Personalised Grid Service Discovery

The authors take a broad view that ultimately Grid- or Web-services must be located via personalised, semantic-rich discovery processes. They argue that such processes must rely on the storage of arbitrary metadata about services that originates from both service providers and service users. Examples of such metadata are reliability metrics, quality of service data, or semantic service description markup. This paper presents UDDI-MT, an extension to the standard UDDI service directory approach that supports the storage of such metadata via a tunnelling technique that ties the metadata store to the original UDDI directory. They also discuss the use of a rich, graph-based RDF query language for syntactic queries on this data. Finally, they analyse the performance of each of these contributions in our implementation.

Personalised Grid Service Discovery

We take a broad view that ultimately Grid- or Web-services must be located via personalised, semantic-rich discovery processes. We argue that such processes must rely on the storage of arbitrary metadata about services that originates from both service providers and service users. Examples of such metadata are reliability metrics, quality of service data, or semantic service description markup. This paper presents UDDI-MT, an extension to the standard UDDI service directory approach that supports the storage of such metadata via a tunnelling technique that ties the metadata store to the original UDDI directory. We also discuss the use of a rich, graph-based RDF query language for syntactic queries on this data. Finally, we analyse the performance of each of these contributions in our implementation.

Agents and the Grid: Service Discovery

The Grid is a large-scale computer system that is capable of coordinating resources that are not subject to centralised control, whilst using standard, open, general-purpose protocols and interfaces, and delivering non-trivial qualities of service. In this chapter, we argue that Grid applications very strongly suggest the use of agent-based computing, and we review key uses of agent technologies in Grids: user agents, able to customize and personalise data; agent communication languages offering a generic and portable communication medium; and negotiation allowing multiple distributed entities to reach service level agreements. In the second part of the chapter, we focus on Grid service discovery, which we have identified as a prime candidate for use of agent technologies: we show that Grid-services need to be located via personalised, semantic-rich discovery processes, which must rely on the storage of arbitrary metadata about services that originates from both service providers and service users. We present UDDI-MT, an extension to the standard UDDI service directory approach that supports the storage of such metadata via a tunnelling technique that ties the metadata store to the original UDDI directory. The outcome is a flexible service registry which is compatible with existing standards and also provides metadata-enhanced service discovery.

Towards a Protocol for the Attachment of Metadata to Service Descriptions and its Use in Semantic Discovery

Service discovery in large scale, open distributed systems is difficult because of the need to filter out services suitable to the task at hand from a potentially huge pool of possibilities. Semantic descriptions have been advocated as the key to expressive service discovery, but the most commonly used service descriptions and registry protocols do not support such descriptions in a general manner. In this paper, we present a protocol, its implementation and an API for registering semantic service descriptions and other task/user-specific metadata, and for discovering services according to these. Our approach is based on a mechanism for attaching structured and unstructured metadata, which we show to be applicable to multiple registry technologies. The result is an extremely flexible service registry that can be the basis of a sophisticated semantically-enhanced service discovery engine, an essential component of a Semantic Grid.

Mapping Attribution Metadata to the Open Provenance Model

A description of a data item's provenance can be provided in dierent forms, and which form is best depends on the intended use of that description. Because of this, dierent communities have made quite distinct underlying assumptions in their models for electronically representing provenance. Approaches deriving from the library and archiving communities emphasise agreed vocabulary by which resources can be described and, in particular, assert their attribution (who created the resource, who modied it, where it was stored etc.) The primary purpose here is to provide intuitive metadata by which users can search for and index resources. In comparison, models for representing the results of scientific workflows have been developed with the assumption that each event or piece of intermediary data in a process' execution can and should be documented, to give a full account of the experiment undertaken. These occurrences are connected together by stating where one derived from, triggered, or otherwise caused another, and so form a causal graph. Mapping between the two approaches would be benecial in integrating systems and exploiting the strengths of each. In this paper, we specify such a mapping between Dublin Core and the Open Provenance Model. We further explain the technical issues to overcome and the rationale behind the approach, to allow the same method to apply in mapping similar schemes.