f-SPARQL extension and application to support context recognition

• Published in: 2012 IEEE International Conference on Fuzzy Systems pp. 1 - 8
• Main Authors: De Maio, C., Fenza, G., Furno, D., Loia, V.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2012
• Subjects: Computer architecture; Context; context aware computing; Context-aware services; Database languages; f-SPARQL; fuzzy classification; fuzzy clustering; semantic sensor data; semantic web; Semantics; Sensors; Training
• ISBN: 1467315079; 9781467315074
• ISSN: 1098-7584
• DOI: 10.1109/FUZZ-IEEE.2012.6251224

Context aware computing as well as wearable and ubiquitous computing often attain with pattern recognition on incoming sensor data. Recognizing more (useful) contexts requires more information about the context, and thus more sensors and better recognition algorithms. In order to enable logic inference on incoming data, the proposed work assumes that incoming data are represented by means of semantic languages (e.g., RDF, OWL, etc.). Nevertheless, in a context aware computing purely logic-based reasoning on context may not be enough. So, the work introduces soft computing techniques to approximate context recognition. Specifically, this paper introduces an approach to context analysis and recognition that relies on f-SPARQL[1] tool, that is a flexible extension of SPARQL. In particular, in this work a JAVA implementation of f-SPARQL and the integrated support for fuzzy clustering and classification are discussed. This tool is exploited in the architecture that foresees some task oriented agents in order to achieve context analysis and recognition in order to identify critical situations. Finally, a simple application scenario and preliminary experimental results have been described.