Ontology learning from graph-stream representation of complex process

Abstract

Societies around the world faced arrival of smart technologies in the last decade. Often interconnected, intelligent devices form new entity called Internet of Things (IoT). Mounted to commodities they are versatile tools for collecting various sorts of data about our behavior. Related applications require novel knowledge exploration methods handling large amount of observations containing complex data. Therefore, this paper introduces graph-stream structure as a capable tool for the complex process description. Further, it delivers a method for graph-stream processing making possible extraction of the compact ontological description of the recorded process. Introduced method uses novel online clustering algorithm and was verified experimentally on synthetic data sets.