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

Abstract

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