Model driven validation approach for enterprise architecture and motivation extensions

As the endorsement of Enterprise Architecture (EA) modelling continues to grow in diversity and complexity, management of its schema, artefacts, semantics and relationships has become an important business concern. To maintain agility and flexibility within competitive markets, organizations have also been compelled to explore ways of adjusting proactively to innovations, changes and complex events also by use of EA concepts to model business processes and strategies. Thus the need to ensure appropriate validation of EA taxonomies has been considered severally as an essential requirement for these processes in order to exert business motivation; relate information systems to technological infrastructure. However, since many taxonomies deployed today use widespread and disparate modelling methodologies, the possibility to adopt a generic validation approach remains a challenge. The proliferation of EA methodologies and perspectives has also led to intricacies in the formalization and validation of EA constructs as models often times have variant schematic interpretations. Thus, disparate implementations and inconsistent simulation of alignment between business architectures and heterogeneous application systems is common within the EA domain (Jonkers et al., 2003). In this research, the Model Driven Validation Approach (MDVA) is introduced. MDVA allows modelling of EA with validation attributes, formalization of the validation concepts and transformation of model artefacts to ontologies. The transformation simplifies querying based on motivation and constraints. As the extended methodology is grounded on the semiotics of existing tools, validation is executed using ubiquitous query language. The major contributions of this work are the extension of a metamodel of Business Layer of an EAF with Validation Element and the development of EAF model to ontology transformation Approach. With this innovation, domain-driven design and object-oriented analysis concepts are applied to achieve EAF model’s validation using ontology querying methodology. Additionally, the MDVA facilitates the traceability of EA artefacts using ontology graph patterns

10301 Executive Summary and Abstracts Collection -- Service Value Networks

From 25.07.2010 to 30.07.2010, the Perspectives Workshop 10301 ``Perspectives Workshop: Service Value Networks \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

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Decision-enabled dynamic process management for networked enterprises

In todays networked economy face numerous information management challenges, both from a process management perspective as well as a decision support perspective. While there have been significant relevant advances in the areas of business process management as well as decision sciences, several open research issues exist. In this paper, we highlight the following key challenges. First, current process modeling and management techniques lack in providing a seamless integration of decision models and tools in existing business processes, which is critical to achieve organizational objectives. Second, given the dynamic nature of business processes in networked enterprises, process management approaches that enable organizations to react to business process changes in an agile manner are required. Third, current state-of-the-art decision model management techniques are not particularly amenable to distributed settings in networked enterprises, which limits the sharing and reuse of models in different contexts, including their utility within managing business processes. In this paper, we present a framework for decision-enabled dynamic process management that addresses these challenges. The framework builds on computational formalisms, including the structured modeling paradigm for representing decision models, and hierarchical task networks from the artificial intelligence (AI) planning area for process modeling. Within the framework, interleaved process planning (modeling), execution and monitoring for dynamic process management throughout the process lifecycle is proposed. A service-oriented architecture combined with advances from the semantic Web field for model management support within business processes is proposed

Enterprise architecture for small and medium-sized enterprises : CHOOSE

Enterprise architecture (EA) is a coherent whole of principles, methods, and models that are used in the design and realization of an enterprise’s organizational structure, business processes, information systems, and IT infrastructure. EA is used as a holistic approach to keep things aligned in a company. Some emphasize the use of EA to align IT with the business, others see it broader and use it to also keep the processes aligned with the strategy. Recent research indicates the need for EA in small and medium-sized enterprises (SMEs), important drivers of the economy, as they struggle with problems related to a lack of structure and overview of their business. However, existing EA frameworks are perceived as too complex and, to date, none of the EA approaches are sufficiently adapted to the SME context. Therefore, in this PhD, we present the CHOOSE approach for EA for SMEs. The approach consists of four artifacts: a metamodel, a method, software tool support, and a visualization. The approach is kept simple so that it may be applied in an SME context and is based on the essential dimensions of EA frameworks. Five steps were taken: first, the problem of EA in SMEs was extensively analyzed. Next, the CHOOSE metamodel was developed during action research in SMEs. Then, action research in six companies was used to develop an adequate method (consisting of guidelines, a roadmap, and stop criteria) and to further refine this CHOOSE metamodel, while different types of software tools (PC, iPad, Android, ...) were developed to enable the evaluation rounds. Finally, a proper visualization was established

Knowledge-based Engineering in Product Development Processes - Process, IT and Knowledge Management perspectives

Product development as a field of practice and research has significantly changed due to the general trends of globalization changing the enterprise landscapes in which products are realized. The access to partners and suppliers with high technological specialization has also led to an increased specialization of original equipment manufacturers (OEMs). Furthermore, the products are becoming increasingly complex with a high functional and technological content and many variants. Combined with shorter lifecycles which require reuse of technologies and solutions, this has resulted in an overall increased knowledge intensity which necessitates a more explicit approach towards knowledge and knowledge management in product development. In parallel, methods and IT tools for managing knowledge have been developed and are more accessible and usable today. One such approach is knowledge-based engineering (KBE), a term that was coined in the mid-1980s as a label for applications which automate the design of rule-driven geometries. In this thesis the term KBE embraces the capture and application of engineering knowledge to automate engineering tasks, regardless of domain of application, and the thesis aims at contributing to a wider utilization of KBE in product development (PD). The thesis focuses on two perspectives of KBE; as a process improvement IT method and as a knowledge management (KM) method. In the first perspective, the lack of explicit regard for the constraints of the product lifecycle management (PLM) architecture, which governs the interaction of processes and IT in PD, has been identified to negatively affect the utilization of KBE in PD processes. In the second perspective, KM theories and models can complement existing methods for identifying potential for KBE applications.Regarding the first perspective, it is concluded that explicit regard for the PLM architecture decreases the need to develop and maintain software code related to hard coded redundant data and functions in the KBE application. The concept of service oriented architecture (SOA) has been found to enable an the explicit regard for the PLM architecture.. Regarding the second perspective, it is concluded that potential for KBE applications is indicated by: 1.) application of certain types of knowledge in PD processes 2.) high maturity and formalization of the applied knowledge 3.) a codification strategy for KM and 4.) an agreement and transparency regarding how the knowledge is applied, captured and transferred. It is also concluded that the formulation of explicit KM strategies in PD should be guided by knowledge application and its relation to strategic objectives focusing on types of knowledge, their role in the PD process and the methods and tools for their application. These, in turn, affect the methods and tools deployed for knowledge capture in order for it to integrate with the processes of knowledge origin. Finally, roles and processes for knowledge transfer have to be transparent to assure the motivation of individuals to engage in the KM strategy