Ontology-Driven Visual Analytics Platform for Semantic Data Mining and Fuzzy Classification

Visualization is claimed as one of the essential “V’s” Big Data since it allows presenting data in a human-friendly way and is, therefore, stepping-stone for mining process. Visual analytics, turn, ensures knowledge discovery out through cognitive graphics filtering capabilities. But to be efficient, visualization analytics tools have consider other by handling large volumes, keeping up with growth changing velocity, adapting variety representation formats. We propose using ontology engineering methods create visual platform controlled an ontological base that describes supported types, input formats, filters, objects, algorithms, well available communication protocols computing nodes, modules can run on. This introducing new functions distributed computation scenarios on fly just extending underlying domain ontologies without source code platform’s core. The flow inside this described task enabling semantic As result, seamless integration different sources achieved, including plain files, databases, even third-party soft- hardware solvers. demonstrate viability approach proposed solving several fuzzy classification problems, assessment citizens’ regional identity according mental maps they draw reconstruction ontogenesis extinct synapsid ‘Titanophoneus potens’ Efremov, 1938.