Yibei Ling
Abstract
Sampling-based methods for estimating relation sizes after relational operators such as selections, joins and projections have been intensively studied in recent years. Methods of this type can achieve high estimation accuracy and efficiency. Since the dominating overhead involved in a sampling-based method is the sampling cost, different variants of sampling methods are proposed so as to minimize the sampling percentage (thus reducing the sampling cost) while maintaining the estimation accuracy in terms of the confidence level and relative error (to be precisely defined later in Section 2). In order to determine the minimal sampling percentage, the overall characteristics of the data such as the mean and variance are needed. Currently, the representative sampling-based methods in literature are based on the assumption that overall characteristics of data are unavailable, and thus a significant amount of effort is dedicated to estimating these characteristics so as to approach the optimal (minimal) sampling percentage. The estimation for these characteristics incurs cost as well as suffers the estimation error. In this short essay, we point out that the exact values of these characteristics of data can be kept track of in a database system at a negligible overhead. As a result, the minimal sampling percentage while ensuring the specified relative error and confidence level can be precisely determined.
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Index Terms
- A supplement to sampling-based methods for query size estimation in a database system
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