By Andreas Tolk
This quantity includes a range of cutting-edge contributions to subject matters facing advanced platforms within the Knowledge-based surroundings. complicated platforms are ubiquitous. Examples contain, yet are usually not restricted to approach of platforms, Service-oriented techniques, Agent-based platforms, and complicated disbursed digital platforms. some of these are program domain names that require wisdom, engineering, and administration tools past the scope of conventional structures. The chapters during this e-book take care of a range of suitable themes, starting from uncertainty illustration and administration to using ontological potential in aid of large-scale enterprise integration.
All contributions have been invited in line with the certain attractiveness of the contributing authors of their box in the course of workshops and symposia. through bringing these kind of diversified features jointly in a single quantity, our rationale was once first to give various instruments to the reader in aid of his stories and paintings, and moment to teach how the various features offered within the chapters are complementary contributing in the direction of an rising self-discipline to deal with advanced platforms. the typical denominator of all chapters is using knowledge-based equipment, specifically ontological ability. The chapters are classified into conception contributions and sensible applications.
We wish that this quantity might help researchers, scholars, and practitioners in dealing with the demanding situations of integration, operation, and overview of complicated systems.
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Extra resources for Complex Systems in Knowledge-based Environments: Theory, Models and Applications
Example text
The first step of this simple parameter learning example is to enter the available information to the model in the form of findings (see box StarshipLenghInd Findings). ST8. 15 (box SSBN with Findings). In this example, the MFrags satisfy graph-theoretic conditions under which a re-structuring operation called finding absorption (Buntine 1994b) can be applied without changing the structure of the MFrags. Therefore, the prior distribution of the random variable GlobalAvgLength can be replaced by the posterior distribution obtained when adding evidence in the form of findings.
This chapter describes a layered approach in support of composability and interoperation in complex systems. This approach is neither complete nor exclusive. It is intended to help master students and practitioners in engineering disciplines and information technology to better use means of knowledge management to understand, describe, and manage complex systems in all life cycles. Furthermore, the approach supports evaluation, planning, and execution of integration projects. To this end, the chapter is divided into four sections.
The Levels of Conceptual Interoperability Model (LCIM) was developed to support this approach. The LCIM is a model, that represents a hierarchy of capability for representing the meaning (increasingly conceptual in nature, as the model layers are ascended) of information passed between systems, components, or services. It utilizes the experiences made with interoperability models used in the defense domain pointing into the direction that documentation beyond the technical aspects of interoperability are necessary to insure the interoperation of complex systems and systems of systems.