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

Web Data Extraction, Applications and Techniques: A Survey

Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of applications. Many approaches to extracting data from the Web have been designed to solve specific problems and operate in ad-hoc domains. Other approaches, instead, heavily reuse techniques and algorithms developed in the field of Information Extraction. This survey aims at providing a structured and comprehensive overview of the literature in the field of Web Data Extraction. We provided a simple classification framework in which existing Web Data Extraction applications are grouped into two main classes, namely applications at the Enterprise level and at the Social Web level. At the Enterprise level, Web Data Extraction techniques emerge as a key tool to perform data analysis in Business and Competitive Intelligence systems as well as for business process re-engineering. At the Social Web level, Web Data Extraction techniques allow to gather a large amount of structured data continuously generated and disseminated by Web 2.0, Social Media and Online Social Network users and this offers unprecedented opportunities to analyze human behavior at a very large scale. We discuss also the potential of cross-fertilization, i.e., on the possibility of re-using Web Data Extraction techniques originally designed to work in a given domain, in other domains.Comment: Knowledge-based System

Ontology-based patterns for the integration of business processes and enterprise application architectures

Increasingly, enterprises are using Service-Oriented Architecture (SOA) as an approach to Enterprise Application Integration (EAI). SOA has the potential to bridge the gap between business and technology and to improve the reuse of existing applications and the interoperability with new ones. In addition to service architecture descriptions, architecture abstractions like patterns and styles capture design knowledge and allow the reuse of successfully applied designs, thus improving the quality of software. Knowledge gained from integration projects can be captured to build a repository of semantically enriched, experience-based solutions. Business patterns identify the interaction and structure between users, business processes, and data. Specific integration and composition patterns at a more technical level address enterprise application integration and capture reliable architecture solutions. We use an ontology-based approach to capture architecture and process patterns. Ontology techniques for pattern definition, extension and composition are developed and their applicability in business process-driven application integration is demonstrated

Knowledge infrastructures for software service architectures

Software development has become a distributed, collaborative process based on the assembly of off-the-shelf and purpose-built components or services. The selection of software services from service repositories and their integration into software system architectures, but also the development of services for these repositories requires an accessible information infrastructure that allows the description and comparison of these services. General knowledge relating to software development is equally important in this context as knowledge concerning the application domain of the software. Both form two pillars on which the structural and behavioural properties of software services can be addressed. We investigate how this information space for software services can be organized. Focal point are ontologies that, in addition to the usual static view on knowledge, also intrinsically addresses the dynamics, i.e. the behaviour of software. We relate our discussion to the Web context, looking at the Web Services Framework and the Semantic Web as the knowledge representation framework