Thinking outside the TBox multiparty service matchmaking as information retrieval

Service oriented computing is crucial to a large and growing number of computational undertakings. Central to its approach are the open and network-accessible services provided by many different organisations, and which in turn enable the easy creation of composite workflows. This leads to an environment containing many thousands of services, in which a programmer or automated composition system must discover and select services appropriate for the task at hand. This discovery and selection process is known as matchmaking. Prior work in the field has conceived the problem as one of sufficiently describing individual services using formal, symbolic knowledge representation languages. We review the prior work, and present arguments for why it is optimistic to assume that this approach will be adequate by itself. With these issues in mind, we examine how, by reformulating the task and giving the matchmaker a record of prior service performance, we can alleviate some of the problems. Using two formalisms—the incidence calculus and the lightweight coordination calculus—along with algorithms inspired by information retrieval techniques, we evolve a series of simple matchmaking agents that learn from experience how to select those services which performed well in the past, while making minimal demands on the service users. We extend this mechanism to the overlooked case of matchmaking in workflows using multiple services, selecting groups of services known to inter-operate well. We examine the performance of such matchmakers in possible future services environments, and discuss issues in applying such techniques in large-scale deployments

Quantitative Agent Service Matching

Abstract. The ultimate goal of service matching is to find the service provider(s) that can perform tasks of given description with the best overall degree of satisfaction. However, service description matching solves only part of the problem. Agents that match a given service request description may vary greatly in the level and/or quality of services that they can perform and an agent may have strong and weak areas in its advertised service space. In this work, we take a quantitative approach in which the broker agent (match maker) considers performance rating an integral part of an agent’s capability model, captures an agent’s strong and weak areas through the interaction with the agents, and refines the agent’s capability model with the information gathered. The broker agent also dynamically builds a service distribution model that can provide vital information for determining the degree of match in cases of non-exact matches. An experimental system has been designed and implemented using OWL-S as the upper service ontology and the result statistics show significant advantage over other major levels of brokers. 1