Improving the speed of view maintenance in data warehouses

Abstract

In recent years, due to the use of data warehouses, the subject of materialized view maintanance is important. For quick access to data, data warehouses collect the necessary data from various sources and stored them to form of materialized views. This leads to increased speed of responding to queries. When the data changes over different resources, the materialized views should be updated that leads to the subject of view maintanence. At this time, algorithms are presented in order to view maintanance with optimized cost. The algorithm presented in this paper is the combining of a mathematical method with Cultural meta-heuristic algorithm that leads to reduce search time and optimizes the cost of access to data in data warehouses. Cultural algorithm uses a reasonable belief space, including several incremental view maintenance relations. The best response obtained at the end of every generation is stored in the space called the belief space. The tests showe that Cultural algorithm is faster to maintain incremental views compared to previous methods and algorithms like bacterial and bees and learning tlbo algorithm.

Keywords

References

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