OPTIMIZED GENETIC ALGORITHM FOR SPATIAL DATA EVENTS IN ACTIVE DATA WAREHOUSE
|*Paramasivam . K and Dr.C.Chandrasekar
Research scholar and Associate Professor, Dept. of Computer Science, Periyar University, Salem, TamilNadu -India. [email protected]
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A fast changing in dynamic heterogeneous environment makes the data to be maintained up-to-date. Due to enormous amount and composite formation of spatial data, the optimization of spatial query is a complex process and significant from research viewpoints. Numerous programming techniques and methods are being presented to optimize the solutions formed by spatial analysis tools. The previous work used the GA technique for multi-join operation in active data warehouse. Genetic Algorithm (GA) is one of the normal optimization schemes, which creates many possible optimal solutions than the extra linear programming tools. But the downside of the previous work is that it does not discuss about the spatial data transformation and execution. To overcome the issues raised over spatial data in active data warehouse, in this work, we are going to present a new technique termed as optimized genetic algorithm used to form logical entity relation with spatial state of data extraction and transformation. Several query operations are carried over with spatial data to perform multi-join relations in active data warehouse and the selection of more appropriate combination of multiple relations are done by using optimized genetic algorithm. An optimized crossover and optimized mutation chooses the best combination of multiple relations of joins contains spatial data. Experimental evaluations are carried out with both synthetic and real datasets to estimate the performance of the proposed optimized genetic algorithm for multi-join relation with spatial data extraction and transformation in terms of multi-join execution time, optimal query time and gene population are the metrics being used to compute optimal threshold for multi-relational joins generation.