PROVENANCE

Abdul Saboor

Department of Computer Science

Software Engineering Research Group, Berlin,

Germany

Welcome to this Presentation

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

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What is Provenance?

Provenance Recording the history of data and its place of origin

Provenance Dictionary Definitions1. The Merriam-Webster online diction – Origin , Source 2. Oxford English Dictionary – The place of origin or

earliest known history of something; origin, derivation.Provenance Definitions1. 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

What is Provenance?

Provenance Working Definitions 3. 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 Definition4. 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 ...

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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)

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What kind of History?

Data Creator/Data Publisher Data Creation Date Data Modifier & Modification Date Data Description Etc...

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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

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Two major strands of Provenance

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Data And Workflow Provenance

Data ProvenanceData ProvenanceWhen 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.

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 ProvenanceWorkflow 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.

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.

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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

questionsEntailment - given the results to a particular query, what tuples led to those results 8

Provenance Dimensions - 2

Management of Provenance Information

Publication - Making provenance information available (expose, distribute)Access - Finding and querying provenance informationDissemination 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 InformationUnderstanding - End user consumption of provenance

abstraction, multiple levels of description, summary presentation, visualization

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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

Debugging10

Web of Data

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

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

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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

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The Provenance Data Model

Institutional Level

Institutional Level

Experimental Protocol Level Experimental

Protocol Level

Data Analysis and Significance

Level

Data Analysis and Significance

Level

Dataset Description Level

Dataset Description Level

Metadata associated with origin in terms of its data attributes (e.g, AuthorName, Title, URL, etc.)Metadata associated with origin in terms of its data attributes (e.g, AuthorName, Title, URL, etc.)

The Origin of datasets (e.g. History area, region, organisation or institution)The Origin of datasets (e.g. History area, region, organisation or institution)

Datasets statistical analysis methodology for selecting relevant attributes (e.g. Either datasets divided into parts, output values, versions, etc)

Datasets statistical analysis methodology for selecting relevant attributes (e.g. Either datasets divided into parts, output values, versions, etc)

Who published that datasets. The vocabulary of interlinked datasets such as Dublin Core, voiD, PRV, etc.

Who published that datasets. The vocabulary of interlinked datasets such as Dublin Core, voiD, PRV, etc.

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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.

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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.

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A Provenance Architecture for the Web

of Data

Authoritative agencies require to certify and keep data provenance

secure

Applica

tion

Layer

17Adapted from Cetinia, iSOCO Innovation Lab, J.M.G Perez, Provenance: eScience to the Web of Data, 11/09

Main Action Points

Provenance VocabulariesProvenance Vocabularies

Represent and reason with trust and information

quality

Represent and reason with trust and information

quality

Extend emerging Linked data vocabularies

Extend emerging Linked data vocabularies

VOiDVOiD

Awareness of Data Providers

Awareness of Data Providers

W3C Provenance Incubator GroupW3C Provenance Incubator Group

Linked Data Standards

(VOiD)

Linked Data Standards

(VOiD)

Tools for Data Providers

Tools for Data Providers

Generalization of Provenance Metadata

Generalization of Provenance Metadata

Provenance Authoritative

Agencies

Provenance Authoritative

Agencies

Provenance VisualizationProvenance Visualization

18Adapted from Cetinia, iSOCO Innovation Lab, J.M.G Perez, Provenance: eScience to the Web of Data, 11/09

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.

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OPM Classifies nodes into three parts

ArtifactsArtifacts

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.

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.

ProcessProcess

Process are performed on artifacts in order to produce another artifact.Process are performed on artifacts in order to produce another artifact.

AgentsAgents

Agents indicate the entities which are controlling the process such as user.Agents indicate the entities which are controlling the process such as user.

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Model of Web Data Provenance

Provenance Graph – It describes the provenance of data Items:Provenance Graph – It describes the provenance of data Items:

NodesNodes

Provenance elements (Pieces of provenance information)

Provenance elements (Pieces of provenance information)

EdgesEdges

Relating Provenance elements to each other

Relating Provenance elements to each other

Sub-graphs Sub-graphs

Related data items if possibleRelated data items if possible

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Main Focus of Provenance of Web Data

Provenance Models Define

Types of Provenance elements (roles) Relationship between those elements

22Adapted from Olaf Hartig’s, Humboldt University Berlin, Provenance Information in the Web of Data, 04/09

Provenance Data Quality Assessment

The Quality of Information

Main Objectives are accessing the quality of datasetsQuality of datasets in multidimensional perspectives

Relevance of criteria determined by preferences and performing certain tasks on available datasets

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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

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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.

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Trust Evaluation

Some Questions must need to be considered while provenance data trust evaluation…

1. Who created that content(s) (author or attributions)?

2. Was the contents manipulated? If yes then by what process or source?

3. Who is providing those contents (repositories)?

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Quality of Data Assessment

Assign numeric values to Quality Criteria of Datasets or Scoring/Rating Systems

Proactive ApproachPrecision vs Practicality

Manual ApproachManual Approach

Questionnaires base system

Questionnaires base system

Semi-Automatic Approach

Semi-Automatic Approach

Rating based system Reputation based

system

Rating based system Reputation based

system

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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.

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A Generalize Assessment Approach

Step - 1Step - 1

Step - 2Step - 2

Step - 3Step - 3

Generate a provenance graph for the data itemGenerate a provenance graph for the data item

Annotate the provenance graph with impact valuesAnnotate the provenance graph with impact values

Execute the assessment function/program (script) Execute the assessment function/program (script)

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Generate a Provenance Graph

1. What types of provenance elements are necessarily require?

1. What types of details (i.e. granularity) are necessarily require?

2. Where and how do we get provenance information?

Two complementary options Recordings Analyzing the metadata

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Annotation with Impact Values

1. How might each Provenance element can influence the quality of data?

Each type of element has to analyze systematically

1. 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

1. How do we determine the impact values? 31

Determine the Impact Values

1. From Provenance Information2. From user Input

Rating-based systems, or reputation-based systems Configuration options

1. Through Content Analysis Comparison of data contents Adoption of information retrieval methods Adoption of data cleansing techniques

2. Through Context Analysis Further metadata Domain knowledge

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Annotation with Impact Values

How might each Provenance element can influence the quality of data?

Provenance Element Type

Creation Date

Creation Guidelines

Source data items

Data creator

Impact Values

Creation time

Weights

Expiry time

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Assessment Function (s)

1. 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

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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

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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

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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

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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

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Thanks for your attentions !

Freie University BerlinComputer Science DepartmentSoftware Engineering Research GroupTakuStr 9, Berlin, Germany.

Any Questions?

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References1. W3C Website, What is provenance? Modified at November 2010,

http://www.w3.org/2005/Incubator/prov/wiki/What_Is_Provenance2. 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_Provenance3. Hartig, O. Provenance information in the Web of data. In Proceedings of LDOW 2009 (Madrid, Spain,

April 2009).4. 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 Provenance5. D. Brickley and L. Miller, FOAF Vocabulary Specification, November 2007. http://xmlns.com/foaf/spec6. U. Bojars and J. G. Breslin. SIOC Core Ontology Specification, Revision 1.30, Jan. 2009.

http://rdfs.org/sioc/spec/7. 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. 8. 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, 2009.9. You-Wei cheah, Beth Plale. Provenance Analysis: Towards qaulity provenance. In proceeding of 8 th IEEE

International conference on eScience, Chicago Illinois, Oct. 2012. http://www.ci.uchicago.edu/escience2012/pdf/Provenance_Analysis-Towards_Quality_Provenance.pdf

10. Yogesh Simmhan, Beth Plale, and Dennis Gannon. A survey of data provenance in e-science. SIGMOD Record, 34(3):31–36, 2005.

11. 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.

12. 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.

13. 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.

14. Prat, N., and Madnick, S. Measuring data believability: A provenance approach. Proceedings of HICSS-41 (Big Island, HI, January 2008), IEEE, p.1-10.

15. 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

16. Website : http://www.w3.org/2005/Incubator/prov/wiki/images/0/02/Provenance-XG-Overview.pdf17. http://www.w3.org/2005/Incubator/prov/wiki/Provenance_Dimensions18. http://www.w3.org/2005/Incubator/prov/wiki/W3C_Provenance_Incubator_Group_Wiki