TY - GEN
T1 - Ontologies, rules, workflow and predictive models
T2 - 6th Biennial Meeting of the International Environmental Modelling and Software Society: Managing Resources of a Limited Planet, iEMSs 2012
AU - Sottara, D.
AU - Bragaglia, S.
AU - Mello, P.
AU - Pulcini, D.
AU - Luccarini, L.
AU - Giunchi, D.
PY - 2012/12/1
Y1 - 2012/12/1
N2 - Given the complexity of Waste-Water Treatment Plants (WWTPs), both from the environmental, legal and economic point of view, Environmental Decision Support Systems (E-DSSs) are getting wider adoption to monitor and manage the plants in real time. From a cognitive perspective, the knowledge required by an E-DSS may be encoded in different forms. In this paper, we argue that the operational domain and its most relevant concepts should be defined in a proper ontology, providing a vocabulary to encode inferential or operational knowledge in the form of decision-making rules. The rules process information extracted from data, acquired through sensors and possibly processed using predictive or analytic models. Eventually, the rules themselves and the actions they recommend, can be orchestrated as business processes, using workflow models. Moreover, we argue that standard formats should be used to facilitate the formalisation and exchange of knowledge between different systems, including OWL 2 (ontologies), RIF/RuleML (rules), BPMN 2.0 (workflows) and PMML (predictive models). Finally, we present a use case modelling a periodic plant monitoring routine which is necessary to check that the plant emissions are compliant with the national legislation. The system, implemented using the open source Knowledge Integration Platform Drools, exploits a hybrid knowledge base but relies on a unified data model and execution environment.
AB - Given the complexity of Waste-Water Treatment Plants (WWTPs), both from the environmental, legal and economic point of view, Environmental Decision Support Systems (E-DSSs) are getting wider adoption to monitor and manage the plants in real time. From a cognitive perspective, the knowledge required by an E-DSS may be encoded in different forms. In this paper, we argue that the operational domain and its most relevant concepts should be defined in a proper ontology, providing a vocabulary to encode inferential or operational knowledge in the form of decision-making rules. The rules process information extracted from data, acquired through sensors and possibly processed using predictive or analytic models. Eventually, the rules themselves and the actions they recommend, can be orchestrated as business processes, using workflow models. Moreover, we argue that standard formats should be used to facilitate the formalisation and exchange of knowledge between different systems, including OWL 2 (ontologies), RIF/RuleML (rules), BPMN 2.0 (workflows) and PMML (predictive models). Finally, we present a use case modelling a periodic plant monitoring routine which is necessary to check that the plant emissions are compliant with the national legislation. The system, implemented using the open source Knowledge Integration Platform Drools, exploits a hybrid knowledge base but relies on a unified data model and execution environment.
KW - Environmental decision support systems
KW - Hybrid systems
KW - Knowledge-base systems
KW - Waste- water treatment plants
UR - http://www.scopus.com/inward/record.url?scp=84894153207&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84894153207&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84894153207
SN - 9788890357428
T3 - iEMSs 2012 - Managing Resources of a Limited Planet: Proceedings of the 6th Biennial Meeting of the International Environmental Modelling and Software Society
SP - 204
EP - 211
BT - iEMSs 2012 - Managing Resources of a Limited Planet
Y2 - 1 July 2012 through 5 July 2012
ER -