<p>In knowledge-based engineering, the inference engine plays an important part in the behaviour of the system. A flexible and adaptive execution scheme allows the designer to experiment with different modes of operation and selecting an appropriate one with respect to the initial data set and the execution goal.</p><p>In this project, an extension of an existing research prototype software in the field of knowledge-based engineering will be developed, with the goal of building a reliable and easy to use dependency resolution engine that will replace a less-than-ideal current implementation of the same. A discussion will be included how the knowledge concepts and objects can be represented in an abstract mathematical form, converting at the same time the problem of dependency resolution to a more formally specified one in terms of the data abstraction proposed. Some algorithms and methods that are used to operate on the data set will be discussed from both a theoretical and programming point of view, analysing their complexity, proposing and testing their implementation. Graphical interface controls that can be used to visualize and understand easily the relations in the available knowledge base will be also demonstrated.</p><p>The testing and verification of the resulting software will be presented, comparing its behaviour against reference tools serving similar purposes. Methods for validating the consistency of the knowledge base will also be discussed. Finally, the integration of the newly-developed code within the context of the prototype will be discussed, commenting on the new features and functionality gained.</p>

In knowledge-based engineering, the inference engine plays an important part in the behaviour of the system. A flexible and adaptive execution scheme allows the designer to experiment with different modes of operation and selecting an appropriate one with respect to the initial data set and the execution goal. In this project, an extension of an existing research prototype software in the field of knowledge-based engineering will be developed, with the goal of building a reliable and easy to use dependency resolution engine that will replace a less-than-ideal current implementation of the same. A discussion will be included how the knowledge concepts and objects can be represented in an abstract mathematical form, converting at the same time the problem of dependency resolution to a more formally specified one in terms of the data abstraction proposed. Some algorithms and methods that are used to operate on the data set will be discussed from both a theoretical and programming point of view, analysing their complexity, proposing and testing their implementation. Graphical interface controls that can be used to visualize and understand easily the relations in the available knowledge base will be also demonstrated. The testing and verification of the resulting software will be presented, comparing its behaviour against reference tools serving similar purposes. Methods for validating the consistency of the knowledge base will also be discussed. Finally, the integration of the newly-developed code within the context of the prototype will be discussed, commenting on the new features and functionality gained.

A comprehensive failure detection model is proposed to detect service failures in asynchronous distributed systems. The proposed model takes advantage of P2P technology to provide required functionality. When compared to similar studies in failure detection, the presented failure detection model is more autonomous in resolving service dependencies, embodies more flexibility in providing different failure detection functions (like unreliable failure detectors, membership services) and offers more security. A failure detection library is developed using JXTA P2P framework to show realization of such a model.