Combining hierarchy encoding and pre-grouping: Intelligent grouping in star join processing

Pieringer, R; Elhardt, K; Ramsak, F; Markl, V; Fenk, R; Bayer, R; Karayannidis, N; Tsois, A; Sellis, T

dc.contributor.author	Pieringer, R	en
dc.contributor.author	Elhardt, K	en
dc.contributor.author	Ramsak, F	en
dc.contributor.author	Markl, V	en
dc.contributor.author	Fenk, R	en
dc.contributor.author	Bayer, R	en
dc.contributor.author	Karayannidis, N	en
dc.contributor.author	Tsois, A	en
dc.contributor.author	Sellis, T	en
dc.date.accessioned	2014-03-01T02:42:13Z
dc.date.available	2014-03-01T02:42:13Z
dc.date.issued	2003	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30868
dc.subject	Data Warehouse	en
dc.subject	Hierarchical Clustering	en
dc.subject	Materialized Views	en
dc.subject	Perforation	en
dc.subject	Query Processing	en
dc.subject	Query Rewriting	en
dc.subject.other	Hierarchy semantics	en
dc.subject.other	Logical optimization	en
dc.subject.other	Star query processing	en
dc.subject.other	Algorithms	en
dc.subject.other	Data processing	en
dc.subject.other	Optimization	en
dc.subject.other	Performance	en
dc.subject.other	Program compilers	en
dc.subject.other	Query languages	en
dc.subject.other	Semantics	en
dc.subject.other	Data warehouses	en
dc.title	Combining hierarchy encoding and pre-grouping: Intelligent grouping in star join processing	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ICDE.2003.1260803	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICDE.2003.1260803	en
heal.publicationDate	2003	en
heal.abstract	Efficient star query processing is crucial for a performance data warehouse (DW) implementation and much work is available on physical optimization (e.g., indexing and schema design) and logical optimization (e.g., pre-aggregated materialized views with query rewriting). One important step in the query processing phase is, however, still a bottleneck: the residual join of results from the fact table with the dimension tables in combination with grouping and aggregation. This phase typically consumes between 50% and 80% of the overall processing times. In typical DW scenarios pre-grouping methods only have a limited effect as the grouping is usually specified on the hierarchy levels of the dimension tables and not on the fact table itself. In this paper, we suggest a combination of hierarchical clustering and pre-grouping as we have implemented in the relational DBMS Transbase. Exploiting hierarchy semantics for the pre-grouping of fact table result tuples is several times faster than conventional query processing. The reason for this is that hierarchical pre-grouping reduces the number the join operations significantly. With this method even queries covering a large part of the table can be executed within a time span acceptable for interactive query processing.	en
heal.journalName	Proceedings - International Conference on Data Engineering	en
dc.identifier.doi	10.1109/ICDE.2003.1260803	en
dc.identifier.spage	329	en
dc.identifier.epage	340	en