1. Provenance: an open approach to experiment validation in e- Science Professor Luc Moreau L.Moreau@ecs.soton.ac.uk University of Southampton www.ecs.soton.ac.uk/~lavm

2. Provenance & PASOA Teams University of Southampton Luc Moreau, Paul Groth, Simon Miles, Victor Tan, Miguel Branco, Sofia Tsasakou, Sheng Jiang, Steve Munroe, Zheng Chen IBM UK (EU Project Coordinator) John Ibbotson, Neil Hardman, Alexis Biller University of Wales, Cardiff Omer Rana, Arnaud Contes, Vikas Deora, Ian Wootten Universitad Politecnica de Catalunya (UPC) Steven Willmott, Javier Vazquez SZTAKI Laszlo Varga, Arpad Andics German Aerospace Andreas Schreiber, Guy Kloss, Frank Danneman

3. Contents Motivation Provenance Concepts Provenance Architecture Standardisation Provenance Queries Conclusions

4. Motivation

5. Scientific Research Academic Peer Review

6. Business Regulations Audit (Sarbanes-Oxley) Audit (Basel II) Accounting Banking

7. Accounting Audit (Sabanes-Oxley)

8. Banking Audit (Basel II)

9. Health Care Management European Recommendation R(97)5: on the protection of medical data

10. e-Science datasets How to undertake peer-reviewing and validation of e-Scientific results?

11. Sarbanes-Oxley The American Competitiveness and Corporate Accountability Act of 2002, commonly known as the Sarbanes-Oxley Act, was signed into law on July 30, 2002. The law is intended to protect investors by improving the accuracy and reliability of corporate disclosures. Sarbanes-Oxley also defines a higher level of responsibility, accountability, and financial reporting transparency - changes that are intended to return confidence to investors, as well.

12. Food & Drug Administration

13. Basel II

14. Compliance to Regulations The “next-compliance” problem Can we be certain that by ensuring compliance to a new regulation, we do not break previous compliance?

15. Current Solutions Proprietary, Monolithic Silos, Closed Do not inter-operate with other applications Not adaptable to new regulations

16. Provenance Oxford English Dictionary: the fact of coming from some particular source or quarter; origin, derivation the history or pedigree of a work of art, manuscript, rare book, etc.; concretely, a record of the ultimate derivation and passage of an item through its various owners. Concept vs representation

17. Provenance in Computer Systems Our definition of provenance in the context of applications for which process matters to end users: The provenance of a piece of data is the process that led to that piece of data Our aim is to conceive a computer-based representation of provenance that allows us to perform useful analysis and reasoning to support our use cases

18. Our Approach Define core concepts pertaining to provenance Specify functionality required to become “provenance-aware” Define open data models and protocols that allow systems to inter-operate Standardise data models and protocols Provide a reference implementation Provide reasoning capability

19. Context (1) Aerospace engineering: maintain a historical record of design processes, up to 99 years. Organ transplant management: tracking of previous decisions, crucial to maximise the efficiency in matching and recovery rate of patients

20. Context (2) High Energy Physics: tracking, analysing, verifying data sets in the ATLAS Experiment of the Large Hadron Collider (CERN) Bioinformatics: verification and auditing of “experiments” (e.g. for drug approval)

21. Provenance Concepts

22. Provenance “Lifecycle” Application Results Provenance Store Record Documentation of Execution Query and Reason over Provenance of Data Administer Store and its contents Core Interfaces to Provenance Store

23. Nature of Documentation We represent the provenance of some data by documenting the process that led to the data: documentation can be complete or partial; it can be accurate or inaccurate; it can present conflicting or consensual views of the actors involved; it can provide operational details of execution or it can be abstract.

24. p-assertion A given element of process documentation will be referred to as a p-assertion p-assertion: is an assertion that is made by an actor and pertains to a process.

25. Service Oriented Architecture Broad definition of service as component that takes some inputs and produces some outputs. Services are brought together to solve a given problem typically via a workflow definition that specifies their composition. Interactions with services take place with messages that are constructed according to services interface specification. The term actor denotes either a client or a service in a SOA. A process is defined as execution of a workflow

26. M1 M2 M3 M4 Actor 1 Actor 2 I received M1, M4 I sent M2, M3 I received M3 I sent M4 From these p-assertions, we can derive that M3 was sent by Actor 1 and received by Actor 2 (and likewise for M4) If actors are black boxes, these assertions are not very useful because we do not know dependencies between messages Process Documentation (1)

27. M1 M2 M3 M4 Actor 1 Actor 2 M2 is in reply to M1 M3 is caused by M1 M2 is caused by M4 M4 is in reply to M3 These assertions help identify order of messages, but not how data was computed Process Documentation (2)

28. f M1 M2 M3 M4 Actor 1 Actor 2 f1 f2 M3 = f1(M1) M2 = f2(M1,M4)M4 = f(M3) These assertions help identify how data is computed, but provide no information about non-functional characteristics of the computation (time, resources used, etc) Process Documentation (3)

29. M1 M2 M3 M4 Actor 1 Actor 2 I used 386 cluster Request sat in queue for 6min I used sparc processor I used algorithm x version x.y.z Process Documentation (4)

30. Types of p-assertions (1) Interaction p-assertion: is an assertion of the contents of a message by an actor that has sent or received that message I received M1, M4 I sent M2, M3

31. Types of p-assertions (2) Relationship p-assertion: is an assertion, made by an actor, that describes how the actor obtained output data or the whole message sent in an interaction by applying some function to input data or messages from other interactions. M2 is in reply to M1 M3 is caused by M1 M2 is caused by M4 M3 = f1(M1) M2 = f2(M1,M4)

32. Types of p-assertions (3) Actor state p-assertion: assertion made by an actor about its internal state in the context of a specific interaction I used sparc processor I used algorithm x version x.y.z

33. Data flow Interaction p-assertions allow us to specify a flow of data between actors Relationship p-assertions allow us to characterise the flow of data “inside” an actor Overall data flow (internal + external) constitutes a DAG, which characterises the process that led to a result

34. Provenance Architecture

35. Interfaces to Provenance Store Application Results Provenance Store Record Documentation of Execution Query and Reason over Provenance Of Data Administer Store and its contents

37. P-Assertion schemas

38. The p-structure (1) The p-structure is a common logical structure of the provenance store shared by all asserting and querying actors Hierarchical Indexed by Interactions

39. The p-structure (2)

40. Recording Protocol (Groth04-06) Abstract machines DS Properties Termination Liveness Safety Statelessness Documentation Properties Immutability Attribution Datatype safety Foundation for adding necessary cryptographic techniques

41. Querying Functionality (Miles06) Process Documentation Query Interface: allows for “navigation” of the documentation of execution Allows us to view the provenance store (i.e. the p- structure) as if containing XML data structures Independent of technology used for running application and internal store representation Seamless navigation of application dependent and application independent provenance representation

42. Querying Functionality (Miles06) Provenance Query Interface: allows us to obtain the provenance of some specific data A recognition that there is not “one” provenance for a piece of data, but there may be different, depending on the end- user’s interest Hence, provenance is seen as a query: Identify a piece of data Scope of the process of interest: Filter in/out p-assertions according to actors, process, types of relationships, etc

43. Available Software PReServ (Paul Groth & Simon Miles) Offer recording and querying interfaces Available from www.pasoa.org www.pasoa.org Soon ogsa-dai based version available from www.gridprovenance.org Is being used in a bioinformatics application (cf. hpdc’05, iswc’05)

44. Standardisation

45. Standardisation Options APIs Programmatic inter-op Recording and querying Interfaces Service inter-op Provenance Model Data inter-op

46. Purpose of Standardisation Application Provenance Stores Record Documentation of Execution Application Allow for multiple applications to document their execution. Applications may be running in different institutions.

47. Purpose of Standardisation Application Provenance Store Record Documentation of Execution Allow for multiple stores from multiple IT providers Provenance Store Provenance Store

48. Purpose of Standardisation Provenance Store Query Provenance Of Data Allow for multiple stores from multiple IT providers Provenance Store

49. Purpose of Standardisation Allow for legacy, monolithic applications to expose their contents (according to standard schema) Convert in standard data format

50. Purpose of Standardisation Allow third parties to host provenance stores, which are trusted by application owners but also auditors Application Provenance Store

51. Compliance Oriented Architectures Application Provenance Store Record Documentation of Execution Query Provenance Of Data Compliance verification

52. Compliance Oriented Architectures Separate execution documentation from compliance verification Allows for multiple compliance verifications Allows for validation to take place across multiple applications, possibly run by different institutions (in particular, allows for outsourcing and subcontracting). Approach is suitable for e- scientific peer-reviewing and business compliance verification

53. Provenance Queries (Miles’06)

54. Example Application GUI Averager Divider Store 1. average (7, 5) 2. divide (12, 2) 3. answer (6) 4. answer (6) 5. store (“6”, file1) Averager(in1,in2) { return (in1+in2)/2; } Averager delegates the division operation to the service Divider

55. Example Application GUI Averager Divider Store 1. average (7, 5) 2. divide (12, 2) 3. answer (6) 4. answer (6) 5. store (“6”, file1) Relationships 12in msg 2 is sum of7, 5in msg 1 6in msg 3 is division of12, 2in msg 2 6in msg 4 is copy of6in msg 3 6in msg 4 is average of7, 5in msg 1 6in msg 6 is copy of6in msg 4 Tracers are used to demarcate activities (aka sets of services) added by Averager in call to Divider returned by Divider in response

56. Identifying what to Find the Provenance of Identify the event where the entity is documented: In this case, the event is the receipt of a request to store the data in file named file1 Identify the data entity within that message In this case, the data of interest is the “6” stored in file1 “file1” Store

57. Provenance Graph “6” StoreGUI “6” GUIAverager “6” AveragerDivider “12” DividerAverager “2” DividerAverager “5” AveragerGUI “7” AveragerGUI Copy of Division of DividendDivisor Sum of Average of

58. Scoped Provenance Graph “6” StoreGUI “6” GUIAverager “6” AveragerDivider “12” DividerAverager “2” DividerAverager “5” AveragerGUI “7” AveragerGUI Copy of Division of DividendDivisor Sum of Average of Filter to exclude “Average of” relationships Allows us to take a given perspective on the provenance of a piece of data: e.g. looking at the restorations of a painting rather than its various owners

59. Scoped Provenance Graph “6” StoreGUI “6” GUIAverager “6” AveragerDivider “12” DividerAverager “2” DividerAverager “5” AveragerGUI “7” AveragerGUI Copy of Division of DividendDivisor Sum of Average of Filter to exclude messages containing tracer This is equivalent to hiding the internal operation of Averager Allows us to consider a given service (and all its inferior invocations) as a black box: e.g. no detail should be provided about the internals of Averager

60. Scoped Provenance Graph “6” StoreGUI “6” GUIAverager “6” AveragerDivider “12” DividerAverager “2” DividerAverager “5” AveragerGUI “7” AveragerGUI Copy of Division of DividendDivisor Sum of Average of Filter to exclude Divisor parameters Allows us to scope the provenance graph according to types of data:

61. Provenance Query

62. Practically … Data Identification (here Event) //ps:interactionRecord [ps:interactionKey/ps:messageSink/ wsa:EndpointReference/wsa:Address= "http://www.example.com/store"] Unscoped query / Exclude ‘averageOf’ relation /pq:relationshipTarget[ps:relation!= "http://www.example.com#averageOf"] Exclude tracer introduced by Averager /pq:relationshipTarget/ps:interactionPAssertion [not(ex:envelope/ph:pheader/ ph:interactionMetaData [ph:tracer="process://sub/1"])]

63. Conclusions

64. Provenance Store Record To Sum Up Query Compliance check Rerun/Reproduce Analyse Standardising the documentation of Business Processes Provenance Architecture Methodology Apply Healthcare Distribution Finance Aerospace Automobile Pharmaceutical Slide from John Ibbotson

65. Conclusions Crucial topic for many applications Full architectural specification An implementation available for download Methodology to make application provenance-aware www.pasoa.org www.gridprovenance.org

66. www.ipaw.info/ipaw06

67. Publications 1. Paul Groth, Simon Miles, Weijian Fang, Sylvia C. Wong, Klaus-Peter Zauner, and Luc Moreau. Recording and Using Provenance in a Protein Compressibility Experiment. In Proceedings of the 14th IEEE International Symposium on High Performance Distributed Computing (HPDC'05), July 2005. 2. Paul Groth, Michael Luck, and Luc Moreau. A protocol for recording provenance in service-oriented Grids. In Proceedings of the 8th International Conference on Principles of Distributed Systems (OPODIS'04), Grenoble, France, December 2004. 3. Paul Groth, Michael Luck, and Luc Moreau. Formalising a protocol for recording provenance in Grids. In Proceedings of the UK OST e-Science second All Hands Meeting 2004 (AHM'04), Nottingham, UK, September 2004. 4. Simon Miles, Paul Groth, Miguel Branco, and Luc Moreau. The requirements of recording and using provenance in e-Science experiments. Technical report, University of Southampton, 2005. 5. Luc Moreau, Syd Chapman, Andreas Schreiber, Rolf Hempel, Omer Rana, Lazslo Varga, Ulises Cortes, and Steven Willmott. Provenance-based Trust for Grid Computing --- Position Paper. In, 2003. 6. Paul Townend, Paul Groth, and Jie Xu. A Provenance-Aware Weighted Fault Tolerance Scheme for Service-Based Applications. In Proc. of the 8th IEEE International Symposium on Object-oriented Real-time distributed Computing (ISORC 2005), May 2005. 7. Paul Groth, Simon Miles, Victor Tan, and Luc Moreau. Architecture for Provenance Systems. Technical report, University of Southampton, October 2005.

68. Questions

70. OTM Application