Query Size Estimation by Adaptive Sampling

Query Estimation by Adaptive Sampling

The ability to provide accurate and efficient result estimations of user queries is very important for the query optimizer in database systems. In this paper, we show that the traditional estimation techniques with data reduction points of view do not produce satisfiable estimation results if the query patterns are dynamically changing. We further show that to reduce query estimation error, ins...

Query Size Estimation using Systematic Sampling

In this paper, we propose a new approach to the estimation of query size for select and join operations. The technique, which we have called \systematic sam-pling", is a novel variant of the sampling approach, which sorts the relation before sampling, and which maintains a summary relation to improve run-time performance. We compare the method to a number of existing methods to tackle the probl...

Adaptive Query-Based Sampling of Distributed Collections

As part of a Distributed Information Retrieval system a description of each remote information resource, archive or repository is usually stored centrally in order to facilitate resource selection. The acquisition of precise resource descriptions is therefore an important phase in Distributed Information Retrieval, as the quality of such representations will impact on selection accuracy, and ul...

Adaptive Threshold Sampling and Estimation

Sampling is a fundamental problem in both computer science and statistics. A number of issues arise when designing a method based on sampling. These include statistical considerations such as constructing a good sampling design and ensuring there are good, tractable estimators for the quantities of interest as well as computational considerations such as designing fast algorithms for streaming ...

Size Estimation for Query Results Using Histograms