Pinar Alper

Feta: a light-weight architecture for user oriented semantic service discovery

Semantic Web Services offer the possibility of highly flexible web service architectures, where new services can be quickly discovered, orchestrated and composed into workflows. Most existing work has, however, focused on complex service descriptions for automated composition. In this paper, we describe the requirements from the bioinformatics domain which demand technically simpler descriptions, involving the user community at all levels. We describe our data model and light-weight semantic discovery architecture. We explain how this fits in the larger architecture of the myGrid project, which overall enables interoperability and composition across, disparate, autonomous, third-party services. Our contention is that such light-weight service discovery provides a good fit for user requirements of bioinformatics and possibly other domains.

An overview of S-OGSA: A Reference Semantic Grid Architecture

The Grids vision, of sharing diverse resources in a flexible, coordinated and secure manner through dynamic formation and disbanding of virtual communities, strongly depends on metadata. Currently, Grid metadata is generated and used in an ad hoc fashion, much of it buried in the Grid middlewares code libraries and database schemas. This ad hoc expression and use of metadata causes chronic dependency on human intervention during the operation of Grid machinery, leading to systems which are brittle when faced with frequent syntactic changes in resource coordination and sharing protocols. The Semantic Grid is an extension of the Grid in which rich resource metadata is exposed and handled explicitly, and shared and managed via Grid protocols. The layering of an explicit semantic infrastructure over the Grid Infrastructure potentially leads to increased interoperability and greater flexibility. In recent years, several projects have embraced the Semantic Grid vision. However, the Semantic Grid lacks a Reference Architecture or any kind of systematic framework for designing Semantic Grid components or applications. The Open Grid Service Architecture (OGSA) aims to define a core set of capabilities and behaviours for Grid systems. We propose a Reference Architecture that extends OGSA to support the explicit handling of semantics, and defines the associated knowledge services to support a spectrum of service capabilities. Guided by a set of design principles, Semantic-OGSA (S-OGSA) defines a model, the capabilities and the mechanisms for the Semantic Grid. We conclude by highlighting the commonalities and differences that the proposed architecture has with respect to other Grid frameworks.

The my Grid ontology: bioinformatics service discovery

(my)Grid supports in silico experiments in the life sciences, enabling the design and enactment of workflows as well as providing components to assist service discovery, data and metadata management. The (my)Grid ontology is one component in a larger semantic discovery framework for the identification of the highly distributed and heterogeneous bioinformatics services in the public domain. From an initial model of formal OWL-DL semantics throughout, we now adopt a spectrum of expressivity and reasoning for different tasks in service annotation and discovery. Here, we discuss the development and use of the (my)Grid ontology and our experiences in semantic service discovery.

Recycling workflows and services through discovery and reuse

Scientific workflows are becoming a valuable tool for scientists to capture and automate e‐Science procedures. Their success brings the opportunity to publish, share, reuse and re‐purpose this explicitly captured knowledge. Within the

Requirements and Services for Metadata Management

^{my}

e-Science and the semantic web: a symbiotic relationship

Grid project, we have identified key resources that can be shared including complete workflows, fragments of workflows and constituent services. We have examined the alternative ways that these resources can be described by their authors (and subsequent users) and developed a unified descriptive model to support their later discovery. By basing this model on existing standards, we have been able to extend existing Web service and Semantic Web service infrastructure whilst still supporting the specific needs of the e‐Scientist. The

Data capture in bioinformatics: requirements and experiences with Pedro

^{my}

Grid metadata management: Requirements and architecture

Grid components enable a workflow life‐cycle that extends beyond execution to include the discovery of previous relevant designs, the reuse of those designs and their subsequent publication. Experience with example groups of scientists indicates that this cycle is valuable. The growing number of workflows and services mean more work is needed to support the user in effective ranking of search results and to support the re‐purposing process. Copyright

Enhancing and abstracting scientific workflow provenance for data publishing

Knowledge-intensive applications pose new challenges to metadata management, including distribution, access control, uniformity of access, and evolution in time. This paper identifies general requirements for metadata management and describes a simple model and service that focuses on RDF metadata to address these requirements.

A workflow PROV-corpus based on taverna and wings

e-Science is scientific investigation performed through distributed global collaborations between scientists and their resources, and the computing infrastructure that enables this. Scientific progress increasingly depends on pooling know-how and results; making connections between ideas, people, and data; and finding and reusing knowledge and resources generated by others in perhaps unintended ways. It is about harvesting and harnessing the “collective intelligence” of the scientific community. The Semantic Web is an extension of the current Web in which information is given well-defined meaning to facilitate sharing and reuse, better enabling computers and people to work in cooperation. Applying the Semantic Web paradigm to e-Science has the potential to bring significant benefits to scientific discovery. We identify the benefits of lightweight and heavyweight approaches, based on our experiences in the Life Sciences.