ADAPTIVE SHARING METHOD FOR MULTIPLE CONTINUOUS QUERY
Journal Title: International Journal of Advanced Research in Computer Engineering & Technology(IJARCET) - Year 2013, Vol 2, Issue 3
Abstract
As the data management field has diversified to consider settings in which queries are increasingly complex, statistics are less available, or data is stored remotely, there has been an acknowledgment that the traditional optimize-then-execute paradigm is insufficient. The main objective of our project is to develop and produce an optimized global execution plan for the collective evaluation of a static set of multi-way continuous queries. Generally in a data stream environment, a considerable number of continuous queries are registrated in advance and should be processed continuously. For that there is a need of multiple query optimizations. For this purpose, this paper proposes a new method called A-SEGO that optimizes a set of multi-way join queries collectively by tracing a set of promising subplans. The proposed approach uses a cost profile that maintains statistical synopses of the cost of join operations in past global execution plans. Based on these cost statistics and on a user-defined cost-bound parameter, a set of promising subplans is determined to trace them concurrently. This paper focuses on producing an optimized global execution plan for the collective evaluation of a static set of multi-way continuous queries. In real data stream applications, a set of continuous queries registered in a DSMS can be changed as time goes. Therefore, it is necessary to devise an incremental optimization mechanism. With this approach, the system needs to store existing query computations, identify the common computations between the new query and the existing query plan, choose optimally among multiple sharing paths, and add unsharable new computations to the plan[4].
Authors and Affiliations
Mariat Thomas , Mr. G. Naveen Sundar
Keywords
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