The state-of-the-art in the area of modelling of organisations is based on fixed metamodels. Due to rapid changing business requirements the complexity in developing applications which deliver business solutions is continually growing. To manage this complexity, environments providing flexible metamodelling capabilities instead of fixed metamodels has shown to be helpful. The main characteristic of such environments is that the formalism of modelling -the metamodel -can be freely defined and therefore be adapted to the problem under consideration. This paper gives an introduction into metamodelling concepts and presents a generic architecture for metamodelling platforms. Three best practice examples from industry projects applying metamodelling concepts in the area of business process modelling for e-business, e-learning, and knowledge management are presented. Finally, an outlook to future developments and research directions in the area of metamodelling is given.
This memorandum was originally published in German in the Zeitschrift für betriebswirtschaftliche Forschung (zfbf), Volume 62, pp. 662-672 and is translated and reprinted here with the kind permission of Fachverlag der Verlagsgruppe Handelsblatt GmbH. The authors would like to acknowledge the generous assistance of EJIS Senior Associate Editor Nicholas Romano who helped with the translation from the German.
Highlights• We propose a new paradigm for next generation enterprise information systems for the continuous alignment of business and IT for the agile enterprise.• The metamodelling approach supports both human-interpretable enterprise architecture models and machineinterpretable enterprise ontologies.• Semantic lifting transforms metamodels for the enterprise architectures into machine-interpretable enterprise ontologies.• Semantic metamodels express the semantics of all modelling concepts by an ontology. The ontology is extended by a metamodel, which defines the notation and syntax of the graphical modelling language.• Examples of next generation enterprise information systems are described, which embed modelling tools and algorithms for model analysis, identification of adaptation needs, and risk assessment.Abstract-The paper deals with Next Generation Enterprise Information Systems in the context of Enterprise Engineering. The continuous alignment of business and IT in a rapidly changing environment is a grand challenge for today's enterprises. The ability to react timeously to continuous and unexpected change is called agility and is an essential quality of the modern enterprise. Being agile has consequences for the engineering of enterprises and enterprise information systems. We propose a new paradigm for next generation enterprise information systems, which shifts the development approach of model-driven engineering to continuous adaptation of the agile enterprise. We propose a metamodeling approach, which supports both humaninterpretable representations, i.e. graphical models, and machine-interpretable representations, namely enterprise ontologies. Furthermore, we describe next generation enterprise information systems, which embed modeling tools and algorithms for model analysis.Keywords-Enterprise Engineering, Enterprise Architecture, Enterprise Ontology, Metamodeling Topic-Engineering the agile enterprise, embedding enterprise architecture and enterprise ontology into information systems 2
Current workflow management systems (WFMS) offer little aid for the acquisition of workflow models and their adaptation to changing requirements. To support these activities we propose to integrate machine learning and workflow management. This enables an inductive approach to workflow acquisition and adaptation by processing traces of manually enacted workflows. We present a machine learning component that combines two different machine learning algorithms. In this paper we focus mainly on the first one, which induces the structure of the workflow, based on the induction of hidden markov models. The second algorithm, a standard decision rule induction algorithm, induces transition conditions. The main concepts have been implemented in a prototype, which we have validated using artificial process traces. The induced workflow models can be imported by the business process management system ADONIS 1 .
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