Ontology-based metrics computation for business process analysis

Business Process Management (BPM) aims to support the whole life-cycle necessary to deploy and maintain business processes in organisations. Crucial within the BPM lifecycle is the analysis of deployed processes. Analysing business processes requires computing metrics that can help determining the health of business activities and thus the whole enterprise. However, the degree of automation currently achieved cannot support the level of reactivity and adaptation demanded by businesses. In this paper we argue and show how the use of Semantic Web technologies can increase to an important extent the level of automation for analysing business processes. We present a domain-independent ontological framework for Business Process Analysis (BPA) with support for automatically computing metrics. In particular, we define a set of ontologies for specifying metrics. We describe a domain-independent metrics computation engine that can interpret and compute them. Finally we illustrate and evaluate our approach with a set of general purpose metrics

SMILE: smart monitoring intelligent learning engine. An ontology-based context-aware system for supporting patients subjected to severe emergencies

Remote healthcare has made a revolution in the healthcare domain. However, an important problem this field is facing is supporting patients who are subjected to severe emergencies (as heart attacks) to be both monitored and protected while being at home. In this paper, we present a conceptual framework with the main objectives of: 1) emergency handling through monitoring patients, detecting emergencies and insuring fast emergency responses; 2) preventing an emergency from happening in the first place through protecting patients by organising their lifestyles and habits. To achieve these objectives, we propose a layered middleware. Our context model combines two modelling methods: probabilistic modelling to capture uncertain information and ontology to ease knowledge sharing and reuse. In addition, our system uses a two-level reasoning approach (ontology-based reasoning and Bayesian-based reasoning) to manage both certain and uncertain contextual parameters in an adaptive manner. Bayesian network is learned from ontology. Moreover, to ensure a more sophisticated decision-making for service presentation, influence diagram and analytic hierarchy process are used along with regular probabilistic rules (confidence level) and basic semantic logic rules