Execution Planning for Continuous Queries over Dissemination Network of Data Aggregators

International Journal of Computer Trends and Technology (IJCTT)          
© - October Issue 2013 by IJCTT Journal
Volume-4 Issue-10                           
Year of Publication : 2013
Authors :M .Naresh Kumar , R.Sailaja


M .Naresh Kumar , R.Sailaja"Execution Planning for Continuous Queries over Dissemination Network of Data Aggregators "International Journal of Computer Trends and Technology (IJCTT),V4(10):3476-3484 October Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract:- Normally continuous queries are those used, to observe the dynamically changing data and to give results helpful for Online decision making. Generally a user wants to achieve the value of few aggregation functions over shared data items, for example, to regulate when the value of a stock portfolio exceeds a threshold. We focus on approaches and techniques to assign such dynamically changing data to a large number of users with high accuracy, efficiency, and scalability. In these queries a client maintains a consistency requirement as part of the query. We come up with a minimal –cost based approach to answer continuous aggregation queries using a network of aggregators of dynamic data items. In that network of data aggregators, each data aggregator handles a set of data items at definite coherencies. Our technique is splitting a client query into sub-queries and executing sub-queries on properly chosen data aggregators with their respective sub-query incoherency bounds. We give a mechanism for collecting the optimal set of sub-queries with their inconsistency bounds which satisfies client query’s coherency requirement with minimum number of update messages issued from aggregators to the client. We layout a cost based model which can be used to evaluating the number of messages prescribed to satisfy the client specified incoherency bound .


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Keywords :— Coherency, Continuous queries, Cost, Distributed query processing, Data dissemination, Performance.