Query Optimization in Uncertain and Probabilistic Databases

Abstract

Query optimization is a critical aspect of database systems as it helps to reduce query execution time and improve system performance. In this study, Probabilistic object models to get the specific facts from available statistics and efficient query optimization. Query optimization is a technique that considers potential query plans based on lineage in order to determine the most effective way to perform a particular query. Many exceptional components are together used to perform query optimization along with scanner and parser, intermediate shape of query and query optimizer. Based on order of all of the clause and lineage expression tree of query, many query execution plan can be generated, then query optimizer will select efficient query plan for query optimization. The query's ultimate result will be produced based on that strategy. This paper is concentrated on, to analyzed the performance of different query optimization techniques in uncertain and probabilistic databases using the RelationalCross Model, Simple PODM, Optimize PODM, and Optimize Cache PODM. The outcomes demonstrated that in terms of query execution time and system performance, the Optimize Cache PODM strategy performed better than the other techniques. It also identified some challenges and limitations in query optimization for uncertain and probabilistic databases. Overall, this study highlights the importance of query optimization techniques in uncertain and probabilistic databases and provides insights into the effectiveness of different optimization techniques. These findings can help database administrators and developers to make informed decisions when choosing the most suitable query optimization technique for their database system.