AN INFORMATION SYSTEM ARCHITECTURE TO SUPPORT AND TO PARTLY AUTOMATE PROCESS MODELLING : RETHINKING THE IDEA OF ARTIFICIAL NEURAL NETWORKS

Abstract: Process modelling is one of the extensively time consuming tasks within the fieldof process management. This thesis represents an alternative approach minimizing the effort for process modelling based on an intelligent, self-learning system.The current situation of process management in practise is the starting point forthe definition of the requirements which an appropriate information system architecture has to fulfil to solve the issues of contemporary process modelling. The current situation is described by a combination of theoretical considerations aboutcontemporary process management and a survey estimating the efforts needed tomodel and update process descriptions and diagrams out of several, very different documents. The identified requirements represent the groundwork for the derivation of manda-tory features and the architectural composition of the basic components of an intelligent software system and its integration in a process manager’s work. Moreover the effects of the integration of the defined intelligent software system on the social-technical interactions within organizations are sketched. Because text analysis and the transformation of texts into process diagrams crucially influences efficiency and effectivity in process management appropriate, established technologies are comparatively evaluated and discussed regarding theirability to reduce these efforts. According to the identified advantages and limitations of artificial neural networks within the qualitative comparison of the dif-ferent technologies a new concept was developed to fit the needs more holisticthan the existing concepts. The new concept consists of aspects of different established technologies, biological findings and philosophical thoughts about the impossibility of a physical representation of human mind. It results in a conceptof three-dimensional, spatiotemporal flexible, artificial neural networks, which change themselves permanently. A final qualitative comparison figures out thedifferences between the characteristics of existing concepts of artificial neural networks and determines the improvements and benefits the new concept of neural networks achieves.