Combining hierarchy encoding and pre-grouping: intelligent grouping in star join processing
| Autor: | Roland Pieringer, Nikos Karayannidis, Timos Sellis, Robert Fenk, Volker Markl, Rudolf Bayer, Klaus Elhardt, Aris Tsois, Frank Ramsak |
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| Rok vydání: | 2004 |
| Předmět: |
Hierarchy
Theoretical computer science Computer science Search engine indexing Materialized view Fact table computer.software_genre Query optimization Data warehouse Database index Relational database management system Star schema Sargable Data mining Tuple Dimension (data warehouse) Query Rewriting computer |
| Zdroj: | ICDE |
| DOI: | 10.1109/icde.2003.1260803 |
| Popis: | Efficient star query processing is crucial for a performant 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 time. 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. 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 of join operations significantly. With this method even queries covering a large part of the fact table can be executed within a time span acceptable for interactive query processing. |
| Databáze: | OpenAIRE |
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