2011/01/30 by Matteo Riondato, Riondato, Matteo, Mert Akdere +8 · 1 citation
Computer Science · #Advanced Database Systems and Queries #Data Management and Algorithms #Data Structures and Algorithms (cs.DS) #Databases (cs.DB) #FOS: Computer and information sciences #G.3 #H.2.4 #Machine Learning (cs.LG) #Machine Learning and Algorithms #cs.DB #cs.DS #cs.LG
paper · pdf · doi:10.48550/arxiv.1101.5805
20 pages, 3 figures
openalex publication_date 2011/01/30 · arxiv created 2011/08/11 · arxiv updated 2015/03/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We develop a novel method, based on the statistical concept of the Vapnik-Chervonenkis dimension, to evaluate the selectivity (output cardinality) of SQL queries - a crucial step in optimizing the execution of large scale database and data-mining operations. The major theoretical contribution of this work, which is of independent interest, is an explicit bound to the VC-dimension of a range space defined by all possible outcomes of a collection (class) of queries. We prove that the VC-dimension is a function of the maximum number of Boolean operations in the selection predicate and of the maximum number of select and join operations in any individual query in the collection, but it is neither a function of the number of queries in the collection nor of the size (number of tuples) of the database. We leverage on this result and develop a method that, given a class of queries, builds a concise random sample of a database, such that with high probability the execution of any query in the class on the sample provides an accurate estimate for the selectivity of the query on the original large database. The error probability holds simultaneously for the selectivity estimates of all queries in the collection, thus the same sample can be used to evaluate the selectivity of multiple queries, and the sample needs to be refreshed only following major changes in the database. The sample representation computed by our method is typically sufficiently small to be stored in main memory. We present extensive experimental results, validating our theoretical analysis and demonstrating the advantage of our technique when compared to complex selectivity estimation techniques used in PostgreSQL and the Microsoft SQL Server.