Supporting dynamic parameter sweep applications in workflows: lessons learnt from the CancerGrid project

A vast majority of scientific grid applications are either parameter sweep applications or a significant subpart of these applications belong to class of parameter sweep activities. The paper describes a new graphical workflow language in which any node of the DAG-based workflow can be a parameter sweep node and the execution of these nodes are transparently executed either in service grids or in desktop grids depending on the computational complexity of the workflow node. The new concept is supported by the CancerGrid portal that has been established for a chemist community.

A review of dynamic Bayesian network techniques with applications in healthcare risk modelling

Coping with an ageing population is a major concern for healthcare organisations around the world. The average cost of hospital care is higher than social care for older and terminally ill patients. Moreover, the average cost of social care increases with the age of the patient. Therefore, it is important to make efficient and fair capacity planning which also incorporates patient centred outcomes. Predictive models can provide predictions which their accuracy can be understood and quantified. Predictive modelling can help patients and carers to get the appropriate support services, and allow clinical decision-makers to improve care quality and reduce the cost of inappropriate hospital and Accident and Emergency admissions. The aim of this study is to provide a review of modelling techniques and frameworks for predictive risk modelling of patients in hospital, based on routinely collected data such as the Hospital Episode Statistics database. A number of sub-problems can be considered such as Length-of-Stay and End-of-Life predictive modelling. The methodologies in the literature are mainly focused on addressing the problems using regression methods and Markov models, and the majority lack generalisability. In some cases, the robustness, accuracy and re-usability of predictive risk models have been shown to be improved using Machine Learning methods. Dynamic Bayesian Network techniques can represent complex correlations models and include small probabilities into the solution. The main focus of this study is to provide a review of major time-varying Dynamic Bayesian Network techniques with applications in healthcare predictive risk modelling.

Business Models for Cloud Computing: Experiences from Developing Modeling & Simulation as a Service Applications in Industry

The potential of cloud computing is gaining significant interest in Modeling & Simulation (M&S). The underlying concept of using computing power as a utility is very attractive to users that can access state-of-the-art hardware and software without capital investment. Moreover, the cloud computing characteristics of rapid elasticity and the ability to scale up or down according to workload make it very attractive to numerous applications including M&S. Research and development work typically focuses on the implementation of cloud-based systems supporting M&S as a Service (MSaaS). Such systems are typically composed of a supply chain of technology services. How is the payment collected from the end-user and distributed to the stakeholders in the supply chain? We discuss the business aspects of developing a cloud platform for various M&S applications. Business models from the perspectives of the stakeholders involved in providing and using MSaaS and cloud computing are investigated and presented.

Extending Science Gateway Frameworks to Support Big Data Applications in the Cloud

Cloud computing offers massive scalability and elasticity required by many scien-tific and commercial applications. Combining the computational and data handling capabilities of clouds with parallel processing also has the potential to tackle Big Data problems efficiently. Science gateway frameworks and workflow systems enable application developers to implement complex applications and make these available for end-users via simple graphical user interfaces. The integration of such frameworks with Big Data processing tools on the cloud opens new oppor-tunities for application developers. This paper investigates how workflow sys-tems and science gateways can be extended with Big Data processing capabilities. A generic approach based on infrastructure aware workflows is suggested and a proof of concept is implemented based on the WS-PGRADE/gUSE science gateway framework and its integration with the Hadoop parallel data processing solution based on the MapReduce paradigm in the cloud. The provided analysis demonstrates that the methods described to integrate Big Data processing with workflows and science gateways work well in different cloud infrastructures and application scenarios, and can be used to create massively parallel applications for scientific analysis of Big Data.

Treatment of phenanthrene and benzene using microbial fuel cells operated continuously for possible in situ and ex situ applications

Bioelectrochemical systems could have potential for bioremediation of contaminants either in situ or ex situ. The treatment of a mixture of phenanthrene and benzene using two different tubular microbial fuel cells (MFCs) designed for either in situ and ex situ applications in aqueous systems was investigated over long operational periods (up to 155 days). For in situ deployments, simultaneous removal of the petroleum hydrocarbons (>90% in term of degradation efficiency) and bromate, used as catholyte, (up to 79%) with concomitant biogenic electricity generation (peak power density up to 6.75 mWm−2) were obtained at a hydraulic retention time (HRT) of 10 days. The tubular MFC could be operated successfully at copiotrophic (100 ppm phenanthrene, 2000 ppm benzene at HRT 30 days) and oligotrophic (phenanthrene and benzene, 50 ppb each, HRT 10 days) substrate conditions suggesting its effectiveness and robustness at extreme substrate concentrations in anoxic environments. In the MFC designed for ex situ deployments, optimum MFC performance was obtained at HRT of 30 h giving COD removal and maximum power output of approximately 77% and 6.75 mWm−2 respectively. The MFC exhibited the ability to resist organic shock loadings and could maintain stable MFC performance. Results of this study suggest the potential use of MFC technology for possible in situ/ex situ hydrocarbon-contaminated groundwater treatment or refinery effluents clean-up, even at extreme contaminant level conditions.