A Hybrid BATCS Algorithm to Generate Optimal Query Plan

The enormous increase in the amount of web pages day by day leads to progress in semantic web data management. The issues in semantic web data management are increasing and there is a need for improvement in research to handle them. One of the most important issues is the process of query optimization. The semantic web data stored in the form of Resource Description Framework (RDF) data can be queried using the popular query language SPARQL Protocol And RDF Query Language (SPARQL). As the size of the data increases, complication arises in querying the RDF data. The problem of querying the RDF graphs involves multiple join operations and optimizing those joins becomes NP-hard. Nature inspired algorithms are becoming much popular in recent days to handle problems with high complexity. In this research, a hybrid BAT Algorithm with Cuckoo Search (BATCS) is proposed to handle the problem of query optimization. The algorithm applies the echolocation behaviour of bats and hybrids with cuckoo search if the best solution stagnates for a designated number of iterations. Experiments were conducted with benchmark data sets and the algorithm proves that it performs efficiently in terms of query execution time.