This paper reviews recent literature on techniques for obtaining random samples from databases. We begin with a discussion of why one would want to include sampling facilities in database management systems. We then review basic sampling techniques used in constructing DBMS sampling algorithms, e.g. acceptance/rejection and reservoir sampling. A discussion of sampling from various data structures follows: B + trees, hash files, spatial data structures (including R-trees and quadtrees). Algorithms for sampling from simple relational queries, e.g. single relational operators such as selection, intersection, union, set difference, projection, and join are then described. We then describe sampling for estimation of aggregates (e.g. the size of query results). Here we discuss both clustered sampling, and sequential sampling approaches. Decision-theoretic approaches to sampling for query optimization are reviewed.

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In this paper, we propose a new strategy for optimizing the placement of bin boundaries to minimize the cost of query evaluation using bitmap indices with binning. For attributes with a large number of distinct values, often the most efficient index scheme is a bitmap index with binning. However, this type of index may not be able to fully resolve some user queries. To fully resolve these queries, one has to access parts of the original data to check whether certain candidate records actually satisfy the specified conditions. We call this procedure the candidate check, which usually dominates the total query processing time. Given a set of user queries, we seek to minimize the total time required to answer the queries by optimally placing the bin boundaries. We show that our dynamic programming based algorithm can efficiently determine the bin boundaries. We verify our analysis with some real user queries from the Sloan Digital Sky Survey. For queries that require significant amount of time to perform candidate check, using our optimal bin boundaries reduces the candidate check time by a factor of 2 and the total query processing time by 40%.