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URN: urn:nbn:de:swb:90-304225

Clustering-Initialized Adaptive Histograms and Probabilistic Cost Estimation for Query Optimization

Khachatryan, Andranik

An assumption with self-tuning histograms has been that they can "learn" the dataset if given enough training queries. We show that this is not the case with the current approaches. The quality of the histogram depends on the initial configuration. Starting with few good buckets can improve the efficiency of learning. Without this, the histogram is likely to stagnate, i.e. converge to a bad configuration and stop learning. We also present a probabilistic cost estimation model.

Zugehörige Institution(en) am KIT Institut für Programmstrukturen und Datenorganisation (IPD)
Publikationstyp Hochschulschrift
Jahr 2012
Sprache Englisch
Identifikator KITopen ID: 1000030422
Verlag Karlsruhe
Abschlussart Dissertation
Fakultät Fakultät für Informatik (INFORMATIK)
Institut Institut für Programmstrukturen und Datenorganisation (IPD)
Prüfungsdaten 30.04.2012
Referent/Betreuer Prof. K. Böhm
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