A Novel Adaptive Cuckoo Search for Optimal Query Plan Generation

Abstract

The emergence of multiple web pages day by day leads to the development of the semantic web technology. A World Wide Web Consortium (W3C) standard for storing semantic web data is the resource description framework (RDF). To enhance the efficiency in the execution time for querying large RDF graphs, the evolving metaheuristic algorithms become an alternate to the traditional query optimization methods. This paper focuses on the problem of query optimization of semantic web data. An efficient algorithm called adaptive Cuckoo search (ACS) for querying and generating optimal query plan for large RDF graphs is designed in this research. Experiments were conducted on different datasets with varying number of predicates. The experimental results have exposed that the proposed approach has provided significant results in terms of query execution time. The extent to which the algorithm is efficient is tested and the results are documented.

Semantic Representation and Optimized Querying of Cancer Data using Modified Shuffled Frog Leaping Algorithm

“…In this investigation, the fitness function is compared with the cost of the right deep tree. The cost of a right deep tree depends on the selectivity of the triples and cardinality estimation [6]. Consider Ri be the cardinality of the triples and fi,j be the selectivity of the triples.…”

Optimization of Different Queries using Optimization Algorithm (DE)

“…Gubichev and Neumann [32] extend Characteristic Sets to provide a new RDF statistical synopsis that accurately estimates cardinalities. Gomathi et al [19] presented an optimization algorithm named Adaptive Cuckoo search(ACS) to optimize the SPARQL query. Kalayci et al [33] presented a new solution for the optimization using Ant Colony Optimization (ACO) and reordering triple patterns and selectivity estimation introduced by [26] with some modification.…”

“…The first step to reach our aim is to find the fitness function using cost and then apply the DE algorithm. The DE algorithm [19][35] briefly described as follows:…”

“…Different soft computing algorithms have already been evolved for query optimization in context of RDBMS [11][12][13][14][15][16], and in context of Semantic Web [10] [17][18][19]. The current state of art techniques need for better optimization algorithm.…”

Specific Queries Optimization Using Jaya Approach

“…The heuristic used by this experiment is introduced by [14]. In these solutions for query optimization, Gomathi et al [26] also contributes to an efficient algorithm named adaptive Cuckoo search (ACS) to an optimal query plan for large RDF data. Next, Kalayci et al [27] proposed a new optimization strategy using Ant Colony Optimization (ACO) algorithm for reordering triple patterns and they used statistics for selectivity estimation proposed by [19] with some modification.…”