caBIGtrade (the cancer Biomedical Informatics Gridtrade) is an open-source, open-access information network enabling cancer researchers to share tools, data, applications, and technologies. caGrid is the underlying service-based grid software infrastructure for caBIG, integrating distributed data and analytic resources into a virtual collaborative platform for cancer research. Within caGrid, many cancer-related data analysis and aggregation tasks can make use of "canned" sets of service invocations, or workflows. As a result, there is a need to orchestrate the invocation of caGrid services through the use of both a workflow language and tooling. In this paper, we first explain why we select Taverna as a candidate for workflow authoring and invocation. We then review the development of Taverna plug-ins in general, and describe how we extend Taverna to use caGrid services. We then detail a real-world example and the lessons learned from our research. Finally we conclude with a summary and a description of potential next steps.

Design templates that involve discovery, analysis, and integration of information resources commonly occur in many scientific research projects. In this paper we present examples of design templates from the biomedical translational research domain and discuss the requirements imposed on Grid middleware infrastructures by them. Using caGrid, which is a Grid middleware system based on the model driven architecture (MDA) and the service oriented architecture (SOA) paradigms, as a starting point, we discuss architecture directions for MDA and SOA based systems like caGrid to support common design templates.

OBJECTIVES: To develop a security infrastructure to support controlled and secure access to data and analytical resources in a biomedical research Grid environment, while facilitating resource sharing among collaborators. DESIGN: A Grid security infrastructure, called Grid Authentication and Authorization with Reliably Distributed Services (GAARDS), is developed as a key architecture component of the NCI-funded cancer Biomedical Informatics Grid (caBIG). The GAARDS is designed to support in a distributed environment 1) efficient provisioning and federation of user identities and credentials; 2) group-based access control support with which resource providers can enforce policies based on community accepted groups and local groups; and 3) management of a trust fabric so that policies can be enforced based on required levels of assurance. MEASUREMENTS: GAARDS is implemented as a suite of Grid services and administrative tools. It provides three core services: Dorian for management and federation of user identities, Grid Trust Service for maintaining and provisioning a federated trust fabric within the Grid environment, and Grid Grouper for enforcing authorization policies based on both local and Grid-level groups. RESULTS: The GAARDS infrastructure is available as a stand-alone system and as a component of the caGrid infrastructure. More information about GAARDS can be accessed at http://www.cagrid.org. CONCLUSIONS: GAARDS provides a comprehensive system to address the security challenges associated with environments in which resources may be located at different sites, requests to access the resources may cross institutional boundaries, and user credentials are created, managed, revoked dynamically in a de-centralized manner.

OBJECTIVE: To develop software infrastructure that will provide support for discovery, characterization, integrated access, and management of diverse and disparate collections of information sources, analysis methods, and applications in biomedical research. DESIGN: An enterprise Grid software infrastructure, called caGrid version 1.0 (caGrid 1.0), has been developed as the core Grid architecture of the NCI-sponsored cancer Biomedical Informatics Grid (caBIG) program. It is designed to support a wide range of use cases in basic, translational, and clinical research, including 1) discovery, 2) integrated and large-scale data analysis, and 3) coordinated study. MEASUREMENTS: The caGrid is built as a Grid software infrastructure and leverages Grid computing technologies and the Web Services Resource Framework standards. It provides a set of core services, toolkits for the development and deployment of new community provided services, and application programming interfaces for building client applications. RESULTS: The caGrid 1.0 was released to the caBIG community in December 2006. It is built on open source components and caGrid source code is publicly and freely available under a liberal open source license. The core software, associated tools, and documentation can be downloaded from the following URL: https://cabig.nci.nih.gov/workspaces/Architecture/caGrid. CONCLUSIONS: While caGrid 1.0 is designed to address use cases in cancer research, the requirements associated with discovery, analysis and integration of large scale data, and coordinated studies are common in other biomedical fields. In this respect, caGrid 1.0 is the realization of a framework that can benefit the entire biomedical community.

This paper presents a fault tolerance framework for applications that process data using a distributed network of user-defined operations in a pipelined fashion. The framework saves intermediate results and messages exchanged among application components in a distributed data management system to facilitate quick recovery from failures. The experimental results show that the framework scales well and our approach introduces very little overhead to application execution.

Service-oriented architectures and applications have gained wide acceptance in the Grid computing community. A number of tools and middleware systems have been developed to support application development using Grid Services architectures. Most of these efforts, however, have focused on low-level support for management and execution of Grid services, management of Grid-enabled resources, and deployment and execution of applications that make use of Grid services. Simple-to-use service development tools, which would allow a Grid service developer to leverage Grid technologies without needing to know low-level details, are becoming increasingly important for wider application of the Grid. In this paper, we describe an open-source, extensible toolkit, called Introduce, that supports easy development and deployment of Web Services Resource Framework (WSRF) compliant services. Introduce is designed to reduce the service development and deployment effort by hiding low level details of the Globus Toolkit and to enable the implementation of strongly typed services. In strongly typed services, a service produces and consumes data types that are well-defined and published in the Grid. This enables data-level syntactic interoperability so that clients and services can access and consume data elements programmatically and correctly. We expect that enabling strongly typed Grid services while lowering the difficulty of entry to the Grid via toolkits like Introduce will have a major impact to the success of the Grid and its wider adoption as a viable technology of choice in the commercial sector as well as in academic, medical, and government research.

Identity management and federation is becoming an ever present problem in large multi-institutional environments. By their nature, Grids span multiple institutional administration boundaries and aim to provide support for the sharing of applications, data, and computational resources in a collaborative environment. One underlying problem is to enable participating institutions to manage the identities of their own members by leveraging existing institutional identity management systems, while at the same time facilitating the participation in larger Grids through the deployment of grid-wide user credentials. Those grid-wide identities are used for features such as single sign-on, secure communication, and are the basis for authorization decisions. In this paper we presented the design and implementation of Dorian, a grid service infrastructure component that enables the federation of users across the collaboration

MOTIVATION: The complexity of cancer is prompting researchers to find new ways to synthesize information from diverse data sources and to carry out coordinated research efforts that span multiple institutions. There is a need for standard applications, common data models, and software infrastructure to enable more efficient access to and sharing of distributed computational resources in cancer research. To address this need the National Cancer Institute (NCI) has initiated a national-scale effort, called the cancer Biomedical Informatics Grid (caBIGtrade mark), to develop a federation of interoperable research information systems. RESULTS: At the heart of the caBIG approach to federated interoperability effort is a Grid middleware infrastructure, called caGrid. In this paper we describe the caGrid framework and its current implementation, caGrid version 0.5. caGrid is a model-driven and service-oriented architecture that synthesizes and extends a number of technologies to provide a standardized framework for the advertising, discovery, and invocation of data and analytical resources. We expect caGrid to greatly facilitate the launch and ongoing management of coordinated cancer research studies involving multiple institutions, to provide the ability to manage and securely share information and analytic resources, and to spur a new generation of research applications that empower researchers to take a more integrative, trans-domain approach to data mining and analysis. AVAILABILITY: The caGrid version 0.5 release can be downloaded from https://cabig.nci.nih.gov/workspaces/Architecture/caGrid/. The operational test bed Grid can be accessed through the client included in the release, or through the caGrid-browser web application http://cagrid-browser.nci.nih.gov.

Diverse data sets have become key building blocks of translational biomedical research. Data types captured and referenced by sophisticated research studies include high throughput genomic and proteomic data, laboratory data, data from imagery, and outcome data. In this paper, the authors present the application of an XML-based data management system to support integration of data from disparate data sources and large data sets. This system facilitates management of XML schemas and on-demand creation and management of XML databases that conform to these schemas. They illustrate the use of this system in an application for genotype-phenotype correlation analyses. This application implements a method of phenotype-genotype correlation based on phylogenetic optimization of large data sets of mouse SNPs and phenotypic data. The application workflow requires the management and integration of genomic information and phenotypic data from external data repositories and from the results of phenotype-genotype correlation analyses. Our implementation supports the process of carrying out a complex workflow that includes large-scale phylogenetic tree optimizations and application of Maddison's concentrated changes test to large phylogenetic tree data sets. The data management system also allows collaborators to share data in a uniform way and supports complex queries that target data sets.

Here the authors present a Grid-aware middleware system, called GridPACS, that enables management and analysis of images in a massive scale, leveraging distributed software components coupled with interconnected computation and storage platforms. The need for this infrastructure is driven by the increasing biomedical role played by complex datasets obtained through a variety of imaging modalities. The GridPACS architecture is designed to support a wide range of biomedical applications encountered in basic and clinical research, which make use of large collections of images. Imaging data yield a wealth of metabolic and anatomic information from macroscopic (e.g., radiology) to microscopic (e.g., digitized slides) scale. Whereas this information can significantly improve understanding of disease pathophysiology as well as the noninvasive diagnosis of disease in patients, the need to process, analyze, and store large amounts of image data presents a great challenge.

A key challenge in supporting data-driven scientific applications is the storage and management of input and output data in a distributed environment. We describe a distributed storage middleware, based on a data and metadata management framework, to address this problem. In this middleware system, applications define the structure of their input and output data using XML schemas. The system provides support for 1) registration, versioning, management of schemas, and 2) management of storage, querying, and retrieval of instance data corresponding to the schemas in distributed databases. We carry out an experimental evaluation of the system on a set of PC clusters connected over wide- (WANs) and local-area networks (LANs).