SlideShare ist ein Scribd-Unternehmen logo
1 von 17
Downloaden Sie, um offline zu lesen
Digital Enterprise Research Institute                                          www.deri.ie




            Capturing interactive data transformation
             operations using provenance workflows

             Tope Omitola, Andre Freitas, Edward Curry, Sean
             O'Riain, Nicholas Gibbins and Nigel Shadbolt



  SWPM Workshop 28.05.2012, Herakleion, Crete


 Copyright 2009 Digital Enterprise Research Institute. All rights reserved.
Outline
Digital Enterprise Research Institute                 www.deri.ie




           Motivation
           Interactive data transformations (IDTs)
           IDT & Provenance
           Modelling IDTs
           Provenance Representation
           Provenance Capture
           Case Study
           Conclusion
Motivation
Digital Enterprise Research Institute                                  www.deri.ie




           Dataspaces:
                 High number of heterogeneous data sources
                 Complex data transformation environment
                 Need for both repeatable data transformations and once-
                  off transformations
           Traditional    ETL     approaches                 for     data
            transformation/integration:
                 Based on scripting/programming
                 Focus on repeatable data transformation processes
Interactive Data Transformation (IDTs)
Digital Enterprise Research Institute                   www.deri.ie




        Based on user interaction paradigms for user
         creation of data transformations
        Explores    GUI    elements    mapping   to   data
         transformation operations
        Instant feedback of each iteration
        Complementary to existing ETL tools
        Lower the barriers for non-programmers (reduces
         programming effort) of doing data transformations
        Example platforms: Google Refine, Potters Wheel,
         Wrangler
Interactive Data Transformation (IDTs)
Digital Enterprise Research Institute      www.deri.ie
Challenges
Digital Enterprise Research Institute                            www.deri.ie




           How to model IDTs?

           Facilitating the reuse of previous IDTs

           Representing IDTs
                                                           Provenance

           Making IDT platforms provenance-aware

           Enabling transportability across IDT and ETL
            platforms
IDT & Provenance
Digital Enterprise Research Institute                     www.deri.ie




           Provenance supports representation of interactive
            data transformations
           Output: a provenance descriptor which shows the
            relationship between the inputs, the outputs, and
            the applied transformation operations
           Both retrospective and prospective provenance
IDT
Digital Enterprise Research Institute        www.deri.ie




           IDT model
           Formal model (Algebra for IDT)
           Provenance representation
           Provenance capture of IDTs
IDT Model: Core Elements
Digital Enterprise Research Institute                       www.deri.ie




           Schema and instance data
           Set of predefined operations
           GUI elements mapping to predefined operations
           User actions
                 Operation selection
                 Parameter selection
                 Operation composition (workflow)
IDT Model
Digital Enterprise Research Institute   www.deri.ie
Formalizing the mapping from IDT to
     Provenance
Digital Enterprise Research Institute                        www.deri.ie




           Definition 1: A provenance-based interactive data
            transformation engine, consists of a set of
            transformations (or activities) on a set of datasets
            generating outputs in the form of other datasets or
            events which may trigger further transformations

           Definition 2: An interactive data transformation
            event, consists of the input dataset, the output
            dataset(s), the applied transformation function,
            and the time the transformation took place
Formalizing the mapping from IDT to
        Provenance
Digital Enterprise Research Institute                       www.deri.ie




           Definition 3: A run is a function from time to
            dataset(s) and the transformation applied to those
            dataset(s)

           Definition 4: A trace is the sequence of pairs of a
            run and the time the run was made
Provenance Representation
Digital Enterprise Research Institute                      www.deri.ie




           Proposed in Representing Interoperable Provenance
            Descriptions for ETL Workflows

           Three-layered provenance model:
                 Open Provenance Model Vocabulary Layer
                 Cogs ETL Provenance Vocabulary
                 Domain-Specific Model Layer


           Linked Data standards
Provenance Capture Layers
Digital Enterprise Research Institute   www.deri.ie
Provenance Event-Capture Sequence Flow
Digital Enterprise Research Institute    www.deri.ie
Case study
Digital Enterprise Research Institute                                                                                    www.deri.ie




        Implementation over the GR Platform
        Example descriptor

   @prefix grf: <http://127.0.0.1:3333/project/1402144365904/> .

   grf :MassCellChange-1092380975 rdf:type opmv:Process,
   cogs:ColumnOperation, cogs:Transformation;                                 Mapping to the actual program
   cogs:operationName "MassCellChange"^^xsd:string;
   cogs:programUsed "com.google.refine.operations.cell.MassEditOperation"^^xsd:string;                  Process
   rdfs:label "Mass edit 1 cells in column ==List of winners=="^^xsd:string.

   grf:MassCellChange-1092380975/1_0 rdf:type opmv:Artifact ;                                                       Input Artifact
   rdfs:label "* '''1955 [[Meena Kumari]]'[[Parineeta (1953 film)|Parineeta]]''''' as '''Lolita'''"^^xsd:string.

   grf:MassCellChange-1092380975/1_1 rdf:type opmv:Artifact;                                                       Output Artifact
   rdfs:label "* '''John Wayne'''"^^xsd:string.
                                                                                                            Workflow structure
   grf:MassCellChange-1092380975/1_1 opmv:wasDerivedFrom grf:MassCellChange-1092380975/1_0.
   grf:MassCellChange-1092380975 opmv:used grf:MassCellChange-1092380975/1_0.
   grf:MassCellChange-1092380975/1_1 opmv:wasGeneratedBy grf:MassCellChange-1092380975.
   grf:MassCellChange-1092380975/1_1 opmv:wasGeneratedAt "2011-11-16T11:2:14"^xsd: dateTime.
Conclusion
Digital Enterprise Research Institute                     www.deri.ie




           The proposed approach provides low impact on the
            existing IDT process
           Provenance representation supports different data
            models
           Preliminary implementation of a Google Refine
            provenance extension

Weitere ähnliche Inhalte

Ähnlich wie Omitola o rian_eswc_idts final

Approximate Semantic Matching of Heterogeneous Events
Approximate Semantic Matching of Heterogeneous EventsApproximate Semantic Matching of Heterogeneous Events
Approximate Semantic Matching of Heterogeneous EventsEdward Curry
 
Approximate Semantic Matching of Heterogeneous Events
Approximate Semantic Matching of Heterogeneous EventsApproximate Semantic Matching of Heterogeneous Events
Approximate Semantic Matching of Heterogeneous EventsSouleiman Hasan
 
Considerations for Abstracting Complexities of a Real-Time ML Platform, Zhenz...
Considerations for Abstracting Complexities of a Real-Time ML Platform, Zhenz...Considerations for Abstracting Complexities of a Real-Time ML Platform, Zhenz...
Considerations for Abstracting Complexities of a Real-Time ML Platform, Zhenz...HostedbyConfluent
 
Data virtualization an introduction
Data virtualization an introductionData virtualization an introduction
Data virtualization an introductionDenodo
 
FAIR Computational Workflows
FAIR Computational WorkflowsFAIR Computational Workflows
FAIR Computational WorkflowsCarole Goble
 
Towards Lightweight Cyber-Physical Energy Systems using Linked Data, the Web ...
Towards Lightweight Cyber-Physical Energy Systems using Linked Data, the Web ...Towards Lightweight Cyber-Physical Energy Systems using Linked Data, the Web ...
Towards Lightweight Cyber-Physical Energy Systems using Linked Data, the Web ...Edward Curry
 
FIWARE Global Summit - IDS Implementation with FIWARE Software Components
FIWARE Global Summit - IDS Implementation with FIWARE Software ComponentsFIWARE Global Summit - IDS Implementation with FIWARE Software Components
FIWARE Global Summit - IDS Implementation with FIWARE Software ComponentsFIWARE
 
An Introduction to Data Virtualization in 2018
An Introduction to Data Virtualization in 2018An Introduction to Data Virtualization in 2018
An Introduction to Data Virtualization in 2018Denodo
 
Flash session -streaming--ses1243-lon
Flash session -streaming--ses1243-lonFlash session -streaming--ses1243-lon
Flash session -streaming--ses1243-lonJeffrey T. Pollock
 
Usage Landscape of Enterprise Open Source Data Integration
Usage Landscape of Enterprise Open Source Data IntegrationUsage Landscape of Enterprise Open Source Data Integration
Usage Landscape of Enterprise Open Source Data IntegrationOKTOPUS Consulting
 
MLOps - Build pipelines with Tensor Flow Extended & Kubeflow
MLOps - Build pipelines with Tensor Flow Extended & KubeflowMLOps - Build pipelines with Tensor Flow Extended & Kubeflow
MLOps - Build pipelines with Tensor Flow Extended & KubeflowJan Kirenz
 
Strata 2017 (San Jose): Building a healthy data ecosystem around Kafka and Ha...
Strata 2017 (San Jose): Building a healthy data ecosystem around Kafka and Ha...Strata 2017 (San Jose): Building a healthy data ecosystem around Kafka and Ha...
Strata 2017 (San Jose): Building a healthy data ecosystem around Kafka and Ha...Shirshanka Das
 
Building a healthy data ecosystem around Kafka and Hadoop: Lessons learned at...
Building a healthy data ecosystem around Kafka and Hadoop: Lessons learned at...Building a healthy data ecosystem around Kafka and Hadoop: Lessons learned at...
Building a healthy data ecosystem around Kafka and Hadoop: Lessons learned at...Yael Garten
 
Why Data Virtualization? An Introduction
Why Data Virtualization? An IntroductionWhy Data Virtualization? An Introduction
Why Data Virtualization? An IntroductionDenodo
 
Denodo DataFest 2016: Data Science: Operationalizing Analytical Models in Rea...
Denodo DataFest 2016: Data Science: Operationalizing Analytical Models in Rea...Denodo DataFest 2016: Data Science: Operationalizing Analytical Models in Rea...
Denodo DataFest 2016: Data Science: Operationalizing Analytical Models in Rea...Denodo
 
Webinar september 2013
Webinar september 2013Webinar september 2013
Webinar september 2013Marc Gille
 
Architecting for change: LinkedIn's new data ecosystem
Architecting for change: LinkedIn's new data ecosystemArchitecting for change: LinkedIn's new data ecosystem
Architecting for change: LinkedIn's new data ecosystemYael Garten
 
Strata 2016 - Architecting for Change: LinkedIn's new data ecosystem
Strata 2016 - Architecting for Change: LinkedIn's new data ecosystemStrata 2016 - Architecting for Change: LinkedIn's new data ecosystem
Strata 2016 - Architecting for Change: LinkedIn's new data ecosystemShirshanka Das
 
Data Virtualization: An Introduction
Data Virtualization: An IntroductionData Virtualization: An Introduction
Data Virtualization: An IntroductionDenodo
 
FAIR Computational Workflows
FAIR Computational WorkflowsFAIR Computational Workflows
FAIR Computational WorkflowsCarole Goble
 

Ähnlich wie Omitola o rian_eswc_idts final (20)

Approximate Semantic Matching of Heterogeneous Events
Approximate Semantic Matching of Heterogeneous EventsApproximate Semantic Matching of Heterogeneous Events
Approximate Semantic Matching of Heterogeneous Events
 
Approximate Semantic Matching of Heterogeneous Events
Approximate Semantic Matching of Heterogeneous EventsApproximate Semantic Matching of Heterogeneous Events
Approximate Semantic Matching of Heterogeneous Events
 
Considerations for Abstracting Complexities of a Real-Time ML Platform, Zhenz...
Considerations for Abstracting Complexities of a Real-Time ML Platform, Zhenz...Considerations for Abstracting Complexities of a Real-Time ML Platform, Zhenz...
Considerations for Abstracting Complexities of a Real-Time ML Platform, Zhenz...
 
Data virtualization an introduction
Data virtualization an introductionData virtualization an introduction
Data virtualization an introduction
 
FAIR Computational Workflows
FAIR Computational WorkflowsFAIR Computational Workflows
FAIR Computational Workflows
 
Towards Lightweight Cyber-Physical Energy Systems using Linked Data, the Web ...
Towards Lightweight Cyber-Physical Energy Systems using Linked Data, the Web ...Towards Lightweight Cyber-Physical Energy Systems using Linked Data, the Web ...
Towards Lightweight Cyber-Physical Energy Systems using Linked Data, the Web ...
 
FIWARE Global Summit - IDS Implementation with FIWARE Software Components
FIWARE Global Summit - IDS Implementation with FIWARE Software ComponentsFIWARE Global Summit - IDS Implementation with FIWARE Software Components
FIWARE Global Summit - IDS Implementation with FIWARE Software Components
 
An Introduction to Data Virtualization in 2018
An Introduction to Data Virtualization in 2018An Introduction to Data Virtualization in 2018
An Introduction to Data Virtualization in 2018
 
Flash session -streaming--ses1243-lon
Flash session -streaming--ses1243-lonFlash session -streaming--ses1243-lon
Flash session -streaming--ses1243-lon
 
Usage Landscape of Enterprise Open Source Data Integration
Usage Landscape of Enterprise Open Source Data IntegrationUsage Landscape of Enterprise Open Source Data Integration
Usage Landscape of Enterprise Open Source Data Integration
 
MLOps - Build pipelines with Tensor Flow Extended & Kubeflow
MLOps - Build pipelines with Tensor Flow Extended & KubeflowMLOps - Build pipelines with Tensor Flow Extended & Kubeflow
MLOps - Build pipelines with Tensor Flow Extended & Kubeflow
 
Strata 2017 (San Jose): Building a healthy data ecosystem around Kafka and Ha...
Strata 2017 (San Jose): Building a healthy data ecosystem around Kafka and Ha...Strata 2017 (San Jose): Building a healthy data ecosystem around Kafka and Ha...
Strata 2017 (San Jose): Building a healthy data ecosystem around Kafka and Ha...
 
Building a healthy data ecosystem around Kafka and Hadoop: Lessons learned at...
Building a healthy data ecosystem around Kafka and Hadoop: Lessons learned at...Building a healthy data ecosystem around Kafka and Hadoop: Lessons learned at...
Building a healthy data ecosystem around Kafka and Hadoop: Lessons learned at...
 
Why Data Virtualization? An Introduction
Why Data Virtualization? An IntroductionWhy Data Virtualization? An Introduction
Why Data Virtualization? An Introduction
 
Denodo DataFest 2016: Data Science: Operationalizing Analytical Models in Rea...
Denodo DataFest 2016: Data Science: Operationalizing Analytical Models in Rea...Denodo DataFest 2016: Data Science: Operationalizing Analytical Models in Rea...
Denodo DataFest 2016: Data Science: Operationalizing Analytical Models in Rea...
 
Webinar september 2013
Webinar september 2013Webinar september 2013
Webinar september 2013
 
Architecting for change: LinkedIn's new data ecosystem
Architecting for change: LinkedIn's new data ecosystemArchitecting for change: LinkedIn's new data ecosystem
Architecting for change: LinkedIn's new data ecosystem
 
Strata 2016 - Architecting for Change: LinkedIn's new data ecosystem
Strata 2016 - Architecting for Change: LinkedIn's new data ecosystemStrata 2016 - Architecting for Change: LinkedIn's new data ecosystem
Strata 2016 - Architecting for Change: LinkedIn's new data ecosystem
 
Data Virtualization: An Introduction
Data Virtualization: An IntroductionData Virtualization: An Introduction
Data Virtualization: An Introduction
 
FAIR Computational Workflows
FAIR Computational WorkflowsFAIR Computational Workflows
FAIR Computational Workflows
 

Kürzlich hochgeladen

08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking MenDelhi Call girls
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoffsammart93
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonetsnaman860154
 
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsJoaquim Jorge
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...apidays
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherRemote DBA Services
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024The Digital Insurer
 
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdfhans926745
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreternaman860154
 
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)wesley chun
 
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...Neo4j
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptxHampshireHUG
 
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Drew Madelung
 
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationScaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationRadu Cotescu
 
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptxEIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptxEarley Information Science
 
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemkeProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemkeProduct Anonymous
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slidevu2urc
 
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Enterprise Knowledge
 
Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024The Digital Insurer
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerThousandEyes
 

Kürzlich hochgeladen (20)

08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
 
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
 
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreter
 
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)
 
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
 
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
 
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationScaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
 
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptxEIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
 
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemkeProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slide
 
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...
 
Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 

Omitola o rian_eswc_idts final

  • 1. Digital Enterprise Research Institute www.deri.ie Capturing interactive data transformation operations using provenance workflows Tope Omitola, Andre Freitas, Edward Curry, Sean O'Riain, Nicholas Gibbins and Nigel Shadbolt SWPM Workshop 28.05.2012, Herakleion, Crete  Copyright 2009 Digital Enterprise Research Institute. All rights reserved.
  • 2. Outline Digital Enterprise Research Institute www.deri.ie  Motivation  Interactive data transformations (IDTs)  IDT & Provenance  Modelling IDTs  Provenance Representation  Provenance Capture  Case Study  Conclusion
  • 3. Motivation Digital Enterprise Research Institute www.deri.ie  Dataspaces:  High number of heterogeneous data sources  Complex data transformation environment  Need for both repeatable data transformations and once- off transformations  Traditional ETL approaches for data transformation/integration:  Based on scripting/programming  Focus on repeatable data transformation processes
  • 4. Interactive Data Transformation (IDTs) Digital Enterprise Research Institute www.deri.ie  Based on user interaction paradigms for user creation of data transformations  Explores GUI elements mapping to data transformation operations  Instant feedback of each iteration  Complementary to existing ETL tools  Lower the barriers for non-programmers (reduces programming effort) of doing data transformations  Example platforms: Google Refine, Potters Wheel, Wrangler
  • 5. Interactive Data Transformation (IDTs) Digital Enterprise Research Institute www.deri.ie
  • 6. Challenges Digital Enterprise Research Institute www.deri.ie  How to model IDTs?  Facilitating the reuse of previous IDTs  Representing IDTs Provenance  Making IDT platforms provenance-aware  Enabling transportability across IDT and ETL platforms
  • 7. IDT & Provenance Digital Enterprise Research Institute www.deri.ie  Provenance supports representation of interactive data transformations  Output: a provenance descriptor which shows the relationship between the inputs, the outputs, and the applied transformation operations  Both retrospective and prospective provenance
  • 8. IDT Digital Enterprise Research Institute www.deri.ie  IDT model  Formal model (Algebra for IDT)  Provenance representation  Provenance capture of IDTs
  • 9. IDT Model: Core Elements Digital Enterprise Research Institute www.deri.ie  Schema and instance data  Set of predefined operations  GUI elements mapping to predefined operations  User actions  Operation selection  Parameter selection  Operation composition (workflow)
  • 10. IDT Model Digital Enterprise Research Institute www.deri.ie
  • 11. Formalizing the mapping from IDT to Provenance Digital Enterprise Research Institute www.deri.ie  Definition 1: A provenance-based interactive data transformation engine, consists of a set of transformations (or activities) on a set of datasets generating outputs in the form of other datasets or events which may trigger further transformations  Definition 2: An interactive data transformation event, consists of the input dataset, the output dataset(s), the applied transformation function, and the time the transformation took place
  • 12. Formalizing the mapping from IDT to Provenance Digital Enterprise Research Institute www.deri.ie  Definition 3: A run is a function from time to dataset(s) and the transformation applied to those dataset(s)  Definition 4: A trace is the sequence of pairs of a run and the time the run was made
  • 13. Provenance Representation Digital Enterprise Research Institute www.deri.ie  Proposed in Representing Interoperable Provenance Descriptions for ETL Workflows  Three-layered provenance model:  Open Provenance Model Vocabulary Layer  Cogs ETL Provenance Vocabulary  Domain-Specific Model Layer  Linked Data standards
  • 14. Provenance Capture Layers Digital Enterprise Research Institute www.deri.ie
  • 15. Provenance Event-Capture Sequence Flow Digital Enterprise Research Institute www.deri.ie
  • 16. Case study Digital Enterprise Research Institute www.deri.ie  Implementation over the GR Platform  Example descriptor @prefix grf: <http://127.0.0.1:3333/project/1402144365904/> . grf :MassCellChange-1092380975 rdf:type opmv:Process, cogs:ColumnOperation, cogs:Transformation; Mapping to the actual program cogs:operationName "MassCellChange"^^xsd:string; cogs:programUsed "com.google.refine.operations.cell.MassEditOperation"^^xsd:string; Process rdfs:label "Mass edit 1 cells in column ==List of winners=="^^xsd:string. grf:MassCellChange-1092380975/1_0 rdf:type opmv:Artifact ; Input Artifact rdfs:label "* '''1955 [[Meena Kumari]]'[[Parineeta (1953 film)|Parineeta]]''''' as '''Lolita'''"^^xsd:string. grf:MassCellChange-1092380975/1_1 rdf:type opmv:Artifact; Output Artifact rdfs:label "* '''John Wayne'''"^^xsd:string. Workflow structure grf:MassCellChange-1092380975/1_1 opmv:wasDerivedFrom grf:MassCellChange-1092380975/1_0. grf:MassCellChange-1092380975 opmv:used grf:MassCellChange-1092380975/1_0. grf:MassCellChange-1092380975/1_1 opmv:wasGeneratedBy grf:MassCellChange-1092380975. grf:MassCellChange-1092380975/1_1 opmv:wasGeneratedAt "2011-11-16T11:2:14"^xsd: dateTime.
  • 17. Conclusion Digital Enterprise Research Institute www.deri.ie  The proposed approach provides low impact on the existing IDT process  Provenance representation supports different data models  Preliminary implementation of a Google Refine provenance extension