Provenance: The Bridge Between Experiments and Data

Current scientific applications are often structured as workflows and rely on workflow systems to compile abstract experiment designs into enactable workflows that utilise the best available resources. The automation of this step and of the workflow enactment, hides the details of how results have been produced. Knowing how compilation and enactment occurred allows results to be reconnected with the experiment design. We investigate how provenance helps scientists to connect their results with the actual execution that took place, their original experiment and its inputs and parameters.

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.

Efficient Multi-Granularity Service Composition

Dynamic composition of services provides the ability to build complex distributed applications at run time by combining existing services, thus coping with a large variety of complex requirements that cannot be met by individual services alone. However, with the increasing amount of available services that differ in granularity (amount of functionality provided) and qualities, selecting the best combination of services becomes very complex. In response, this paper addresses the challenges of service selection, and makes a twofold contribution. First, a rich representation of compositional planning knowledge is provided, allowing the expression of multiple decompositions of tasks at arbitrary levels of granularity. Second, two distinct search space reduction techniques are introduced, the application of which, prior to performing service selection, results in significant improvement in selection performance in terms of execution time, which is demonstrated via experimental results.