Recording and Reasoning Over Data Provenance in Web and Grid Services
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Recording and Reasoning Over Data Provenance in Web and Grid Services

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    Recording and Reasoning Over Data Provenance in Web and Grid Services Recording and Reasoning Over Data Provenance in Web and Grid Services Presentation Transcript

    • Recording and Reasoning Over Data Provenance in Web and Grid Services Martin Szomszor and Luc Moreau [email_address] University of Southampton
    • Contents
      • A definition of provenance
      • Example 1: Aerospace engineering
      • Example 2: Organ transplant management
      • Example 3: Bioinformatics grid
      • Provenance architecture
      • Provenance service
      • Conclusion
    • The Grid and Virtual Organisations
      • The Grid problem is defined as coordinated resource sharing and problem solving in dynamic, multi-institutional virtual organisations [FKT01].
      • Effort is required to allow users to place their trust in the data produced by such virtual organisations
      • Understanding how a given service is likely to modify data flowing into it, and how this data has been generated is crucial.
    • Provenance and Virtual Organisations
      • Given a set of services in an open grid environment that decide to form a virtual organisation with the aim to produce a given result;
      • How can we determine the process that generated the result, especially after the virtual organisation has been disbanded?
      • The lack of information about the origin of results does not help users to trust such open environments.
    • Provenance and Workflows
      • Workflow enactment has become popular in the Web Services and Grid communities
      • Workflow enactment can be seen as a scripted form of virtual organisation.
      • The problem is similar: how can we determine the origin of enactment results.
    • Provenance: Definition
      • Provenance is an annotation able to explain how a particular result has been derived.
      • In a service-oriented architecture, provenance identifies what data is passed between services, what services are available,and what results are generated for particular sets of input values, etc.
      • Using provenance, a user can trace the “process” that led to the aggregation of services producing a particular output.
    • Provenance in Aerospace Engineering
      • Aerospace engineering requires to undertake scientific simulations, data pre- and post-processing and visualisation, composed in complex workflows.
    • Provenance in Aerospace Engineering
      • Provenance is crucially required in this context, as the need to maintain a historical record of outputs from each sub-system is an important requirement for many customers that utilise the end result of simulations.
      • For instance, aircrafts’ provenance data need to be kept for up to 99 years when sold to some countries .
      • Currently, however little direct support is available for this.
    • Provenance in Organ Transplant Management
      • Medical information systems, and in particular decision support systems for organ and tissue transplant, rely on a wide range of data sources, patient data, and knowledge added by doctors, surgeons and other individuals using the systems .
    • Provenance in Organ Transplant Management
      • Such a domain is heavily regulated
      • European, national, regional and site specific rules govern how decisions are made
      • Application of these rules must be ensured, be auditable and may change over time
      • Patient recovery is highly dependent on
        • organ allocation choice,
        • extraction and insertion methods,
        • care/recovery regime.
    • Provenance in Organ Transplant Management
      • Tracking back previous decisions in any one centre to identify whether the best match was made, who was involved in the decision, what was the context .
      • Maximise the efficiency in matching and recovery rate of patients.
    • Provenance in a Bioinformatics Grid (myGrid)
      • myGrid aims to build a personalised problem-solving environment, in which:
      • the scientist can construct in silico experiments,
      • find and adapt others,
      • store results in data repositories,
      • have their own view on public repositories,
      • be better informed as to the provenance and
      • the currency of the tools and data directly
      • relevant to their experimental space.
    • Provenance in a Bioinformatics Grid (myGrid)
      • Two major forms of provenance [Greenwood03]:
        • The derivation path records the process by which results are generated from input data.
        • Derivation data provides the answer to questions about what initial data was used for a result, and how was the transformation from initial data to result achieved.
        • FDA requirement on drug companies to keep a record of provenance of drug discovery as long as the drug is in use (up to 50 years sometimes).
    • Provenance in a Bioinformatics Grid (myGrid)
      • Two major forms of provenance [Greenwood03]:
        • Annotations are attached to objects, or collections of objects.
        • Annotation data provides more contextual information that might be of interest: who performed an experiment, when did they supply any comments on the specific methods and materials used, when an object was created, last updated,who owns it and its format.
        • Useful to provide personalised environment.
    • Other Provenance Requirements and Uses
      • Standard lineage representation, automated lineage recording, unobtrusive information collecting [Frew and Brose]
      • To give reliability and quality, justification and audit, re-usability, reproducibility and repeatability, change and evolution, ownership, security, credit and copyright [Goble]
    • What is the problem?
      • Provenance recording should be part of the infrastructure, so that users can elect to enable it when they execute their complex tasks over the Grid or in Web Services environments.
      • Currently, the Web Services protocol stack and the Open Grid Services Architecture do not provide any support for recording provenance.
    • Our Contributions
      • A service-oriented architecture for provenance support in Grid and Web Services environments, based on the idea of a provenance service;
      • A client-side API for recording provenance data for Web Service invocation;
      • A data model for storing provenance data;
      • A server-side interface for querying provenance data;
      • Two components making use of provenance: provenance browsing and provenance validation.
    • Overall Architecture
    • Overall Architecture
      • Provenance gathering is a collaborative process that involves multiple entities, including the workflow enactment engine, the enactment engine's client, the service directory, and the invoked services.
      • Provenance data will be submitted to one or more “provenance repositories” acting as storage for provenance data.
      • Upon user's requests, some analysis, navigation and reasoning over provenance data can be undertaken.
    • Overall Architecture
      • Storage could be achieved by a provenance service.
      • A library, optionally hosted in the provenance service, would perform the analysis, navigation or reasoning.
      • A client side library would submit provenance data to the provenance service.
    • System Overview
    • Sequence Diagram
      • To identify the interactions between provenance service, client side library and enactment engine
      • Creation of a session
      • Need to be able to support the most complex workflows including conditional branching, iteration, recursion and parallel execution.
      • Support asynchronous submission of provenance data so that provenance submission does not delay workflow execution.
    • Sequence Diagram
    • Provenance Data Model
      • Must support recording of all information necessary to replay execution
      • Must support all complex forms of workflows (recursion, iterations, parallel execution).
    • Provenance Data Model
    •  
    • Discussion
      • In order for provenance data to be useful, we expect such a protocol to support some “classical” properties of distributed algorithms.
      • Using mutual authentication , an invoked service can ensure that it submits data to a specific provenance server, and vice-versa, a provenance server can ensure that it receives data from a given service.
      • With non-repudiation , we can retain evidence of the fact that a service has committed to executing a particular invocation and has produced a given result.
      • We anticipate that cryptographic techniques will be useful to ensure such properties
      • The purpose of project PASOA to investigate provenance in Grid architectures
      • Funded by EPSRC under the “fundamental computer science for e-Science call”
      • In collaboration with Cardiff
      • www.pasoa.org
    • Conclusion
      • Provenance is a rather unexplored domain
      • Strategic to bring trust in open environment
      • Our provenance service is the first attempt to incorporate provenance in the infrastructure of Web and Grid services
      • Need to further investigate the algorithmic foundations of provenance, which will lead to scalable and secure industrial solutions.
    • Acknowledgements
      • Syd Chapman, IBM
      • Omer Rana, Cardiff
      • Andreas Schreiber and Rolf Hempel, DLR
      • Lazslo Varga, SZTAKI
      • Ulises Cortes and Steven Willmott, UPC
      • Mark Greenwood, Carole Goble, Manchester