OPTIMIZED INTENTION FOR CONTINUOUS QUERIES IN THE ACTIVE DATA AGGREGATION NETWORK WITH COST MODEL

Within an RDBMS streams of changes to the data and reporting when the result of a query defined over the data changes. These queries are referred to as the continuous queries since they continually produce results whenever new data arrives or existing data changes. To make online decision we require monitoring the continuous queries. Here the aim is to introduce the low-cost, scalable technique to answer continuous aggregation queries using a network of aggregators of dynamic data items. There is significant work in systems that can efficiently deliver the relevant updates automatically and also to provide for getting the optimal set of sub queries with their incoherency bounds which satisfies client query’s coherency requirement with least number of refresh messages sent from aggregators to the client. For optimal query execution divide the query into sub-queries and evaluate each sub-query at a judiciously chosen data aggregator. The main purpose is to response the client with the least number of tasks with the help of random query selection. Random query selection means for the user submitted query the relevant queries, sub queries are generated. A query cost model which can be used to estimate the number of messages required to satisfy the client specified incoherency bound.