Abstract
Data-intensive systems routinely use derived data (e.g., indexes or materialized views) to improve query-evaluation performance. We present a system architecture for Query-Performance Enhancement by Tuning (QPET), which combines design and use of derived data in an end-to-end approach to automated query-performance tuning. Our focus is on a tradeo. between (1) the amount of system resources spent on designing derived data and on keeping the data up to date, and (2) the degree of the resulting improvement in query performance. From the technical point of view, the novelty that we introduce is that we combine aggregate query rewriting techniques [1, 2] and view selection techniques [3] to achieve our goal.
Keywords
- Evaluation Cost
- Data Cube
- Query Performance
- Aggregate Query
- View Selection
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Afrati, F., Chirkova, R., Gupta, S., Loftis, C. (2005). Designing and Using Views to Improve Performance of Aggregate Queries (Extended Abstract). In: Zhou, L., Ooi, B.C., Meng, X. (eds) Database Systems for Advanced Applications. DASFAA 2005. Lecture Notes in Computer Science, vol 3453. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11408079_48
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DOI: https://doi.org/10.1007/11408079_48
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