Utilising Provenance to Enhance Social Computation

Postprin

PLACES'10: The 3rd Workshop on Programmng Language Approaches to concurrency and Communication-Centric Software

Paphos, Cyprus. March 201

Patterns-based Evaluation of Open Source BPM Systems: The Cases of jBPM, OpenWFE, and Enhydra Shark

In keeping with the proliferation of free software development initiatives and the increased interest in the business process management domain, many open source workflow and business process management systems have appeared during the last few years and are now under active development. This upsurge gives rise to two important questions: what are the capabilities of these systems? and how do they compare to each other and to their closed source counterparts? i.e. in other words what is the state-of-the-art in the area?. To gain an insight into the area, we have conducted an in-depth analysis of three of the major open source workflow management systems - jBPM, OpenWFE and Enhydra Shark, the results of which are reported here. This analysis is based on the workflow patterns framework and provides a continuation of the series of evaluations performed using the same framework on closed source systems, business process modeling languages and web-service composition standards. The results from evaluations of the three open source systems are compared with each other and also with the results from evaluations of three representative closed source systems - Staffware, WebSphere MQ and Oracle BPEL PM, documented in earlier works. The overall conclusion is that open source systems are targeted more toward developers rather than business analysts. They generally provide less support for the patterns than closed source systems, particularly with respect to the resource perspective which describes the various ways in which work is distributed amongst business users and managed through to completion

Model Exploration Using OpenMOLE - a workflow engine for large scale distributed design of experiments and parameter tuning

OpenMOLE is a scientific workflow engine with a strong emphasis on workload distribution. Workflows are designed using a high level Domain Specific Language (DSL) built on top of Scala. It exposes natural parallelism constructs to easily delegate the workload resulting from a workflow to a wide range of distributed computing environments. In this work, we briefly expose the strong assets of OpenMOLE and demonstrate its efficiency at exploring the parameter set of an agent simulation model. We perform a multi-objective optimisation on this model using computationally expensive Genetic Algorithms (GA). OpenMOLE hides the complexity of designing such an experiment thanks to its DSL, and transparently distributes the optimisation process. The example shows how an initialisation of the GA with a population of 200,000 individuals can be evaluated in one hour on the European Grid Infrastructure.Comment: IEEE High Performance Computing and Simulation conference 2015, Jun 2015, Amsterdam, Netherland