Conceptual modeling for the design of intelligent and emergent information systems

Abstract

Abstract-A key requirement to today's fast changing economic environment is the ability of organizations to adapt dynamically in an effective and efficient manner. Information and Communication Technologies play a crucially important role in addressing such adaptation requirements. The notion of 'intelligent software' has emerged as a means by which enterprises can respond to changes in a reactive manner but also to explore, in a pro-active manner, possibilities for new business models. The development of such software systems demands analysis, design and implementation paradigms that recognize the need for 'co-development' of these systems with enterprise goals, processes and capabilities. The work presented in this paper is motivated by this need and to this end it proposes a paradigm that recognizes co-development as a knowledge-based activity. The proposed solution is based on a multi-perspective modeling approach that involves (i) modeling key aspects of the enterprise, (ii) reasoning about design choices and (iii) supporting strategic decision-making through simulations. The utility of the approach is demonstrated though a case study in the field of marketing for a start-up company.

Toward an Adaptive Enterprise Modelling Platform

“…Conceptual modelling (CM) became a basis for enterprise modelling [6][7][8], simulation [9], and information systems development [10][11][12]. In information systems research CM is used to describe behavioural, structural, informational or functional aspects of a system.…”

“…Sandkuhl et al [32]). Another area which has also received attention is that which is relevant to architecture patterns [33], whereby an analyst can orchestrate enterprise models from previously defined patterns, and thus speed up the modelling process and deployment [10]. Patterns can be structural, behavioural, functional, constraining, viewpoints-related, or can be a value proposition.…”

“…BPSim can interchange and parameterize business-process analysis data to apply KPIs better, predict business performance, validate process design, allocate resources, and reduce overall operational risk [34]. Simulation can cover both discrete-event [35] and continuous dynamic [10] simulations. Other simulation techniques such as agentbased simulation, graph transformation, and network analysis can be used for different simulation purposes.…”

Piloting Experiential Learning through 360° Video and 3d Printing to Improve System Modelling

“…When using UML, learners are challenged to conceptualize a problem domain or narrative for better modelling decisions [27]. Some of the difficulties of understanding the system itself can lead to disconnection and therefore learning disengagement [11].…”

The smart waste collection routing problem: Alternative operational management approaches

“…With this in mind, companies are developing the capabilities needed to explore the use of Information and Communication Technologies (ICT). These will potentiate higher efficiency and a better effectiveness of their operations (Daugherty et al, 2005;Oliveira et al, 2015), while enhancing the organizations' adjustment to today's dynamic environment (Fayoumi and Loucopoulos, 2016). In this set, the necessary conditions to develop dynamic expert systems able to explore real-time information to inform the organizations' practice and improve their operational decision-making are available.…”