Web service-based business process automation using matching algorithms

In this paper, we focus on two problems of the Web service-based business process integration: the discovery of Web services based on the capabilities and properties of published services, and the composition of business processes based on the business requirements of submitted requests. We propose a solution to these problems, which comprises multiple matching algorithms, a micro-level matching algorithm and macro-level matching algorithms. The solution from the macro-level matching algorithms is optimal in terms of meeting a certain business objective, e.g., minimizing the cost or execution time, or maximizing the total utility value of business properties of interest. Furthermore, we show how existing Web service standards, UDDI and BPEL4WS, can be used and extended to specify the capabilities of services and the business requirements of requests, respectively.Applications in Artificial Intelligence - Ontologies and Intelligent WebRed de Universidades con Carreras en Informática (RedUNCI

Web service-based business process automation using matching algorithms

In this paper, we focus on two problems of the Web service-based business process integration: the discovery of Web services based on the capabilities and properties of published services, and the composition of business processes based on the business requirements of submitted requests. We propose a solution to these problems, which comprises multiple matching algorithms, a micro-level matching algorithm and macro-level matching algorithms. The solution from the macro-level matching algorithms is optimal in terms of meeting a certain business objective, e.g., minimizing the cost or execution time, or maximizing the total utility value of business properties of interest. Furthermore, we show how existing Web service standards, UDDI and BPEL4WS, can be used and extended to specify the capabilities of services and the business requirements of requests, respectively.Applications in Artificial Intelligence - Ontologies and Intelligent WebRed de Universidades con Carreras en Informática (RedUNCI

Semantic Reasoning with Contextual Ontologies on Sensor Cloud Environment

This research first focused on processing enormous number of sensor events from a variety of city-wide sensor networks. As a solution, the well-known big data handling scheme, Hadoop cluster framework, drew, unquestionably, our attention. The acquired sensor events are to be used to immediately detect a certain abnormal situation within the framework. Accordingly, we integrated our existing context-aware collaboration framework with Hadoop cluster framework by interfacing data collection and context-aware reasoning parts of the existing framework with the Hadoop cluster framework. This approach enabled us to effectively process massive sensor events and semantically analyze the big data within the cluster environment. The proposed smart city sensor cloud framework provides ontology-enabled semantic reasoning scheme with the XOntology in combination with the Context-Aware Inference (CAI) model. By applying the ontology technology, the proposed framework enhances the availability and interoperability of the contextual information across many cooperating parties according to semantic reasoning results. Further, this framework is flexible enough to integrate any heterogeneous platforms including many existing IT solutions as well as mobile platforms. In addition, this approach presents the direction of progressive migration of many existing sensor network solutions into big data handling sensor cloud framework