Provision of an integrated data analysis platform for computational neuroscience experiments

Munir, Kamran; Kiani, Saad Liaquat; Hasham, Khawar; McClatchey, Richard; Branson, Andrew; Shamdasani, Jetendr

doi:10.1108/jsit-01-2014-0004

Cited by 12 publications

(10 citation statements)

References 14 publications

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“…Once a workflow has been enacted in the Grid, the Provenance Service coordinates the retrieval of all data outcomes in addition to any data that was produced during the execution of the saved workflow. CRISTAL populates the appropriate structures in the Analysis Base of the N4U project (see [20]) to enable tracking. (The N4U Analysis Base captures information about all image datasets, algorithms, clinical data and accumulated provenance data for a community of researchers).…”

Section: Results and Technological Outcomes 31 Cristal As The Basis mentioning

confidence: 99%

“…Figure 4 shows the portal of the neuGRID Provenance Service. The API allows clients to store workflow templates, to create instances of workflows and to update the status of workflow instances; in N4U this is all managed in the Analysis Base alongside the meta-data describing data-sets (further detail can be found in [20]). The Translator component is responsible for converting the workflow passed to the Provenance Service in a standard format into CRISTAL's internal format.…”

Section: The Neugrid Provenance Service In Practicementioning

confidence: 99%

“…Other neuGRID services (e.g. the Querying Service) directly interact with the Analysis Base for the execution of users' queries to perform search for different datasets, algorithms and pipelines [20].…”

Section: The Neugrid Provenance Service In Practicementioning

confidence: 99%

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Providing traceability for neuroimaging analyses

McClatchey¹,

Branson²,

Anjum³

et al. 2013

International Journal of Medical Informatics

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In 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.

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Section: Results and Technological Outcomes 31 Cristal As The Basis mentioning

confidence: 99%

Section: The Neugrid Provenance Service In Practicementioning

confidence: 99%

See 1 more Smart Citation

Providing traceability for neuroimaging analyses

McClatchey¹,

Branson²,

Anjum³

et al. 2013

International Journal of Medical Informatics

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“…Modern scientific experiments such as the Large Hadron Collider (LHC) 1 , and projects such as neuGRID [1] and its follow-on neuGRIDforUsers [2] are producing huge amounts of data. This data is processed and analysed to extract meaningful information by employing scientific workflows that orchestrate the complex data analysis processes [3].…”

Section: Introductionmentioning

confidence: 99%

Re-provisioning of Cloud-Based Execution Infrastructure Using the Cloud-Aware Provenance to Facilitate Scientific Workflow Execution Reproducibility

Hasham

Munir

McClatchey

et al. 2016

Communications in Computer and Information Science

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Abstract. Provenance has been considered as a means to achieve scientific workflow reproducibility to verify the workflow processes and results. Cloud computing provides a new computing paradigm for the workflow execution by o↵ering a dynamic and scalable environment with on-demand resource provisioning. In the absence of Cloud infrastructure information, achieving workflow reproducibility on the Cloud becomes a challenge. This paper presents a framework, named ReCAP, to capture the Cloud infrastructure information and to interlink it with the workflow provenance to establish the Cloud-Aware Provenance (CAP). This paper identifies di↵erent scenarios of using the Cloud for workflow execution and presents di↵erent mapping approaches. The reproducibility of the workflow execution is performed by re-provisioning the similar Cloud resources using CAP and re-executing the workflow; and by comparing the outputs of workflows. Finally, this paper also presents the evaluation of ReCAP in terms of captured provenance, workflow execution time and workflow output comparison.

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“…The scientific community is processing and analysing huge amounts of data being generated in modern scientific experiments that include projects such as DNA analysis (Foster 2008), the Large Hadron Collider (LHC) (http://lhc.cern.ch), and projects such as neuGRID (Mehmood 2009) and its follow-on neuGRIDforUsers (Munir 2013(Munir , 2014. In particular the neuGRID community is utilising scientific workflows to orchestrate the complex analysis of neuro-images to diagnose Alzheimer disease.…”

Section: Introductionmentioning

confidence: 99%

Using Cloud-Aware Provenance to Reproduce Scientific Workflow Execution on Cloud

Hasham

Munir

McClatchey

2015

Proceedings of the 5th International Conference on Cloud Computing and Services Science

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Abstract:Provenance has been thought of a mechanism to verify a workflow and to provide workflow reproducibility. 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. This paper presents a novel approach that can assist in mitigating this challenge. This approach can collect Cloud infrastructure information from an outside Cloud client along with workflow provenance and can establish a mapping between them. This mapping is later used to re-provision resources on the Cloud for workflow execution. The reproducibility of the workflow execution is performed by: (a) capturing the Cloud infrastructure information (virtual machine configuration) along with the workflow provenance, (b) reprovisioning the similar resources on the Cloud and re-executing the workflow on them and (c) by comparing the outputs of workflows. The evaluation of the prototype suggests that the proposed approach is feasible and can be investigated further. Moreover, there is no reference reproducibility model exists in literature that can provide guidelines to achieve this goal in Cloud. This paper also attempts to present a model that is used in the proposed design to achieve workflow reproducibility in the Cloud environment.

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Provision of an integrated data analysis platform for computational neuroscience experiments

Cited by 12 publications

References 14 publications

Providing traceability for neuroimaging analyses

Providing traceability for neuroimaging analyses

Re-provisioning of Cloud-Based Execution Infrastructure Using the Cloud-Aware Provenance to Facilitate Scientific Workflow Execution Reproducibility

Using Cloud-Aware Provenance to Reproduce Scientific Workflow Execution on Cloud

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