Title: StreamJess: a stream reasoning framework for water quality monitoring

Authors: Edmond Jajaga; Lule Ahmedi; Figene Ahmedi

Addresses: Department of Computer Science, South East European University, Tetovë, Macedonia ' Department of Computer Engineering, University of Prishtina, Prishtinë, Kosova ' Department of Hydro-Technic, University of Prishtina, Prishtinë, Kosova

Abstract: Stream data knowledge bases modelled with OWL are a proved natural approach. But, querying and reasoning over these knowledge bases is not supported with standard Semantic Web technologies like SPARQL and SWRL. Query processing systems enable querying, but to the best of our knowledge, Semantic Web rules are still unable to handle the required reasoning features for effective inference over stream data, i.e. non-monotonic, closed-world and time-aware reasoning. In absence of such a system, we showed in our previous work how Jess can be used for monitoring water quality, but by bringing input data manually. In this paper, we enable stream data support and thus a timely detection of faulty water quality statuses by also extending our ontology with the pollutants module. C-SPARQL abilities to filter and aggregate RDF streams are utilised to enable closed-world and time-aware reasoning with Jess rules. Moreover, Jess Tab functions are used to enable non-monotonic behaviour.

Keywords: stream data; expert system; reasoning; Jess; rules; ontologies; water quality monitoring; SWRL; surface water classification; SPARQL; sources of water pollution; Rete.

DOI: 10.1504/IJMSO.2016.083507

International Journal of Metadata, Semantics and Ontologies, 2016 Vol.11 No.4, pp.207 - 220

Received: 17 Jun 2016
Accepted: 04 Nov 2016

Published online: 07 Apr 2017 *

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