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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.

Volltext §
DOI: 10.5445/IR/1000030422
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Programmstrukturen und Datenorganisation (IPD)
Publikationstyp Hochschulschrift
Publikationsjahr 2012
Sprache Englisch
Identifikator urn:nbn:de:swb:90-304225
KITopen-ID: 1000030422
Verlag Karlsruher Institut für Technologie (KIT)
Art der Arbeit Dissertation
Fakultät Fakultät für Informatik (INFORMATIK)
Institut Institut für Programmstrukturen und Datenorganisation (IPD)
Prüfungsdaten 30.04.2012
Referent/Betreuer Böhm, K.
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