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

  1. 1. Recording and Reasoning Over Data Provenance in Web and Grid Services Martin Szomszor and Luc Moreau [email_address] University of Southampton
  2. 2. Contents <ul><li>A definition of provenance </li></ul><ul><li>Example 1: Aerospace engineering </li></ul><ul><li>Example 2: Organ transplant management </li></ul><ul><li>Example 3: Bioinformatics grid </li></ul><ul><li>Provenance architecture </li></ul><ul><li>Provenance service </li></ul><ul><li>Conclusion </li></ul>
  3. 3. The Grid and Virtual Organisations <ul><li>The Grid problem is defined as coordinated resource sharing and problem solving in dynamic, multi-institutional virtual organisations [FKT01]. </li></ul><ul><li>Effort is required to allow users to place their trust in the data produced by such virtual organisations </li></ul><ul><li>Understanding how a given service is likely to modify data flowing into it, and how this data has been generated is crucial. </li></ul>
  4. 4. Provenance and Virtual Organisations <ul><li>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; </li></ul><ul><li>How can we determine the process that generated the result, especially after the virtual organisation has been disbanded? </li></ul><ul><li>The lack of information about the origin of results does not help users to trust such open environments. </li></ul>
  5. 5. Provenance and Workflows <ul><li>Workflow enactment has become popular in the Web Services and Grid communities </li></ul><ul><li>Workflow enactment can be seen as a scripted form of virtual organisation. </li></ul><ul><li>The problem is similar: how can we determine the origin of enactment results. </li></ul>
  6. 6. Provenance: Definition <ul><li>Provenance is an annotation able to explain how a particular result has been derived. </li></ul><ul><li>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. </li></ul><ul><li>Using provenance, a user can trace the “process” that led to the aggregation of services producing a particular output. </li></ul>
  7. 7. Provenance in Aerospace Engineering <ul><li>Aerospace engineering requires to undertake scientific simulations, data pre- and post-processing and visualisation, composed in complex workflows. </li></ul>
  8. 8. Provenance in Aerospace Engineering <ul><li>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. </li></ul><ul><li>For instance, aircrafts’ provenance data need to be kept for up to 99 years when sold to some countries . </li></ul><ul><li>Currently, however little direct support is available for this. </li></ul>
  9. 9. Provenance in Organ Transplant Management <ul><li>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 . </li></ul>
  10. 10. Provenance in Organ Transplant Management <ul><li>Such a domain is heavily regulated </li></ul><ul><li>European, national, regional and site specific rules govern how decisions are made </li></ul><ul><li>Application of these rules must be ensured, be auditable and may change over time </li></ul><ul><li>Patient recovery is highly dependent on </li></ul><ul><ul><li>organ allocation choice, </li></ul></ul><ul><ul><li>extraction and insertion methods, </li></ul></ul><ul><ul><li>care/recovery regime. </li></ul></ul>
  11. 11. Provenance in Organ Transplant Management <ul><li>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 . </li></ul><ul><li>Maximise the efficiency in matching and recovery rate of patients. </li></ul>
  12. 12. Provenance in a Bioinformatics Grid (myGrid) <ul><li>myGrid aims to build a personalised problem-solving environment, in which: </li></ul><ul><li>the scientist can construct in silico experiments, </li></ul><ul><li>find and adapt others, </li></ul><ul><li>store results in data repositories, </li></ul><ul><li>have their own view on public repositories, </li></ul><ul><li>be better informed as to the provenance and </li></ul><ul><li>the currency of the tools and data directly </li></ul><ul><li>relevant to their experimental space. </li></ul>
  13. 13. Provenance in a Bioinformatics Grid (myGrid) <ul><li>Two major forms of provenance [Greenwood03]: </li></ul><ul><ul><li>The derivation path records the process by which results are generated from input data. </li></ul></ul><ul><ul><li>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. </li></ul></ul><ul><ul><li>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). </li></ul></ul>
  14. 14. Provenance in a Bioinformatics Grid (myGrid) <ul><li>Two major forms of provenance [Greenwood03]: </li></ul><ul><ul><li>Annotations are attached to objects, or collections of objects. </li></ul></ul><ul><ul><li>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. </li></ul></ul><ul><ul><li>Useful to provide personalised environment. </li></ul></ul>
  15. 15. Other Provenance Requirements and Uses <ul><li>Standard lineage representation, automated lineage recording, unobtrusive information collecting [Frew and Brose] </li></ul><ul><li>To give reliability and quality, justification and audit, re-usability, reproducibility and repeatability, change and evolution, ownership, security, credit and copyright [Goble] </li></ul>
  16. 16. What is the problem? <ul><li>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. </li></ul><ul><li>Currently, the Web Services protocol stack and the Open Grid Services Architecture do not provide any support for recording provenance. </li></ul>
  17. 17. Our Contributions <ul><li>A service-oriented architecture for provenance support in Grid and Web Services environments, based on the idea of a provenance service; </li></ul><ul><li>A client-side API for recording provenance data for Web Service invocation; </li></ul><ul><li>A data model for storing provenance data; </li></ul><ul><li>A server-side interface for querying provenance data; </li></ul><ul><li>Two components making use of provenance: provenance browsing and provenance validation. </li></ul>
  18. 18. Overall Architecture
  19. 19. Overall Architecture <ul><li>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. </li></ul><ul><li>Provenance data will be submitted to one or more “provenance repositories” acting as storage for provenance data. </li></ul><ul><li>Upon user's requests, some analysis, navigation and reasoning over provenance data can be undertaken. </li></ul>
  20. 20. Overall Architecture <ul><li>Storage could be achieved by a provenance service. </li></ul><ul><li>A library, optionally hosted in the provenance service, would perform the analysis, navigation or reasoning. </li></ul><ul><li>A client side library would submit provenance data to the provenance service. </li></ul>
  21. 21. System Overview
  22. 22. Sequence Diagram <ul><li>To identify the interactions between provenance service, client side library and enactment engine </li></ul><ul><li>Creation of a session </li></ul><ul><li>Need to be able to support the most complex workflows including conditional branching, iteration, recursion and parallel execution. </li></ul><ul><li>Support asynchronous submission of provenance data so that provenance submission does not delay workflow execution. </li></ul>
  23. 23. Sequence Diagram
  24. 24. Provenance Data Model <ul><li>Must support recording of all information necessary to replay execution </li></ul><ul><li>Must support all complex forms of workflows (recursion, iterations, parallel execution). </li></ul>
  25. 25. Provenance Data Model
  26. 27. Discussion <ul><li>In order for provenance data to be useful, we expect such a protocol to support some “classical” properties of distributed algorithms. </li></ul><ul><li>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. </li></ul><ul><li>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. </li></ul><ul><li>We anticipate that cryptographic techniques will be useful to ensure such properties </li></ul>
  27. 28. <ul><li>The purpose of project PASOA to investigate provenance in Grid architectures </li></ul><ul><li>Funded by EPSRC under the “fundamental computer science for e-Science call” </li></ul><ul><li>In collaboration with Cardiff </li></ul><ul><li> </li></ul>
  28. 29. Conclusion <ul><li>Provenance is a rather unexplored domain </li></ul><ul><li>Strategic to bring trust in open environment </li></ul><ul><li>Our provenance service is the first attempt to incorporate provenance in the infrastructure of Web and Grid services </li></ul><ul><li>Need to further investigate the algorithmic foundations of provenance, which will lead to scalable and secure industrial solutions. </li></ul>
  29. 30. Acknowledgements <ul><li>Syd Chapman, IBM </li></ul><ul><li>Omer Rana, Cardiff </li></ul><ul><li>Andreas Schreiber and Rolf Hempel, DLR </li></ul><ul><li>Lazslo Varga, SZTAKI </li></ul><ul><li>Ulises Cortes and Steven Willmott, UPC </li></ul><ul><li>Mark Greenwood, Carole Goble, Manchester </li></ul>