A scalable adaptive method for complex reasoning over semantic data streams

Abstract

Data streams are the infinite sequences of data elements that are being generated by companies, social network, mobile phones, smart homes, public transport vehicles and other modern infrastructures. Current stream processing solutions can handle streams of data to timely produce new results but they lack the complex reasoning capacities that are required to go from data to actionable knowledge. Conversely, engines that can perform such complex reasoning tasks, are mostly designed to work on static data. The main aim of my research proposal is to provide a solution to perform complex reasoning on dynamic semantic information in a scalable way. At its core, this requires a solution which combines advantages of both stream processing and reasoning research areas, and has flexible heuristics for adaptation of the stream reasoning processes in order to enhance scalability.