Towards Scalable Federated Context-Aware Stream Reasoning
- Cite this paper as:
- Dejonghe A. (2016) Towards Scalable Federated Context-Aware Stream Reasoning. In: Sack H., Blomqvist E., d'Aquin M., Ghidini C., Ponzetto S., Lange C. (eds) The Semantic Web. Latest Advances and New Domains. ESWC 2016. Lecture Notes in Computer Science, vol 9678. Springer, Cham
With the rising interest in internet connected devices and sensor networks, better known as the Internet of Things, data streams are becoming ubiquitous. Integration and processing of these data streams is challenging. Semantic Web technologies are able to deal with the variety of data but are not able to deal with the velocity of the data. An emerging research domain, called stream reasoning, tries to bridge the gap between traditional stream processing and semantic reasoning. Research in the past years has resulted in several prototyped RDF Stream Processors, each of them with its own features and application domain. They all cover querying over RDF streams but lack support for complex reasoning. This paper presents how adaptive stream processing and context-awareness can be used to enhance semantic reasoning over streaming data. The result is a federated context-aware architecture that allows to leverage reasoning capabilities on data streams produced by distributed sensor devices. The proposed solution is stated by use cases in pervasive health care and smart cities.