Statistics Based Optimization Technique for Query Processing

Abstract

Semantic web consists of the data in the structure manner and query searching methods can access these structured data to provide effective search result. The query recommendation in the semantic web relevance is needed to be improved based on the user input query. Many existing methods are used to improve the query recommendation efficiency using the optimization technique such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO). These methods involve in the use of many features which are selected from the user query. This in-turn increases the cost of a query in the semantic web. In this research, the query optimization was carried out by using the statistics method. The statistics based optimization method requires fewer features such as triple pattern and node priority etc., for finding the relevant results. The LUBM dataset contains the semantic queries and this dataset is used to measure the efficiency of the proposed Statistical based optimization method. The SPARQL queries are used to plot the query graph and triple scores are extracted from the graph. The cost value of the triple scores is measured and given as input to the proposed statistics method. The execution time of the statistics based optimization method for the query is 35 ms while the existing method has 48 ms.