Streaming Knowledge Bases

Abstract

With the advent of pervasive computing, we encounter many scenarios where data is constantly flowing between sensors and applications. The volume of data produced is large, so is the rate of the dataflow. In such scenarios, knowledge extraction boils down to finding useful information i.e. detecting events of interest. Typical use cases where event detection is of paramount importance are surveillance, tracking, telecommunications data management, disease outburst detection and environmental monitoring. There are many streaming database applications built to deal with these dynamic environments. However, they can only deal with raw data – not with streaming facts. We argue that much like a new database approach had to be developed to deal with streaming data, a new approach will be required to deal with streaming facts expressed in the languages of the Semantic Web. Existing reasoners use techniques that load the whole RDF graph in main memory and carry out queries on it. This approach is of little use in real-time reasoning for streaming scenarios and takes considerable amount of time. In this paper, we combine a continuous query processors with Semantic Web techniques to build a reasoner that can deal with streaming facts. We describe our technique, and present empirical validation of its efficacy.