Qualitative simulation predicts the set of possible behaviors...

It is possible to design cooperative work tools based only on "common sense " and good intuitions. But the history of technology is replete with examples of good theories greatly aiding the development of useful technology. Where, then, might we look for theories to help us design computer-supported cooperative work tools? In this paper, we will describe one possible perspective-the interdisciplinary study of coordination-that focuses, in part, on how people work together now and how they might do so differently with new information technologies. In one sense, there is little that is new about the study of coordination. Many different disciplines--including computer science, sociology, political science, management science, systems theory, economics, linguistics, and psychology--have all dealt, in one way or another, with fundamental questions about coordination. Furthermore, several previous writers have suggested that theories about coordination are likely to be important for designing cooperative work tools (e.g., Holt8], (Wino86]). We hope to suggest here, however, that the potential for fruitful interdisciplinary connections concerning coordination is much greater than has as yet been widely

In this paper we present a system that electromagnetically tracks the positions and orientations of multiple wireless objects on a tabletop display surface. The system offers two types of improvements over existing tracking approaches such as computer vision. First, the system tracks objects quickly and accurately without susceptibility to occlusion or changes in lighting conditions. Second, the tracked objects have state that can be modified by attaching physical dials and modifiers. The system can detect these changes in realtime. We present several new interaction techniques developed in the context of this system. Finally, we present two applications of the system: chemistry and system dynamics simulation. Keywords Tangible user interface, interactive surface, object tracking, two-handed manipulation, system dynamics, augmented reality

Abstract not found

Shortcomings of qualitative simulation and of quantitative simulation motivate combining them to do simulations exhibiting strengths of both. The resulting class of techniques is called semi-quantitative simulation. One approach to semi-quantitative simulation is to use numeric intervals to represent incomplete quantitative information. In this research we demonstrate semiquantitative simulation using intervals in an implemented semi-quantitative simulator called Q3. Q3 progressively refines a qualitative simulation, providing increasingly specific quantitative predictions which can converge to a numerical simulation in the limit while retaining important correctness guarantees from qualitative and interval simulation techniques. Q3's simulations are based on a technique we call step size refinement. While a pure qualitative simulation has a very coarse step size, representing the state of a system trajectory at relatively few qualitatively distinct states, Q3 interpolates newly expl...

For years, researchers and practitioners have primarily investigated the various processes within manufacturing supply chains individually. Recently, however, there has been increasing attention placed on the performance, design, and analysis of the supply chain as a whole. This attention is largely a result of the rising costs of manufacturing, the shrinking resources of manufacturing bases, shortened product life cycles, the leveling of the playing field within manufacturing, and the globalization of market economies. The objectives of this paper are to: (1) provide a focused review of literature in multi-stage supply chain modeling and (2) define a research agenda for future research in this area. supply chain, production, distribution, logistics 1

The paper introduces first insights into a methodology for developing eBusiness business models, which was elaborated at evolaris and is currently validated in various business cases. This methodology relies upon a definition of the term business model, which is first examined and upon which prerequisites for such a methodology are presented. A business model is based on a mental representation of certain aspects of the real world that are relevant for the business. Supporting this change of the mental model is therefore a major prerequisite for a methodology for developing business models. This paper demonstrates that it addition, a business model discussion should be theory based, able to handle complex systems, provide a way for risk free experiments and be practically applicable. In order to fulfill the above critieria, the evolaris methodology is grounded on system theory and combines aspects of system dynamics and action research.

The ability to identify and represent the knowledge that a human expert has about o particular domoin is a key method in the creation of an expert com-puter system. The first part of this paper demonstrates a methodology for collecting and onalyzing observations of experts at work, in order to find the conceptual framework used for the particular domain. The second part de-velops a representation for qualitative knowledge of the structure ond be-havior of a mechanism. The qualitative simulation, or envisionment, process is given a qualitative structural description of a mechanism and some initiali-zation information. and produces a detailed description of the mechanism’s behavior. The simulation process hos been fully implemented, and its results are shown for a particular diseose mechanisms in nephrology. This vertical slice of the construction of a cognitive model demonstrates an effective knowl-edge acquisition method for the purpose of determining the structure of the representation itself, not simply the content of the knowledge lo be encoded in that representation. Most importantly, it demonstrates the interaction among constraints derived from the textbook knowledge of the domain, from observations of the human expert, ond from the computational requirements of successful performance. 1.

This paper summarizes several classes of software cost estimation models and techniques: parametric models, expertise-based techniques, learning-oriented techniques, dynamics-based models, regression-based models, and composite-Bayesian techniques for integrating expertise-based and regression-based models. Experience to date indicates that neural-net and dynamics-based techniques are less mature than the other classes of techniques, but that all classes of techniques are challenged by the rapid pace of change in software technology. The primary conclusion is that no single technique is best for all situations, and that a careful comparison of the results of several approaches is most likely to produce realistic estimates. 1.

Abstract not found