A Hybrid BATCS Algorithm to Generate Optimal Query Plan
Gomathi Ramalingam1 and Sharmila Dhandapani2
1Department
of Computer Science and Engineering, Bannari Amman Institute of Technology, India
2Department of Electronics and Instrumentation Engineering, Bannari Amman
Institute of Technology, India
Abstract: 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.
Keywords: Data
management, query optimization, nature inspired algorithms, bat algorithm, cuckoo
search algorithm.