Heuristic optimization of OLAP queries in multidimensionally hierarchically clustered databases

Theodoratos, D; Tsois, A

dc.contributor.author	Theodoratos, D	en
dc.contributor.author	Tsois, A	en
dc.date.accessioned	2014-03-01T02:41:53Z
dc.date.available	2014-03-01T02:41:53Z
dc.date.issued	2001	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30656
dc.subject	Heuristic Optimization	en
dc.subject	Hierarchical Clustering	en
dc.subject	Indexation	en
dc.subject	Indexing Method	en
dc.subject	Multidimensional Access Method	en
dc.subject	Multidimensional Database	en
dc.subject	On Line Analytical Processing	en
dc.subject	Relational Database Management System	en
dc.subject.other	Data flow analysis	en
dc.subject.other	Decision support systems	en
dc.subject.other	Heuristic methods	en
dc.subject.other	Hierarchical systems	en
dc.subject.other	Optimization	en
dc.subject.other	Relational database systems	en
dc.subject.other	Response time (computer systems)	en
dc.subject.other	Hierarchical clustered database	en
dc.subject.other	Multidimensional clustered database	en
dc.subject.other	Online analysitical processing	en
dc.subject.other	Query processor	en
dc.subject.other	Query languages	en
dc.title	Heuristic optimization of OLAP queries in multidimensionally hierarchically clustered databases	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1145/512236.512243	en
heal.identifier.secondary	http://dx.doi.org/10.1145/512236.512243	en
heal.publicationDate	2001	en
heal.abstract	On-Line Analytical Processing (OLAP) is a technology that encompasses applications requiring a multidimensional and hierarchical view of data. OLAP applications often require fast response time to complex grouping/aggregation queries on enormous quantities of data. Commercial relational database management systems use mainly multiple one-dimensional indexes to process OLAP queries that restrict multiple dimensions. However, in many cases, multidimensional access methods outperform one-dimensional indexing methods. We present an architecture for multidimensional databases that are clustered with respect to multiple hierarchical dimensions. It is based on the star schema and is called CSB star. Then, we focus on heuristically optimizing OLAP queries over this schema using multidimensional access methods. Users can still formulate their queries over a traditional star schema, which are then rewritten by the query processor over the CSB star. We exploit the different clustering features of the CSB star to efficiently process a class of typical OLAP queries. We detect special cases where the construction of an evaluation plan can be simplified and we discuss improvements of our technique.	en
heal.journalName	ACM International Workshop on Data Warehousing and OLAP (DOLAP)	en
dc.identifier.doi	10.1145/512236.512243	en
dc.identifier.spage	48	en
dc.identifier.epage	55	en