International Journal of Software Engineering and Knowledge Engineering

This paper proposes a formal representation of modeling languages based on category theory. These languages are generally described by "metamodels", i.e. structures composed by classes and relations, and related by "transformations". Thus, this paper studies how the key categorical concepts such as functors and relations between functors (called natural transformations) can be used for equational reasoning about modeling artifacts (models, metamodels, transformations). As a result, this paper proposes a formal point of view of models usable to specify/prove equivalence between models or transformations (with an application to refactoring).

Now, with the development of intelligent service technology, information provisioning models are changing from simple, single-type models to complex, on-demand models as required. Remote sensing analysis modeling often requires the use of multiple data sources, computer resources, tools, and models, and couples these resources into a workflow for global change research. Therefore, remote sensing analysis modeling research today faces a critical challenge: the registry, discovery, and allocation of modeling resources are complex, as they are heterogeneous and geographically distributed. This paper proposes a resource package-oriented approach that provides guidelines for remote sensing analysis modeling to increase model sharing and integrating. This unified wrapping approach can be used to make the management and use of existing resources becoming easy and reusable. Based on this description of construction method, different remote sensing analysis resources can be combined together on-demand. We developed a web environment to compose different remote sensing modeling resources as on-demand processing components, based on the existing workflow engineering, and use a dust storm monitoring model as an example. Comparative analyses are given to further explain the applications of this hierarchical construction method in the areas of the remote sensing analysis model.

Nowadays, it happens very often that in order to make a decision we rely on knowledge originating from different and autonomous sources, i.e. from experts or the Internet. For many problems we often ask for an opinion of not one but a group of sources (a collective). It may be due to the fact that we collect very large amount of information which might be inconsistent unfortunately. The process of appointing the knowledge state of a collective (final and consistent opinions) is often very complex and time-consuming. Therefore, we want to know if it is possible to determine a common and reliable opinion of a collective only by analyzing given knowledge of a collective. For this task the consensus theory is applied. This work is devoted to analyses of a susceptibility to a consensus for a few types of representations of experts' opinions (knowledge of a collective) such as a number, a binary vector, ordered partitions or ordered coverings.

In this paper, we present a logical framework to facilitate users in assessing a software system in terms of the required survivability features. Survivability evaluation is essential in linking foreign software components to an existing system or obtaining software systems from external sources. It is important to make sure that any foreign components/systems will not compromise the current system's survivability properties. Given the increasing large scope and complexity of modern software systems, there is a need for an evaluation framework to accommodate uncertain, vague, or even ill-known knowledge for a robust evaluation based on multi-dimensional criteria. Our framework incorporates user-defined constrains on survivability requirements. Necessity-based possibilistic uncertainty and user survivability requirement constraints are effectively linked to logic reasoning. A proof-of-concept system has been developed to validate the proposed approach. To our best knowledge, our work is the first attempt to incorporate vague, imprecise information into software system survivability evaluation.

"The new source of power is not money in the hands of a few, but information in the hands of many." The aforementioned quote from John Naisbitt seems to be even more relevant in the world of finance at this very moment. Many financial decisions come from watching the information stream, selecting relevant data, analyzing it and acting accordingly. With the increasing global competition, the need for swift data analysis, high accuracy and quality becomes a must. XBRL (Extensible Business Reporting Language)XBRL: http://www.xbrl.org/ standard was proposed to improve efficiency of data exchange in the financial domain. However; it is still struggling with interoperability problems, not to mention comparability of data or multisource data integration. This paper presents the FLORA intelligent platform: an approach for dealing with current financial information shortcomings and achieving more effective way of processing financial data based on the Linked Data principles. The article also explains the process of data extraction and semantic modeling which are the cornerstones of efficient financial data analysis. As a result, the FLORA architecture facilitates effective, data-driven, financial analyses and Web-scale integration between financial applications and platforms.

In this paper an example of a hierarchical context-aware run-time reconfigurable control system is presented. The context-awareness is resulting from using policy-based computing as a technology allowing the control system to replace its decision making logic in run-time in response to changing environment conditions.

The proposed solution allows system experts to specify policies (AGILE policies) used in the Supervision Layer for the purpose of making decisions regarding the most appropriate controller configuration and on the other side, they can specify policies (Fuzzy Logic policies) used in the Control Layer in order to generate control signals allowing to achieve specified control goals.

Novelty of the proposed solutions lays in combination of two technologies, Open Decision Point technology originating from the Software Engineering domain Policy-based Computing that is originating from the Knowledge Engineering domain in application to non-linear control systems.