The ready availability of data is leading to the increased opportunity of their re-use for new applications and for analyses. Most of these data are not necessarily in the format users want, are usually heterogeneous, and highly dynamic, and this necessitates data transformation efforts to re-purpose them. Interactive data transformation (IDT) tools are becoming easily available to lower these barriers to data transformation efforts. This paper describes a principled way to capture data lineage of interactive data transformation processes. We provide a formal model of IDT, its mapping to a provenance representation, and its implementation and validation on Google Refine. Provision of the data transformation process sequences allows assessment of data quality and ensures portability between IDT and other data transformation platforms. The proposed model showed a high level of coverage against a set of requirements used for evaluating systems that provide provenance management solutions.

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
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 Motivation
 Interactive data transformations (IDTs)
 IDT & Provenance
 Modelling IDTs
 Provenance Representation
 Provenance Capture
 Case Study
 Conclusion

3. Motivation
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 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)
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 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)
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6. Challenges
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 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
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 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
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 IDT model
 Formal model (Algebra for IDT)
 Provenance representation
 Provenance capture of IDTs

9. IDT Model: Core Elements
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 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
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11. Formalizing the mapping from IDT to
Provenance
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 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
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 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
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15. Provenance Event-Capture Sequence Flow
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16. Case study
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 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
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 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