PROVENANCE

1. Department of Computer Science Software Engineering Research Group, Berlin, Germany PROVENANCE Abdul Saboor Welcome to this Presentation

2. Presentation Agenda • What is Provenance? • Why Provenance is important and two major strands of Provenance? • Provenance and Linked Data • Provenance Data Model • Provenance Vocabularies • The Open Provenance Model • Provenance Data Quality Assessment • Summary - Scientific and Technical Challenges of Provenance 1

3. What is Provenance? Provenance • Recording the history of data and its place of origin Provenance Dictionary Definitions • The Merriam-Webster online diction – Origin , Source • Oxford English Dictionary – The place of origin or earliest known history of something; origin, derivation. Provenance Definitions 1. Provenance refers to the source of Information such as entities and processes involved in producing or delivering an artifact. (Yolanda) 2. Provenance is a description of how things came to be, and how they came to be in the state they are in today. Statements about the provenance can themselves be considered to have provenance. (Jim M) Continues ... 2

4. What is Provenance? Provenance Working Definitions • Provenance of a resource is a record that describes entities and processes involved in producing and delivering or otherwise influencing that resource. Provenance provides a critical foundation for assessing authenticity, enabling trust, and allowing reproducibility. Provenance assertions are a form of contextual metadata and can themselves become important records with their own provenance. (W3C) Provenance Web Definition 4. On the web, provenance would include information about the creation and publication of web resources as well as information about access of those resources, and activities related to their discussion, linking, and reuse. Continues ... 3

5. What is Provenance? Provenance Definitions 5. Provenance is documentation of the set of artifacts, processes, and agents that have caused a artifact to be, and of the contexts of these entities. Provenance provides a critical foundation for assessing authenticity, enabling trust, and allowing reproducibility and assertions of provenance can themselves become important records with their own provenance. (Jim M) 4

6. What kind of History? • Data Creator/Data Publisher • Data Creation Date • Data Modifier & Modification Date • Data Description • Etc... 5

7. Why Provenance is Important? The need of Provenance for data integration and reuse • Data comes from various diverse data sources • Varying Quality • Different Scope • Different Assumptions 6

8. Two major strands of Provenance • Data Provenance • Data provenance is Fine-grain Provenance • Derivation of a piece of data i.e. results of transformations • Description of the origin of a piece of data and process by which it arrives in a database • Workflow Provenance • Workflow Provenance is coarse-grain provenance • Refers to records of history of the derivation of the final output of workflow • Perform typically for complex processing tasks 7

9. Data And Workflow Provenance Data Provenance When information describing that how data has moved through a network of databases is referred to as “fine-grain” or “data” provenance. Fine-grain provenance can further categorized into: where, how and why-Provenance. A query execution simply copy data elements from some source to some target database and where-provenance identifies these source elements where the data in the target is copied from. Why-provenance provides justification for the data elements appearing in the output and how-provenance describes some parts of the input influenced certain parts of the output. Workflow Provenance When Information describing how derived data has been calculated from raw observations that is referred to as “coarse-grain” or “workflow” provenance. The widespread use of workflow flow tools for processing scientific data facilitate for capturing provenance information. The workflow process describes all the steps involved in producing a given data set and, hence captures it provenance information. 7A

10. Provenance Dimensions - 1 Content of Provenance Information • Attribution - provenance as the sources or entities that were used to create a new result • Responsibility - knowing who endorses a particular piece of information or result • Origin - recorded vs reconstructed, verified vs non-verified, asserted vs inferred • Process - provenance as the process that yielded an artifact • Reproducibility (e.g. workflows, mashups, text extraction) • Data Access (e.g. access time, accessed server, party responsible for accessed server) • Evolution and versioning • Republishing (e.g. re-tweeting, re-blogging, re-publishing) • Updates (e.g. a document with content from various sources and that changes over time) • Justification for decisions – Includes argumentation, hypotheses, why-not questions • Entailment - given the results to a particular query, what tuples led to those results 8

11. Provenance Dimensions - 2 Management of Provenance Information • Publication - Making provenance information available (expose, distribute) • Access - Finding and querying provenance information • Dissemination control – Track policies specified by creator for when/how an artifact can be used • Access Control - incorporate access control policies to access provenance information • Licensing - stating what rights the object creators and users have based on provenance • Law enforcement (e.g. enforcing privacy policies on the use of personal information) • Scale - how to operate with large amounts of provenance information Use of Provenance Information • Understanding - End user consumption of provenance • abstraction, multiple levels of description, summary • presentation, visualization 9

12. Provenance Dimensions - 3 • Interoperability - combining provenance produced by multiple different systems • Comparison - finding what is common in the provenance of two or more entities • Accountability - the ability to check the provenance of an object with respect to some expectation • Verification - of a set of requirements • Compliance - with a set of policies • Trust - making trust judgments based on provenance • Information quality - choosing among competing evidence from diverse sources (e.g. linked data use cases) • Incorporating reputation and reliability ratings with attribution information • Imperfections - reasoning about provenance information that is not complete or correct • Incomplete provenance • Uncertain/probabilistic provenance • Erroneous provenance • Fraudulent provenance • Debugging 10

13. Web of Data 11 Adapted from Cetinia, iSOCO Innovation Lab, J.M.G Perez, Provenance: eScience to the Web of Data, 11/09

14. The Linked Data Paradigm • How can we exploit all the available data? • Data can be reuse and remix • Common flexible and usable APIs • Standard vocabularies to describe interlinked datasets • Various Tools • Understand the Semantic Web vision 12

15. Provenance and Link Data • Provenance provides the ability • Trace the sources of various kinds of data • Enable the exploration of relationships between datasets, their authors and affiliations • Provenance analysis provides an insight on how data is produced and exploited • Provenance create a notion of information quality • Is a certain dataset consistent and up to date? • Is the connection between two datasets meaningful? • Is a given dataset relevant for a particular domain? • Provenance to establish information trustworthiness • Provenance to provide data views relating to some criteria 13

16. The Provenance Data Model Institutional Level Metadata associated with origin in terms of its data attributes (e.g, AuthorName, Title, URL, etc.) Experimental Protocol Level The Origin of datasets (e.g. History area, region, organisation or institution) Data Analysis and Significance Level Datasets statistical analysis methodology for selecting relevant attributes (e.g. Either datasets divided into parts, output values, versions, etc) Dataset Description Level Who published that datasets. The vocabulary of interlinked datasets such as Dublin Core, voiD, PRV, etc. 14

17. The Provenance Related Vocabularies • DC – Dublin Core • FOAF – Friend of a Friend • SIOC – Semantic Interlinked online communities • WOT – Web of Trust Schema • OMV – Ontology Metadata vocabulary • SWP – Semantic Web Publishing • VoiD – Vocabulary for interlinked datasets • PRV – Provenance Vocabulary • PML – Proof Markup Language • PAV – SWAN provenance ontology • OUZO – Provenance ontology • CS – Changeset Vocabulary • Etc. 15

18. Provenance Related Metadata Provenance related metadata is either directly attached to data item or its host the documents or it is available as additional data on web. For example – Attached metadata are RDF statements about an RDF graph that contains the statements, AuthorName and Creation date of blog entries added to syndication feed, or information about an image and detached metadata can be represented in RDF using vocabularies. 16

19. A Provenance Architecture for the Web of Data Application Layer Authoritative agencies require to certify and keep data provenance secure 17 Adapted from Cetinia, iSOCO Innovation Lab, J.M.G Perez, Provenance: eScience to the Web of Data, 11/09

20. Main Action Points Provenance Vocabularies Awareness of Data Providers Tools for Data Providers Represent and reason with trust and information quality Generalization of Provenance Metadata W3C Provenance Incubator Group Extend emerging Linked data vocabularies Provenance Authoritative Agencies Linked Data Standards (VOiD) VOiD Provenance Visualization 18 Adapted from Cetinia, iSOCO Innovation Lab, J.M.G Perez, Provenance: eScience to the Web of Data, 11/09

21. The Open Provenance Model • The Open Provenance Model in which data is being produced/transformed into new state. It can also represent the one or more data items from an old to a new state. • OPM graph model for provenance which describes the graph whose edges denote the relationship between occurrence presented by the nodes. • The main purpose of OPM is to support the assessment of various data qualities such as reliability, accuracy and timeliness. 19

22. OPM Classifies nodes into three parts Artifacts Artifacts are the parts of data of fixed value and context that possibly represent an entity in a given state. Edges can also have annotations for providing the information on how occurrence cause another. Process Process are performed on artifacts in order to produce another artifact. Agents Agents indicate the entities which are controlling the process such as user. 20

23. Model of Web Data Provenance Provenance Graph – It describes the provenance of data Items: Nodes Edges Sub-graphs Provenance elements (Pieces of provenance information) Relating Provenance elements to each other Related data items if possible 21

24. Main Focus of Provenance of Web Data • Provenance Models Define • Types of Provenance elements (roles) • Relationship between those elements 22 Adapted from Olaf Hartig’s, Humboldt University Berlin, Provenance Information in the Web of Data, 04/09

25. Provenance Data Quality Assessment The Quality of Information • Main Objectives are accessing the quality of datasets • Quality of datasets in multidimensional perspectives • Relevance of criteria determined by preferences and performing certain tasks on available datasets 23

26. Provenance Data Quality • Data Trustworthiness • Data Authenticity • Data Reliability • Dimensions of Believability • Trustworthiness of source • Data Lineage – The origin of data • Related Artifacts and actors • Reasonableness of data • Possibility – The extent to which data value is possible • Consistency – The extent to which a data value is consistent with other values of same data • Quality of Data Provenance has Three dimensions: • Correctness • Completeness • Relevancy 24

27. Provenance Data Quality • Quality of Datasets • Timeliness • Consistency between datasets • Consistency over source – The extent to which a data value is consistent with other values of the same data • Consistency over time – The extent to which the data value is consistent with past data values • Stable and meaningful data • Temporal of Data • Transaction valid times closeness – The extent to which a data value is credible based on proximity of transaction time to valid times. • Transaction time overlap – The extent to which a data value is derived from data values with overlapping valid times. 25

28. Trust Evaluation Some Questions must need to be considered while provenance data trust evaluation… • Who created that content(s) (author or attributions)? • Was the contents manipulated? If yes then by what process or source? • Who is providing those contents (repositories)? 26

29. Quality of Data Assessment • Assign numeric values to Quality Criteria of Datasets or Scoring/Rating Systems • Proactive Approach • Precision vs Practicality Semi-Automatic Approach Manual Approach • Rating based system • Reputation based system • Questionnaires base system 27

30. Reasons of Assessment Main Reasons • Provenance of assessed data on the web • Primary Objectives • Identify the methods / approaches to automatically assess the quality of data on the web • Or Identify the methods to assess the Quality Criteria of Data automatically of web data. 28

31. A Generalize Assessment Approach Step - 1 Generate a provenance graph for the data item Annotate the provenance graph with impact values Step - 2 Execute the assessment function/program (script) Step - 3 29

32. Generate a Provenance Graph • What types of provenance elements are necessarily require? • What types of details (i.e. granularity) are necessarily require? • Where and how do we get provenance information? • Two complementary options • Recordings • Analyzing the metadata 30

33. Annotation with Impact Values • How might each Provenance element can influence the quality of data? • Each type of element has to analyze systematically • What kinds of impact values are necessary and how to represent the influence through impact values? • It is not necessary that impact values should be numeric • It also depends on the assessment functions • How do we determine the impact values? 31

34. Determine the Impact Values • From Provenance Information • From user Input • Rating-based systems, or reputation-based systems • Configuration options • Through Content Analysis • Comparison of data contents • Adoption of information retrieval methods • Adoption of data cleansing techniques • Through Context Analysis • Further metadata • Domain knowledge 32

35. Annotation with Impact Values • How might each Provenance element can influence the quality of data? 33

36. Assessment Function (s) • How the assessment function look alike? • Develop function together with impact values • Take incompleteness into consideration • Provenance graph could be fragmentary • Annotation could be missing 34

37. Scientific and Technical Challenges of Provenance – 1(SUMMARY) Provenance information need to be: • Represented • Captured and recorded • Stored and secured, queries and reasoned about • Visualized and browsed 35

38. Scientific and Technical Challenges of Provenance - 2 • Vocabularies for representation of provenance contents • Need representation of process (workflow), entities roles, data collections, meta-assertions, etc. • The open provenance model (OPM) • Granularity of provenance records • How much detail is useful, manageable/scalable in practice? • Size of provenance can be orders of magnitude larger than base data. • Provenance evaluation for information quality and trust management 36

39. Scientific and Technical Challenges of Provenance – 2a • Evaluation and updates • Shelf timeliness of data • Determine when data becomes obsolete based on provenance information • Versioning of data sources • Relate updates of data based on provenance information • Provenance-aware visualization, navigation and resource consumption 37

40. Scientific and Technical Challenges of Provenance and Trust – 3 • Policies based on Provenance information • Association-based policies • Source is cited in Spiegel • Source is cited in Wikipedia • Bias-based policies • Source is an Oil company • Distrust policies • Source is a blog • Policies may be restricted to a context • Topic of search, topics of pages, tags of page • Trust policies may be shared across users 38

41. Thanks for your attentions ! Any Questions? Freie University Berlin Computer Science Department Software Engineering Research Group TakuStr 9, Berlin, Germany. 39

42. References W3C Website, What is provenance? Modified at November 2010, http://www.w3.org/2005/Incubator/prov/wiki/What_Is_Provenance W3C Website, A working Definition of Provenance, Modified at November 2010, http://www.w3.org/2005/Incubator/prov/wiki/What_Is_Provenance#A_Working_Definition_of_Provenance Hartig, O. Provenance information in the Web of data. In Proceedings of LDOW 2009 (Madrid, Spain, April 2009). O. Hartig and J. Zhao. Using web data provenance for quality assessment. Pro-ceedings of the 1st Int. Workshop on the Role of Semantic Web in Provenance D. Brickley and L. Miller, FOAF Vocabulary Specification, November 2007. http://xmlns.com/foaf/spec U. Bojars and J. G. Breslin. SIOC Core Ontology Specification, Revision 1.30, Jan. 2009. http://rdfs.org/sioc/spec/ Luc Moreau, Juliana Freire, Joe Futrelle, Robert E. McGrath, Jim Myers, and Patrick Paulson. The open provenance model: An overview. In IPAW, pages 323–326, 2008. L. L. Pipino, Y. W. Lee, and R. Y. Wang, “Data Quality Assessment,”Communications of the ACM, vol. 45, Issue no. 4, p. 211-218, 2009. You-Wei cheah, Beth Plale. Provenance Analysis: Towards qaulity provenance. In proceeding of 8th IEEE International conference on eScience, Chicago Illinois, Oct. 2012. http://www.ci.uchicago.edu/escience2012/pdf/Provenance_Analysis-Towards_Quality_Provenance.pdf Yogesh Simmhan, Beth Plale, and Dennis Gannon. A survey of data provenance in e-science. SIGMOD Record, 34(3):31–36, 2005. Prat, N., and Madnick, S. Evaluating and aggregating data believability across quality sub-dimensions and data lineage. In Proceedings of WITS 2007 (Montreal, Canada, December 2007), p.169-174. Y. Simmhan, B. Plale, and D. Gannon. A Survey of Data Provenance in e-Science. SIGMOD Record, Computer Science Department, Indiana University. Vol. 34, Issue No. 3, p31–36, ACM, Sept. 2005. P. Buneman, S. Khanna, and W. C. Tan. Data Provenance: Some Basic Issues. In Proceedings of the 20th Conference on Foundations of Software Technology and Theoretical Computer Science (FST TCS), p87-93, Springer, Dec. 2000. Prat, N., and Madnick, S. Measuring data believability: A provenance approach. Proceedings of HICSS-41 (Big Island, HI, January 2008), IEEE, p.1-10. Jose Manuel Gomez-Perez, Invited Lectures on Programmable web and the web of data, November 2009, URJC, Campus de Mostoles, Departmental II, Salon de grados, Madrid, Spain, Website, http://www.cetinia.urjc.es/es/node/331 Website : http://www.w3.org/2005/Incubator/prov/wiki/images/0/02/Provenance-XG-Overview.pdf http://www.w3.org/2005/Incubator/prov/wiki/Provenance_Dimensions http://www.w3.org/2005/Incubator/prov/wiki/W3C_Provenance_Incubator_Group_Wiki