Velo: A Knowledge-Management Framework for Modeling and Simulation

Gorton, Ian; Sivaramakrishnan, Chandrika; Black, Gary D.; White, Signe K.; Purohit, Sumit; Lansing, Carina; Madison, Michael C.; Schuchardt, Karen; Yan, Liu

doi:10.1109/mcse.2011.116

Cited by 23 publications

(10 citation statements)

References 6 publications

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“…The computerized portion of the workflow was designed to control and aggregate results for the 12,000 GCAM-USA runs in a factorial/Monte Carlo experiment. The software framework includes a user environment based upon the Velo scientific knowledge management platform [37] coupled with the MeDICi Integration Framework [38] for defining and orchestrating scientific workflows.…”

Section: Uncertainty Propagation Workflowmentioning

confidence: 99%

Calculating impacts of energy standards on energy demand in U.S. buildings with uncertainty in an integrated assessment model

Scott

Daly

Hathaway

et al. 2015

Energy

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Section: Uncertainty Propagation Workflowmentioning

confidence: 99%

Calculating impacts of energy standards on energy demand in U.S. buildings with uncertainty in an integrated assessment model

Scott

Daly

Hathaway

et al. 2015

Energy

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“…As the system complexity increases the notion of architectural knowledge is gaining importance. There were developed tools that support storing, verifying and analyzing that knowledge [12,13,18]. It also influences modern development methodologies [3].…”

Section: Motivation and Related Workmentioning

confidence: 99%

On quick comprehension and assessment of software

Bartoszuk

Dąbrowski

Stencel

et al. 2013

Proceedings of the 14th International Conference on Computer Systems and Technologies

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By an architecture of a software system we mean the fundamental organization of the system embodied in its components, their relationships to one another and to the system's environment. It also encompasses principles governing the system's design and evolution. Architectures of complex systems are obviously complex as well. The goal of our research is to harness this complexity. In this paper we focus on providing software architects with ability to quickly comprehend the complexity and assess the quality of software. The essential tools we use are: (1) a graph-based repository for collecting information on software artefacts, accompanied by (2) tools to perform software intelligence tasks, like analyzing dependencies among those artefacts, calculating their importance, and quality. On top of those tools we implement visualization methods that render the relative importance using size and the quality using colours. By means of such methods a software architect can at glance comprehend and assess the software, He/she can (1) find the starting points to dig into a complex system; (2) judge the cohesion and coupling of system components; and (3) assess the overall quality. We demonstrate this method using selected open-source projects of various sizes and qualities.

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“…Velo [6] is a reusable, domain independent, eScience environment and knowledge management infrastructure for modeling and simulation. Velo leverages, integrates, and extends Web-based open source collaborative and data management technologies to create a scalable core platform that can be tailored to specific science domains.…”

Section: Velomentioning

confidence: 99%

“…In the development of the framework and its core exemplary components, the project is building on existing efforts such as developing capabilities for data acquisition, annotation and storage (ICAT and MeDICi) [3], [4], [5] collaborative data management and analysis (Velo) [6], semantic characterization [3], data compression [7], data reduction [8], image reconstruction [9], [10], [11], [12], [13], segmentation [14], [15], feature detection [16], [17], registration [18], [19] and visualization [20], [21]. In this project we do not only aim to extend the existing capabilities to support real-time image analysis, but we are also looking towards identifying core analysis capabilities that are essential to larger groups of analysis workflows, thus aiding the move away from current ad hoc solutions to more sophisticated analysis methods.…”

Section: Introductionmentioning

confidence: 99%

Velo and REXAN — Integrated data management and high speed analysis for experimental facilities

Dam

Carson

Corrigan

et al. 2012

2012 IEEE 8th International Conference on E-Science

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The Chemical Imaging Initiative at the Pacific Northwest National Laboratory (PNNL) is creating a 'Rapid Experimental Analysis' (REXAN) Framework, based on the concept of reusable component libraries. REXAN allows developers to quickly compose and customize high throughput analysis pipelines for a range of experiments, as well as supporting the creation of multi-modal analysis pipelines. In addition, PNNL has coupled REXAN with its collaborative data management and analysis environment Velo to create an easy to use data management and analysis environments for experimental facilities. This paper will discuss the benefits of Velo and REXAN in the context of three examples:• PNNL High Resolution Mass Spectrometry -reducing analysis times from hours to seconds, and enabling the analysis of much larger data samples (100KB to 40GB) at the same time. • ALS X-Ray Tomography -reducing analysis times of combined STXM and EM data collected at the ALS from weeks to minutes, decreasing manual work and increasing data volumes that can be analysed in a single step. •Multi-modal nano-scale analysis of STXM and TEM data -providing a semi automated process for particle detection. The creation of REXAN has significantly shortened the development time for these analysis pipelines. The integration of Velo and REXAN has significantly increased the scientific productivity of the instruments and their users by creating easy to use data management and analysis environments with greatly reduced analysis times and improved analysis capabilities.

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Velo: A Knowledge-Management Framework for Modeling and Simulation

Cited by 23 publications

References 6 publications

Calculating impacts of energy standards on energy demand in U.S. buildings with uncertainty in an integrated assessment model

Calculating impacts of energy standards on energy demand in U.S. buildings with uncertainty in an integrated assessment model

On quick comprehension and assessment of software

Velo and REXAN — Integrated data management and high speed analysis for experimental facilities

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Velo: A Knowledge-Management Framework for Modeling and Simulation

Cited by 23 publications

References 6 publications

Calculating impacts of energy standards on energy demand in U.S. buildings with uncertainty in an integrated assessment model

Calculating impacts of energy standards on energy demand in U.S. buildings with uncertainty in an integrated assessment model

On quick comprehension and assessment of software

Velo and REXAN &#x2014; Integrated data management and high speed analysis for experimental facilities

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Velo and REXAN — Integrated data management and high speed analysis for experimental facilities