Advanced Ad Hoc Star Query Processing
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Introduction.
Star queries are the most prevalent kind of queries in data warehousing, online
analytical processing (OLAP), and business intelligence applications. Star queries
impose restrictions on the dimension tables that are used for selecting specific
facts from the fact table; these facts are further grouped and aggregated according
to the user demands. Furthermore, advanced decision support calls for ad hoc
analysis, in contrast to using predefined reports that are constructed periodically,
or have already been precomputed. The foundation for this kind of analysis is the
support of ad hoc star queries, which comprise the real essence of OLAP. Efficient
processing of ad hoc star queries is a very difficult task considering, on one hand,
the native complexity of typical OLAP queries, which potentially combine huge
amounts of data, and on the other, the fact that no a priori knowledge for the query
exists and thus no precomputation of results or other query-specific tuning can be
exploited. The only way to evaluate these queries is to access directly the base data
in an efficient way.
Traditionally, the major bottleneck in evaluating star queries has been the join of
the central (and usually very large) fact table with the surrounding dimension tables
(also known as a star join).
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