Query Size Estimation through Sampling
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Date
2005-01-06
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Abstract
Current Database management systems (DBMS) handle huge amounts of data and need fast query response time. DBMSs apply several strategies to execute user queries. Query optimizers in DBMSs compare costs for these strategies and choose the cheapest one. Materialized views are suggested to enhance query response time as one of the strategies in DBMSs. Cost of each strategy has to be accurately estimated to choose right strategy.
Because the materialized view is stored as a one table, we consider that sequential scan is used for executing of the materialized view. Therefore, I/O and CPU costs to execute materialized views depend on the number of tuples for the result. Hence, Our focus will be the accuracy of estimation of the number of tuples in materialized views; that is a query-size estimation. Many researches have been proposed to find methods to estimate the cost of query. This thesis reviews these researches and compares good and bad aspects for each cost estimation method. We choose size estimation methods that are more accurate than others to implement. We suggest various query environments for experiments. We suggest a guideline by experimental results.
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query size estimation, Query optimization, sampling
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Degree
MS
Discipline
Computer Science
