Connecting Scientific Data to Scientific Experiments with Provenance

As scientific workflows and the data they operate on, grow in size and complexity, the task of defining how those workflows should execute (which resources to use, where the resources must be in readiness for processing etc.) becomes proportionally more difficult. While "workflow compilers", such as Pegasus, reduce this burden, a further problem arises: since specifying details of execution is now automatic, a workflow's results are harder to interpret, as they are partly due to specifics of execution. By automating steps between the experiment design and its results, we lose the connection between them, hindering interpretation of results. To reconnect the scientific data with the original experiment, we argue that scientists should have access to the full provenance of their data, including not only parameters, inputs and intermediary data, but also the abstract experiment, refined into a concrete execution by the "workflow compiler". In this paper, we describe preliminary work on adapting Pegasus to capture the process of workflow refinement in the PASOA provenance system.

Provenance-based Validation of E-Science Experiments

E-Science experiments typically involve many distributed services maintained by different organisations. After an experiment has been executed, it is useful for a scientist to verify that the execution was performed correctly or is compatible with some existing experimental criteria or standards, not necessarily anticipated prior to execution. Scientists may also want to review and verify experiments performed by their colleagues. There are no existing frameworks for validating such experiments in today's e-Science systems. Users therefore have to rely on error checking performed by the services, or adopt other ad hoc methods. This paper introduces a platform-independent framework for validating workflow executions. The validation relies on reasoning over the documented provenance of experiment results and semantic descriptions of services advertised in a registry. This validation process ensures experiments are performed correctly, and thus results generated are meaningful. The framework is tested in a bioinformatics application that performs protein compressibility analysis.

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 repurpose this explicitly captured knowledge. Within the myGrid 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 these 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. myGrid components enable a workflow life-cycle that extends beyond execution, to include discovery of previous relevant designs, reuse of those designs, and 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 repurposing process.

Monitoring and Explanation of Contract Execution: A Case Study in the Aerospace Domain

In the domain of aerospace aftermarkets, which often has long supply chains that feed into the maintenance of aircraft, contracts are used to establish agreements between aircraft operators and maintenance suppliers. However, violations at the bottom of the supply chain (part suppliers) can easily cascade to the top (aircraft operators), making it difficult to determine the source of the violation, and seek to address it. In this context, we have developed a global monitoring architecture that ensures the detection of norm violations and generates explanations for the origin of violations. In this paper, we describe the implementation and deployment of a global monitor in the aerospace domain of [8] and show how it generates explanations for violations within the maintenance supply chain. We show how these explanations can be used not only to detect violations at runtime, but also to uncover potential problems in contracts before their deployment, thus improving them.

Provenance: The Bridge Between Experiments and Data

Current scientific applications are often structured as workflows and rely on workflow systems to compile abstract experiment designs into enactable workflows that utilise the best available resources. The automation of this step and of the workflow enactment, hides the details of how results have been produced. Knowing how compilation and enactment occurred allows results to be reconnected with the experiment design. We investigate how provenance helps scientists to connect their results with the actual execution that took place, their original experiment and its inputs and parameters.