StreamJess: a stream reasoning framework for water quality monitoring

Abstract

Stream data knowledge bases modeled 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 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. The system also identifies the potential sources of pollution by also extending our ontology with the pollutants module. The solution utilizes C-SPARQL abilities to filter and aggregate RDF streams on windows to enable closed-world and time-aware reasoning with Jess rules. Moreover, Jess Tab functions are used to enable non-monotonic behavior.

Smart City Ontologies and Their Applications: A Systematic Literature Review

“…Ahmedi et al [94] proposed an ontology framework for water quality management that consists of four modules: the core ontology, including concepts for real-time observational water quality data; the regulations ontology, concerning permitted water parameter thresholds regulated by the authorities; the polluters ontology, representing polluters; and the water expert rules, representing if-then water expert rules.…”

Semdpa

“…In support of working with sensor data, semantic web has already been utilized to enable rich modelingandqueryingorevenreasoningoversensordataannotatedwithmeta-descriptionsinform ofontologies:In (Calbimonte,2011),theSSN 6 andSWEET 7 ontologiesareusedtomodelsensordata andtoallowafederatedquerysystemamongthem.In (Phuoc,2011),aLinkedStreamMiddleware (LSM)provideswrappersforrealtimedatacollectingandpublishing,awebinterfacetopublishdata andaSPARQL 8 endpointforqueryingsensordata.AspartoftheInwatersenseproject,in (Ahmedi, 2013),theINWS 9 ontologywhichbuildsontopoftheSSN 10 ontologymodelsWSNsforwaterquality monitoring,whereasin (Jajaga,2017aand (Jajaga,2017b),areasoningframework usesaJessproductionrulesystemoraSemanticWebrulelanguageC-SWRLrespectivelyoverthe INWS'sstreamsensordata.In (Keßler,2010),linkingsensordatausingLinked Dataprinciplesis seenaspromisingapproachinordertomakedataavailabletousersthatarenotinlinewithSWE standards.Eventhoughitmakesqueryingmoredifficult,byenablingannotationswithtimestamp andlocation,stillitmakesexplicitwhatmeta-datadescribes.…”

An Integrated Web Portal for Water Quality Monitoring through Wireless Sensor Networks

“…Finally, in another on-going work within our InWaterSenSe project 2 , an SSN-based (Compton et al 2012) ontology framework for water quality monitoring based on data originated from WSNs or manually collected water samples, is developed to enable the InWaterSenSe, an intelligent wireless sensor network for surface water quality monitoring (Ahmedi, 2013). The ontology can be paired with SWRL rules to infer new knowledge, and by using Water Framework Directive or any other local or international regulation ontology coupled with SWRL rules may be employed to reasoning over sensor data in order to classify water bodies and eventually identify sources of pollution.…”

“…The ontology can be paired with SWRL rules to infer new knowledge, and by using Water Framework Directive or any other local or international regulation ontology coupled with SWRL rules may be employed to reasoning over sensor data in order to classify water bodies and eventually identify sources of pollution. The InWaterSenSe ontology is only one building block of the overall InWaterSenSe system which we aim to provide to water experts and wide public interested in water quality, including the portal introduced in this paper (Ahmedi, 2013). An expert system capable to infer new implicit knowledge using rule-based system over the InWaterSenSe ontology and its water quality instances provided by sensors is further developed and presented in (Jajaga, 2015).…”