BIO-INSPIRED COMPUTING VIA ONTOLOGY TO ENHANCE TAKING A DECISION ON HETEROGENEOUS DATA

Abstract

Although Ontology supports phases of the decision support systems DSSs, there isn’t a standard method in which we could modeled decisions in Ontologies. Heterogeneity in data sources is a challenge in decision support systems. Sometimes, explore the knowledge without integrating data sources is wrong. So, this paper proposed a semantic enhancement on the genotype/phenotype system. That is for a communication decision support system based on the Ontology decision support system framework ODSS. This paper introduced a compact representation, and a search strategy based on the universal Ontology. The proposed method is general to handle any data mining technique on large heterogeneous data. That is by adapting the components of the Gene Expression system in biology. The main components of the Gene Expression system are Genome, phenotype, and mutation. The adaptation is by Ontology to help the communication decision support system. The method adapts mutation as a somatic mutation. We tested the proposed method by applying it on the big sample of heterogeneous communication data.