The MeDICi Integration Framework: A Platform for High Performance Data Streaming Applications

Gorton, Ian; Wynne, Adam; Almquist, Justin; Chatterton, Jack

doi:10.1109/wicsa.2008.21

Cited by 16 publications

(8 citation statements)

References 8 publications

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“…Such models have many attractive features for distributed biological data and services and thus offer the potential for biologists to create analysis pipelines that automatically link experimental data to multiple computations, resulting in new insights. One example of such a resource is MeDICi (Middleware for Data-Intensive Computing; Gorton et al 2008), which represents a workflow tool for biologists based on SOAs.…”

Section: Resultsmentioning

confidence: 99%

Systems Biology Knowledgebase for a New Era in Biology A Genomics:GTL Report from the May 2008 Workshop

Gregurick¹,

Fredrickson²,

Stevens³

2009

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As part of the U.S. Department of Energy's (DOE) Office of Science, the Office of Biological and Environmental Research (OBER) supports fundamental research and technology development aimed at achieving predictive, systems-level understanding of complex biological and environmental systems to advance DOE missions in energy, climate, and environment. Specific OBER mission priorities include the development of biofuels as secure national energy resources, understanding relationships between climate change and Earth's ecosystems, and investigating the fate and transport of subsurface contaminants.To promote development of a data and information management system, OBER hosted the Genomics:GTL Systems Biology Knowledgebase Workshop in May 2008. Experts from scientific disciplines relevant to DOE missions and from the enabling technologies (e.g., bioinformatics, computer science, database development, and systems architecture) met to determine the opportunities and requirements for establishing and managing the proposed GTL Knowledgebase (GKB). Participants envision the GKB as an informatics resource that will support both systems biology research and DOE science-application areas, particularly biofuel development, carbon biosequestration, and environmental remediation. Also discussed were requirements for developing the GKB data capabilities needed to advance such research. To define such capabilities, participants were organized into working groups based on four GKB themes: data, metadata, and information; data integration; database architecture and infrastructure; and community and user issues. This report outlines the workshop's findings and highlights key opportunities for establishing the GTL Knowledgebase.

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Section: Resultsmentioning

confidence: 99%

Systems Biology Knowledgebase for a New Era in Biology A Genomics:GTL Report from the May 2008 Workshop

Gregurick¹,

Fredrickson²,

Stevens³

2009

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“…st also enable ease of s. In Systems Biology, tools s commonly use different ution paradigms, and use utput. In order to integrate standard interfaces must be ground' for plugging ess this issue, we adopt ust be created to wrap each mework [8] , an open source ensive computing, provides m for creating components ipelines. For the Kbase, we and exploit a registry of available components along with semantic their inputs and outputs.…”

Section: Workflow Design Issuesmentioning

confidence: 99%

Exploring Architecture Options for a Federated, Cloud-Based System Biology Knowledgebase

Gorton

Liu

Yin

2010

2010 IEEE Second International Conference on Cloud Computing Technology and Science

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Systems biology is characterized by a larg scientists who use a wide variety of fragmente data sets and computational tools of all scales research. In order to provide a more coheren environment for systems biology, we are wo the Department of Energy Systems Biology (Kbase) project to define a federated clou architecture. The Kbase will eventually host m of biological data, provide high performan computational resources, and support a large with tools and services to enable them to u resources. In this paper, we describe the investigations into the design of a workflo suitable for use in the Kbase. The approach u based workflow description and open so technologies, and incorporates a data a execution layer for exploiting data locality architecture. We describe a use case and the implementation we have built that demonstrat of our approach.

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“…In order to facilitate this requirement for broad, distributed tool integration, the BRM server exploits the MeDICI Integration Framework (MIFhttp://medici.pnl.gov) [18]. The MIF provides a robust middleware infrastructure for non-intrusive integration with heterogeneous software applications.…”

Section: Brm Architecture Overviewmentioning

confidence: 99%

“…To augment BRM with such capabilities, we use the MeDICi Integration Framework (MIF) [18]. MIF is a component-based integration framework built upon the Mule (http://mule.mulesource.org/) middleware technology.…”

Section: Figure 5: Medici Framework Integrated With the Brm Core Archmentioning

confidence: 99%

An Extensible, Scalable Architecture for Managing Bioinformatics Data and Analyses

Shah

Singhal

Gibson

et al. 2008

2008 IEEE Fourth International Conference on eScience

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Systems biology research demands the availability of tools and technologies that span a comprehensive range of computational capabilities, including data management, transfer, processing, integration, and interpretation. To address these needs, we have created the Bioinformatics Resource Manager (BRM), a scalable, flexible, and easy to use tool for biologists to undertake complex analyses. This paper describes the underlying software architecture of the BRM that integrates multiple commodity platforms to provide a highly extensible and scalable software infrastructure for bioinformatics. The architecture integrates a J2EE 3-tier application with an archival Experimental Data Management System, the GAGGLE framework for desktop tool integration, and the MeDICi IntegrationFramework for high-throughput data analysis workflows. This architecture facilitates a systems biology software solution that enables the entire spectrum of scientific activities, from experimental data access to high throughput processing and analysis of data for biologists and experimental scientists.

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The MeDICi Integration Framework: A Platform for High Performance Data Streaming Applications

Cited by 16 publications

References 8 publications

Systems Biology Knowledgebase for a New Era in Biology A Genomics:GTL Report from the May 2008 Workshop

Systems Biology Knowledgebase for a New Era in Biology A Genomics:GTL Report from the May 2008 Workshop

Exploring Architecture Options for a Federated, Cloud-Based System Biology Knowledgebase

An Extensible, Scalable Architecture for Managing Bioinformatics Data and Analyses

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