It also requires even more elaborate
semantic-based optimization transformations that will reduce the amount of data
processed at each step. The second calls for storage structures that are extremely
adaptive to updates, and for processing techniques ???borrowed??? from the field of
data stream processing.
4 Karayannidis, Tsois, & Sellis
Copyright ?© 2007, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of
Idea Group Inc. is prohibited.
Conclusion.
In this chapter, we discussed the processing of ad hoc star queries over hierarchically
clustered fact tables. We presented a complete abstract processing plan that covers
all the necessary steps for answering such queries. This plan directly exploits the
benefits of hierarchically clustered fact tables and opens the road for new optimization
challenges. We showed how this abstract plan can be ???materialized??? for the case
of a multidimensional storage structure that achieves hierarchical clustering, namely
the CUBE File. Finally, we presented the hierarchical pregrouping transformation
as a powerful optimization technique for this type of query processing.
Clearly, star query processing over hierarchically clustered fact tables is signifi-
cantly different from other approaches. The most remarkable difference is that the
fact table access is transformed to a multidimensional range query through the use
of h-surrogates (i.
Pages:
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307