The Art of Creating Models and Models Integration
The sciences do not try to explain, they hardly even try to interpret, they mainly make models. By a model is meant a mathematical construct which, with the addition of certain verbal interpretations, describes observed phenomena. The justification of suc
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The sciences do not try to explain, they hardly even try to interpret, they mainly make models. By a model is meant a mathematical construct which, with the addition of certain verbal interpretations, describes observed phenomena. The justification of such a mathematical construct is solely and precisely that it is expected to work. John von Neumann (1903 - 1957)
1 Introduction The art of abstracting from physical or virtual objects and behaviors for the development of system models is critical to a variety of applications ranging from business processes to computers and communication. As computer and communication systems begin to pervade all walks of lives the question of design, performance and dependability evaluation of such systems proves to be increasingly important. Today's challenge is to develop models that can, not only give a qualitative understanding of ever more complex and diverse phenomena and systems, but can also aid in a quantitative assessment of functional and non-functional properties. System modeling should help to understand the functionality and properties of the system and models are used for development and communication with other developers and users [1]. Different models present the system from different perspectives • • • •
Structural perspective describing the system organization or data structure Behavioral perspective showing the behavior of the system Hierarchical perspective depicting the system’s hierarchical organization to cope with complexity External perspective reflecting the system’s context or environment.
Good models: speak to imagination, are easy to visualize and remember. A beautiful example is a dining philosophers problem in which the problem of concurrency can easily be understood. Good models address the problem at hand and provide useful, preferably quantifiable insight. Leading role and popularity of physics is to a large extent due to models that general public can grasp such as atomic model. Computer scientists, especially software engineers claim to be able to solve almost all specified problems. On the other hand physicists deal with physical world and model the world around us. Software engineers create artifacts and try to solve most problems while neglecting limits and frequently lacking understanding. Computer hardware is mainly R.-D. Kutsche and N. Milanovic (Eds.): MBSDI 2008, CCIS 8, pp. 1–7, 2008. © Springer-Verlag Berlin Heidelberg 2008
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M. Malek
designed by professionals while software is written by a broad spectrum of population ranging from experts to dilettantes. This state of affairs poses a number of challenges. In this paper we give first a historical perspective how we have got to the state that we are in at present and point out major problems and challenges with the state-of-the-art models in general as well as their integration by using a specific example of a model for failure prediction.
2 Historical Perspective Modeling was around since the beginning of times. The first traceable abstractions of reality were numbers and this pr
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