PROJECT TITLE :

Optimizing Cost of Continuous Overlapping Queries over Data Streams by Filter Adaption

ABSTRACT:

The problem we tend to aim to address is that the optimization of cost management for executing multiple continuous queries on information streams, where each query is outlined by many filters, every of that monitors bound standing of the data stream. Specially, the filter will be shared by completely different queries and expensive to guage. The conventional objective for such a drawback is to attenuate the general execution value to solve all queries, by planning the order of filter analysis in shared strategy. But, in the streaming state of affairs, the characteristics of data things may change in method, that can bring some uncertainty to the end result of individual filter analysis, and have an effect on the plan of query execution and the overall execution price. In our work, considering the influence of the unsure variation of data characteristics, we have a tendency to propose a framework to deal with the dynamic adjustment of filter ordering for query execution on knowledge stream, and specialise in the issues of value management. By incrementally monitoring and analyzing the results of filter evaluation, our proposed approach will be effectively adaptive to the various stream behavior and alter the optimal ordering of filter evaluation, so as to optimize the execution value. So as to achieve satisfactory performance and efficiency, we additionally discuss the trade-off between the adaptivity of our framework and also the overhead incurred by filter adaption. The experimental results on artificial and 2 real data sets (traffic and multimedia) show that our framework will effectively reduce and balance the query execution value and keep high adaptivity in streaming scenario.