Electronically Querying for the Provenance of Entities

The provenance of entities, whether electronic data or physical artefacts, is crucial information in practically all domains, including science, business and art. The increased use of software in automating activities provides the opportunity to add greatly to the amount we can know about an entityâ??s history and the process by which it came to be as it is. However, it also presents difficulties: querying for the provenance of an entity could potentially return detailed information stretching back to the beginning of time, and most of it possibly irrelevant to the querier. In this paper, we define the concept of provenance query and describe techniques that allow us to perform scoped provenance queries.

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.

Extracting Causal Graphs from an Open Provenance Data Model

The open provenance architecture (OPA) approach to the challenge was distinct in several regards. In particular, it is based on an open, well-defined data model and architecture, allowing different components of the challenge workflow to independently record documentation, and for the workflow to be executed in any environment. Another noticeable feature is that we distinguish between the data recorded about what has occurred, emphprocess documentation, and the emphprovenance of a data item, which is all that caused the data item to be as it is and is obtained as the result of a query over process documentation. This distinction allows us to tailor the system to separately best address the requirements of recording and querying documentation. Other notable features include the explicit recording of causal relationships between both events and data items, an interaction-based world model, intensional definition of data items in queries rather than relying on explicit naming mechanisms, and emphstyling of documentation to support non-functional application requirements such as reducing storage costs or ensuring privacy of data. In this paper we describe how each of these features aid us in answering the challenge provenance queries.

Transparent Provenance Derivation for User Decisions

It is rare for data's history to include computational processes alone. Even when software generates data, users ultimately decide to execute software procedures, choose their configuration and inputs, reconfigure, halt and restart processes, and so on. Understanding the provenance of data thus involves understanding the reasoning of users behind these decisions, but demanding that users explicitly document decisions could be intrusive if implemented naively, and impractical in some cases. In this paper, therefore, we explore an approach to transparently deriving the provenance of user decisions at query time. The user reasoning is simulated, and if the result of the simulation matches the documented decision, the simulation is taken to approximate the actual reasoning. The plausibility of this approach requires that the simulation mirror human decision -making, so we adopt an automated process explicitly modelled on human psychology. The provenance of the decision is modelled in OPM, allowing it to be queried as part of a larger provenance graph, and an OPM profile is provided to allow consistent querying of provenance across user decisions.