The convergence of sophisticated social networking, digital library tools and persistent digital archive frameworks with data grid technology is providing strong incentives for sharing digital data across time frames that can span decades. To provide scalable solutions, a service-oriented federation framework is demonstrated that can help not only federate data and services across disciplines and societal communities but also in support of technological evolution and long-term fiscal sustainability for current and future usage.

Community Remote Sensing (CRS) is an emerging field where information is collected about the environment by the general public and then integrated into collections to provide a holistic view of the environment with local details. We argue the need for a common architecture for the cyber-infrastructure that will be necessary to cater to the needs of Community Remote Sensing systems. We identify the challenges that such a cyber infrastructure (CRS-CI) has to meet and also proposed five principles as solutions to meet these challenges. Finally, we also describe the integrated Rule Oriented Data System, a data grid middleware that is built upon these principles which provides an ideal and exemplar implementation for CRS-CI.

International data grids are now being built that support joint management of shared collections. An emerging strategy is to build multiple independent data grids, each managed by the local institution. The data grids are then federated to enable controlled sharing of files. We examine the management issues associated with maintaining federations of production data grids, including management of access controls, coordinated sharing of name spaces, replication of data between data grids, and expansion of the data grid federation.

Preservation environments manage digital records for time periods that are much longer than that of a single vendor product. A primary requirement is the preservation of the authenticity and integrity of the digital records while simultaneously minimizing the cost of long-term storage, as the data is migrated onto successive generations of technology. The emergence of low-cost storage hardware has made it possible to implement innovative software systems that minimize risk of data loss and preserve authenticity and integrity. This paper describes software mechanisms in use in current persistent archives and presents an example based upon the NARA research prototype persistent archive.

The integration of grid, data grid, digital library, and preservation technology has resulted in software infrastructure that is uniquely suited to the generation and management of data. Grids provide support for the organization, management, and application of processes. Data grids manage the resulting digital entities. Digital libraries provide support for the management of information associated with the digital entities. Persistent archives provide long-term preservation. We examine the synergies between these data management systems and the future evolution that is required for the generation and management of information.

The 'Grid' is an emerging infrastructure providing coordinated and consistent services to access distributed, heterogeneous computational and information storage resources amongst autonomous organizations. Data grids are being built across the world as the next generation inter-organizational data management systems for coordinated sharing of data as well as storage resources. A data grid is a location independent logical namespace consisting persistent global identifiers for digital entities, storage resources and users in an autonomous inter-organizational collaboration. Data Grid Management Systems (DGMS) provide services on the data grid’s logical name space for management and organization of digital entities and resources. These data-intensive environments involve long run processes, which could be aggregated as Grid Workflows (Gridflows). Gridflows are executed as peer-to-peer workflows in the data grid infrastructure by the Gridflow Management Systems. The tutorial will introduce the grid, provide use-cases in large projects, examine requirements, introduce some possible solutions for managing data in the grid and using a services-oriented infrastructure. It will take real-world examples from current systems, in particular the Storage Resource Broker and the SDSC Matrix project.

We illustrate how a domain scientist can perform a complex scientific task by interleaving data access, querying, and manipulation, as well as analytical steps and computations in complex, problem specific ways. We show how our system is used by a geneticist for solving the problem of discovering the so-called "co-regulated" genes by interlinking data and computation from several Web sites, local computations, as well as local and remote databases. The main distinctive features of our system (compared, e.g., to the ZOO environment (Ioannidis et al., 1996)) include: (i) executable workflows run as Web services; (ii) abstract workflows employ concept names and semantic types that are higher-level (and thus more "scientist friendly") than executable workflows; and (iii) our system supports automatic translation of the latter into the former.

Data grids enable next generation scientific explorations that require intensive computation and analysis of petabyte-scale shared data collections. Apart from the challenge in creation and management of the data, another major challenge is the discovery of derived data products that have already been created. This work addresses the later challenge in minimizing response time and conserving the computing cycles in the grids by using "virtual services" to access derived data products. We analyze the role of virtual services for long running services in a service-oriented architecture and contribute a technique called "semantic mirror". A prototype was developed to discover derived data products of grid services using SRB-MCAT technology and GriPhyN Virtual Data Language (VDL).

Data Grids are becoming increasingly important in scientific communities for sharing large data collections and for archiving and disseminating them in a digital library framework. The Storage Resource Broker (SRB) provides transparent virtualized middleware for sharing data across distributed, heterogeneous data resources separated by different administrative and security domains. The MySRB is a Web-based interface to the SRB that provides a user-friendly interface to distributed collections brokered by the SRB. In this paper we briefly describe the use of the SRB infrastructure as tools in the data grid architecture for building distributed data collections, digital libraries, and persistent archives. We also provide details about the MySRB and its functionalities.

This panel will lay the foundation for understanding the needs of Large Systems so that OO practitioners can:• appreciate the problems faced;• understand the issues involved; and• re-orient the approaches to provide a viable solution when participating in similar efforts.Specifically, this panel will establish the foundation for discussions on Large Systems by establishing concepts, exposing terminology, and highlighting the state-of-the art.Large applications are usually complex and display one or more of the following dimensions of largeness:• Processing power: requiring tens to hundreds of gigabytes of memory and hundreds of gigaflops performance• High connectivity: highly-connected, systems can show aggregate behavior with complex characteristics: they can become chaotic.• Online access: Archival of terabytes of information, with the need to provide online access to information• Archival and online retrieval: The two technologies (database and archival storage), however, currently do not interoperate. There is a need to develop interfaces to integrate these two technologies.• Data-intensive scientific applications: These involve constructing a data handling infrastructure that simplifies the effort required to maintain petabyte archives, identify relevant data sets within the archive, move, the data to processing platforms, and distribute the data sets across multiple nodes.• Internet access: Large applications often require simultaneous access of information by millions of users worldwide;they must provide acceptable response times.• Scaleable architecture: The systems must be prepared to support exponential growth of application load.Is OOT up to the task? OO practitioners will be encountering some of these application domains in the near future. Some may have already gained some experience in trying to solve these problems. However, the literature in OO does not provide sufficient evidence to believe that OO is ready for such large systems today. Part of the problem is the cross-disciplinary nature of these problems requires a steep learning curve for OO practitioners to be effective. The modeling of these problems with an OO approach is also a challenge. Current 00 methods do not do a good job of supporting multiple views of a domain, and multiple layers of a complex application domain.

The authors investigate the distribution of a climate model across homogeneous and heterogeneous computer environments with nodes that can reside at geographically different locations. The application consists of an atmospheric general circulation model (AGCM) coupled to an oceanic general circulation model (OGCM). Three levels of code decomposition are considered to achieve a high degree of parallelism and to mask communication with computation. First, the domains of both the grid-point AGCM and OGCM are divided into sub-domains for which calculations are carried out concurrently (domain decomposition). Second, the model is decomposed based on the diversity of tasks performed by its major components (task decomposition). Last, computation and communication are organized in such a way that the exchange of data between different tasks is carried out in subdomains of the model domain (I/O decomposition). In a dedicated computer/network environment, the wall-clock time required by the resulting distributed application is reduced to that for the AGCM/Physics, with the other two model components and interprocessor communications running in parallel.>