Continuous Aggregation Queries Based on Clustering Based Penalty Adaptive Query Planning?

The passive web pages can transform into active environment by the continuous queries are persistent queries by providing the time varying dynamic query results useful for online decision making. To handle a large number of users with diverse interests a continuous query system must be capable of supporting server push style of Internet-based communication. A network of data aggregators has prior approaches for the scalable handling of push based data dissemination. Their implementation required Greedy Heuristics Algorithm along with pre configured incoherency bounds to manage both multiple aggregators and multiple clients for supporting server push based communications. The sub-optimal solutions are explored by existing heuristic-based approaches can only explore a limited solution space. So we propose to use an adaptive and cost-based approach. In a network of data aggregators, each dedicated and judiciously chosen aggregator serves a set of data items at specific coherencies. By our approach we can decompose a client query into sub-queries and executing sub-queries using aggregators with their individual sub-query incoherency bounds. Our cost model takes into account both the processing cost and the communication cost unlike prior approaches. Clustering based penalty Adaptive Query Planning has better performance in terms of both processing and communication cost.