Query Optimization in OODBMS using Query Decomposition & Query Caching

Query optimization is of great importance for the performance of databases, especially for the execution of complex query statements. A query optimizer determines the best strategy for performing each query. These decisions have a tremendous effect on quer y performance, and query optimization is a key technology for every application, from operational systems to data warehouse and analysis systems to content - management systems. For example, query optimizers transform query statements, so that these complex statements can be transformed into semantically equivalent, but better performing, query statements. The query optimizer chooses, for example, whether or not to use indexes for a given query, and which join techniques to use when joining multiple tables. Query optimizers are typically cost - based. In a cost - based optimization strategy, multiple execution plans are generated for a given query, and then an estimated cost is computed for each plan. The query optimizer chooses the plan with the lowest estimate d cost. This report is based on relatively newer approach for query optimization in object databases, which uses query decomposition and cached query results to improve execution times for a query. Multiple experiments were performed to prove the productivity of this newer way of optimizing a query . The limitation of this technique is that its useful especially in scenarios where data manipulation rate is very low as compared to data retrieval rate