Managing Requirement Volatility in an Ontology-Driven Clinical LIMS Using Category Theory. International Journal of Telemedicine and Applications

Requirement volatility is an issue in software engineering in general, and in Web-based clinical applications in particular, which often originates from an incomplete knowledge of the domain of interest. With advances in the health science, many features and functionalities need to be added to, or removed from, existing software applications in the biomedical domain. At the same time, the increasing complexity of biomedical systems makes them more difficult to understand, and consequently it is more difficult to define their requirements, which contributes considerably to their volatility. In this paper, we present a novel agent-based approach for analyzing and managing volatile and dynamic requirements in an ontology-driven laboratory information management system (LIMS) designed for Web-based case reporting in medical mycology. The proposed framework is empowered with ontologies and formalized using category theory to provide a deep and common understanding of the functional and nonfunctional requirement hierarchies and their interrelations, and to trace the effects of a change on the conceptual framework.Comment: 36 Pages, 16 Figure

Modeling Multi-Agent Systems with Category Theory

The rapidly growing complexity of integrating and monitoring computing systems is beyond the capabilities of even the most expert systems and software developers. The solution is systems must learn to monitor their own behaviors and conform to the requirements – a vision referred to as Autonomic Computing. Reactive Autonomic Systems Framework (RASF) is introduced for real-time reactive systems, which contain autonomic self-managing properties and are adaptive to their environments. The goal of this thesis is about modeling Multi-Agent Systems (MAS) with Category Theory (CAT). MAS is introduced as the realization of Reactive Autonomic Systems, and Jadex is used as a representation of MAS approach. This thesis respects Belief-Desire-Intension (BDI) agent architecture, models the entire Multi-Agent Systems (MAS), zooms into individual intelligent agent, analyzes the relationships among agent plans, goals and beliefs, and provides a fully formal CAT representation on MAS structure. Furthermore, this thesis proposes a formalization of fault-tolerance property of MAS using CAT

Multi-Agent Approach to Modeling and Implementing Fault-Tolerance in Reactive Autonomic Systems

Recently, autonomic computing has been proposed as a promising solution for software complexity in IT industry. As an autonomic approach, the Reactive Autonomic Systems Framework (RASF) proposes a formal modeling based on mathematical category theory, which addresses the self-* properties of reactive autonomic systems in a more abstract level. This thesis is about the specification and implementation of the reactive autonomic systems (RAS) through multi-agent approach by laying emphasis on the fault-tolerance property of RAS. Furthermore, this thesis proposes a model-driven approach to transform the RAS model to agent templates in multi-agent model using Extensible Stylesheet Language Transformation (XSLT). The multi-agent approach in this research is implemented by Jadex, a high-level Java-based agent programming language. The intelligent agents are created in Jadex based on the Belief-Desire-Intension (BDI) agent architecture. The approach is illustrated on a case study

A framework for analyzing changes in health care lexicons and nomenclatures

Ontologies play a crucial role in current web-based biomedical applications for capturing contextual knowledge in the domain of life sciences. Many of the so-called bio-ontologies and controlled vocabularies are known to be seriously defective from both terminological and ontological perspectives, and do not sufficiently comply with the standards to be considered formai ontologies. Therefore, they are continuously evolving in order to fix the problems and provide valid knowledge. Moreover, many problems in ontology evolution often originate from incomplete knowledge about the given domain. As our knowledge improves, the related definitions in the ontologies will be altered. This problem is inadequately addressed by available tools and algorithms, mostly due to the lack of suitable knowledge representation formalisms to deal with temporal abstract notations, and the overreliance on human factors. Also most of the current approaches have been focused on changes within the internal structure of ontologies, and interactions with other existing ontologies have been widely neglected. In this research, alter revealing and classifying some of the common alterations in a number of popular biomedical ontologies, we present a novel agent-based framework, RLR (Represent, Legitimate, and Reproduce), to semi-automatically manage the evolution of bio-ontologies, with emphasis on the FungalWeb Ontology, with minimal human intervention. RLR assists and guides ontology engineers through the change management process in general, and aids in tracking and representing the changes, particularly through the use of category theory. Category theory has been used as a mathematical vehicle for modeling changes in ontologies and representing agents' interactions, independent of any specific choice of ontology language or particular implementation. We have also employed rule-based hierarchical graph transformation techniques to propose a more specific semantics for analyzing ontological changes and transformations between different versions of an ontology, as well as tracking the effects of a change in different levels of abstractions. Thus, the RLR framework enables one to manage changes in ontologies, not as standalone artifacts in isolation, but in contact with other ontologies in an openly distributed semantic web environment. The emphasis upon the generality and abstractness makes RLR more feasible in the multi-disciplinary domain of biomedical Ontology change management