (OODW) approach to datacubes. We represent the Cube notion as a generic Class, thereby semantically enriching the static cube structure and inter-cell relationships via Oids. More specifically, by constructing semantic cube hierarchies and defining class views, we are able to compute/derive new cubes efficiently by avoiding unnecessary access to the lower level detailed tables. Also, recursive cubes can be defined and computed within the same framework. Algorithms are developed to compute specialized dynamic Cubes in a multiFact environment, taking into account a given a set of predefined OLAP queries. We validate our approach with an experimental evaluation of our algorithms, which also provides a foundation for further studies on semantic optimization of Cube queries.

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