Service registration and discovery are functionalities central to any service-oriented architecture, and they are often provided by centralized entities in today's systems. However, there are advantages of scalability, robustness, as well as distribution of control and cost by further decentralization of these functionalities to all the participants in the system. Peer-to-peer networks are great enablers toward this goal as they are designed to be scalable and autonomic; redundancy and automatic reconfiguarion are built into these systems, enabling peers to form and maintain the network autonomously. This article describes a fully decentralized service directory infrastructure built on top of the peer-to-peer protocol Chord. Service registration is performed implicitly by embedding semantic information into the peer identifiers, grouping peers by service categories and forming islands on the ring topology. Service discovery is performed by sending queries and anycast messages to peers registered in the appropriate islands. The routing protocol is further modified to take advantage of the island topology, with reputation mechanism and multi-path routing implemented to avoid the threat of misbehaving peers dropping transit messages in the system. Simulations were performed to assess the efficacy of both the new routing scheme and misbehavior avoidance.

This paper presents an algorithm for viewpoint-based similarity discernment of linguistic concepts on Semantic Network Array Processor (SNAP). The viewpoint-based similarity discernment plays a key role in retrieving similar propositions. This is useful for advanced knowledge processing areas such as analogical reasoning and case-based reasoning. The algorithm assumes that a knowledge base is constructed for SNAP, based on information acquired from the WordNet linguistic database. The algorithm identifies paths on the knowledge base between each given concept and a given viewpoint concept, then computes a similarity degree between the two concepts based on the number of nodes shared by the paths. A small scale knowledge base was constructed and an experiment was conducted on a SNAP simulator that demonstrated the feasibility of this algorithm. Because of SNAP's scalability, the algorithm is expected to work similarly on a large scale knowledge base.

We study the correspondence between problem descriptions and requirements specification documents derived from them. Based on the results of this investigation, a model that integrates the problem space and the requirements specification space is developed. This integration is based on a semantic network representation. We also propose a model of the requirements elicitation process that is consistent with our empirical studies of traceability in requirements documents. In this process, analysts derived requirements specifications from incomplete and ambiguous problem descriptions given by customers, identify missing information, completed it, and then decide the system boundaries that define which part of the problem descriptions to implement as the target system. The model can be used to complete problem descriptions given by customers and determine the system boundaries.