Modeling visualization controls for digital architecture and governance

Companies are continuously changing their strategy, processes, and information systems to benefit from the digital transformation. Controlling the digital architecture and governance is the fundamental goal. Enterprise Governance, Risk and Compliance (GRC) systems are vital for managing digital risks threatening in modern enterprises from many different angles. The most significant constituent to GRC systems is the definition of controls that is implemented on different layers of a digital Enterprise Architecture (EA). As part of the compliant aspect of GRC, the effectiveness of these controls is assessed and reported to relevant management bodies within the enterprise. In this paper, we present a metamodel which links controls to the affected elements of a digital EA and supplies a way of expressing associated assessment techniques and results. We complement a metamodel with an expository instantiation of a control compliance cockpit in an international insurance enterprise

Unternehmensphilosophie und Corporate Identity

SIGLEBibliothek Weltwirtschaft Kiel C 145029 / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman

M-ROSE: A Multi Robot Simulation Environment for Learning Cooperative Behavior

The development of high-performance autonomous multi robot control systems requires intensive experimentation in controllable, repeatable, and realistic robot settings. The need for experimentation is even higher in applications where the robots should automatically learn substantial parts of their controllers. We propose to solve such learning tasks as a three step process. First, we learn a simulator of the robots' dynamics. Second, we perform the learning tasks using the learned simulator. Third, we port the learned controller to the real robot and cross validate the performance gains obtained by the learned controllers. In this paper, we describe M-ROSE, our learning simulator, and provide empirical evidence that it is a powerful tool for learning of sophisticated control modules for real robots