Performance Analysis of a Semantics Enabled Service Registry

Service discovery is a critical task in service-oriented architectures such as the Grid and Web Services. In this paper, we study a semantics enabled service registry, GRIMOIRES, from a performance perspective. GRIMOIRES is designed to be the registry for myGrid and the OMII software distribution. We study the scalability of GRIMOIRES against the amount of information that has been published into it. The methodology we use and the data we present are helpful for researchers to understand the performance characteristics of the registry and, more generally, of semantics enabled service discovery. Based on this experimentation, we claim that GRIMOIRES is an efficient semantics-aware service discovery engine.

Performance Analysis of a Semantics Enabled Service Registry

Service discovery is a critical task in service-oriented architectures such as the Grid and Web Services. In this paper, we study a semantics enabled service registry, GRIMOIRES, from a performance perspective. GRIMOIRES is designed to be the registry for myGrid and the OMII software distribution. We study the scalability of GRIMOIRES against the amount of information that has been published into it. The methodology we use and the data we present are helpful for researchers to understand the performance characteristics of the registry and, more generally, of semantics enabled service discovery. Based on this experimentation, we claim that GRIMOIRES is an efficient semantics-aware service discovery engine.

A scenario-driven decision support system for serious crime investigation

Consideration of a wide range of plausible crime scenarios during any crime investigation is important to seek convincing evidence and hence to minimize the likelihood of miscarriages of justice. It is equally important for crime investigators to be able to employ effective and efficient evidence-collection strategies that are likely to produce the most conclusive information under limited available resources. An intelligent decision support system that can assist human investigators by automatically constructing plausible scenarios, and reasoning with the likely best investigating actions will clearly be very helpful in addressing these challenging problems. This paper presents a system for creating scenario spaces from given evidence, based on an integrated application of techniques for compositional modelling and Bayesian network-based evidence evaluation. Methods of analysis are also provided by the use of entropy to exploit the synthesized scenario spaces in order to prioritize investigating actions and hypotheses. These theoretical developments are illustrated by realistic examples of serious crime investigation.