5 research outputs found
Provision of an integrated data analysis platform for computational neuroscience experiments
© Emerald Group Publishing Limited. Purpose – The purpose of this paper is to provide an integrated analysis base to facilitate computational neuroscience experiments, following a user-led approach to provide access to the integrated neuroscience data and to enable the analyses demanded by the biomedical research community. Design/methodology/approach – The design and development of the N4U analysis base and related information services addresses the existing research and practical challenges by offering an integrated medical data analysis environment with the necessary building blocks for neuroscientists to optimally exploit neuroscience workflows, large image data sets and algorithms to conduct analyses. Findings – The provision of an integrated e-science environment of computational neuroimaging can enhance the prospects, speed and utility of the data analysis process for neurodegenerative diseases. Originality/value – The N4U analysis base enables conducting biomedical data analyses by indexing and interlinking the neuroimaging and clinical study data sets stored on the grid infrastructure, algorithms and scientific workflow definitions along with their associated provenance information
Designing Traceability into Big Data Systems
Providing an appropriate level of accessibility and traceability to data or
process elements (so-called Items) in large volumes of data, often
Cloud-resident, is an essential requirement in the Big Data era.
Enterprise-wide data systems need to be designed from the outset to support
usage of such Items across the spectrum of business use rather than from any
specific application view. The design philosophy advocated in this paper is to
drive the design process using a so-called description-driven approach which
enriches models with meta-data and description and focuses the design process
on Item re-use, thereby promoting traceability. Details are given of the
description-driven design of big data systems at CERN, in health informatics
and in business process management. Evidence is presented that the approach
leads to design simplicity and consequent ease of management thanks to loose
typing and the adoption of a unified approach to Item management and usage.Comment: 10 pages; 6 figures in Proceedings of the 5th Annual International
Conference on ICT: Big Data, Cloud and Security (ICT-BDCS 2015), Singapore
July 2015. arXiv admin note: text overlap with arXiv:1402.5764,
arXiv:1402.575
Scientific Workflow Repeatability through Cloud-Aware Provenance
The transformations, analyses and interpretations of data in scientific
workflows are vital for the repeatability and reliability of scientific
workflows. This provenance of scientific workflows has been effectively carried
out in Grid based scientific workflow systems. However, recent adoption of
Cloud-based scientific workflows present an opportunity to investigate the
suitability of existing approaches or propose new approaches to collect
provenance information from the Cloud and to utilize it for workflow
repeatability in the Cloud infrastructure. The dynamic nature of the Cloud in
comparison to the Grid makes it difficult because resources are provisioned
on-demand unlike the Grid. This paper presents a novel approach that can assist
in mitigating this challenge. This approach can collect Cloud infrastructure
information along with workflow provenance and can establish a mapping between
them. This mapping is later used to re-provision resources on the Cloud. The
repeatability of the workflow execution is performed by: (a) capturing the
Cloud infrastructure information (virtual machine configuration) along with the
workflow provenance, and (b) re-provisioning the similar resources on the Cloud
and re-executing the workflow on them. The evaluation of an initial prototype
suggests that the proposed approach is feasible and can be investigated
further.Comment: 6 pages; 5 figures; 3 tables in Proceedings of the Recomputability
2014 workshop of the 7th IEEE/ACM International Conference on Utility and
Cloud Computing (UCC 2014). London December 201
Providing traceability for neuroimaging analyses
IntroductionWith the increasingly digital nature of biomedical data and as the complexity of analyses in medical research increases, the need for accurate information capture, traceability and accessibility has become crucial to medical researchers in the pursuance of their research goals. Grid- or Cloud-based technologies, often based on so-called Service Oriented Architectures (SOA), are increasingly being seen as viable solutions for managing distributed data and algorithms in the bio-medical domain. For neuroscientific analyses, especially those centred on complex image analysis, traceability of processes and datasets is essential but up to now this has not been captured in a manner that facilitates collaborative study. Purpose and MethodFew examples exist, of deployed medical systems based on Grids that provide the traceability of research data needed to facilitate complex analyses and none have been evaluated in practice. Over the past decade, we have been working with mammographers, paediatricians and neuroscientists in three generations of projects to provide the data management and provenance services now required for 21st century medical research. This paper outlines the finding of a requirements study and a resulting system architecture for the production of services to support neuroscientific studies of biomarkers for Alzheimer’s Disease.ResultsThe paper proposes a software infrastructure and services that provide the foundation for such support. It introduces the use of the CRISTAL software to provide provenance management as one of a number of services delivered on a SOA, deployed to manage neuroimaging projects that have been studying biomarkers for Alzheimer’s disease. ConclusionsIn the neuGRID and N4U projects a Provenance Service has been delivered that captures and reconstructs the workflow information needed to facilitate researchers in conducting neuroimaging analyses. The software enables neuroscientists to track the evolution of workflows and datasets. It also tracks the outcomes of various analyses and provides provenance traceability throughout the lifecycle of their studies. As the Provenance Service has been designed to be generic it can be applied across the medical domain as a reusable tool for supporting medical researchers thus providing communities of researchers for the first time with the necessary tools to conduct widely distributed collaborative programmes of medical analysis
Development of a large-scale neuroimages and clinical variables data atlas in the neuGRID4You (N4U) project
© 2015 Elsevier Inc.. Exceptional growth in the availability of large-scale clinical imaging datasets has led to the development of computational infrastructures that offer scientists access to image repositories and associated clinical variables data. The EU FP7 neuGRID and its follow on neuGRID4You (N4U) projects provide a leading e-Infrastructure where neuroscientists can find core services and resources for brain image analysis. The core component of this e-Infrastructure is the N4U Virtual Laboratory, which offers easy access for neuroscientists to a wide range of datasets and algorithms, pipelines, computational resources, services, and associated support services. The foundation of this virtual laboratory is a massive data store plus a set of Information Services collectively called the 'Data Atlas'. This data atlas stores datasets, clinical study data, data dictionaries, algorithm/pipeline definitions, and provides interfaces for parameterised querying so that neuroscientists can perform analyses on required datasets. This paper presents the overall design and development of the Data Atlas, its associated dataset indexing and retrieval services that originated from the development of the N4U Virtual Laboratory in the EU FP7 N4U project in the light of detailed user requirements