From Scientific Workflows to Research Objects: Publication and Abstraction of Scientific Experiments
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This document discusses the transition from scientific workflows to research objects, focusing on the representation, publication, and abstraction of scientific experiments. It outlines the motivation for this research, which includes improving understandability and reuse of workflows, and presents methodologies for detecting common motifs and fragments within workflows. The concept of research objects is introduced as a means of aggregating various resources related to a research study.
![What are Scientific Workflows?
•“Template defining the set of tasks needed to
carry out a computational experiment” [1]
•Inputs
•Steps
•Intermediate results
•Outputs
•Data driven, usually represented as Directed
Acyclic Graphs (DAGs)
[1] Ewa Deelman, Dennis Gannon, Matthew Shields, Ian Taylor, Workflows and e-science: an overview of workflow
system features and capabilities, Future Generation Computer Systems 25 (5) (2009) 528–540.
3](https://image.slidesharecdn.com/presentacionucm12-02-2014-140211113921-phpapp01/85/From-Scientific-Workflows-to-Research-Objects-Publication-and-Abstraction-of-Scientific-Experiments-3-320.jpg)
![Challenges: Common workflow fragment detection
•Given a collection of workflows, which are the most common fragments?
•Common sub-graphs among the collection
•Sub-graph isomorphism (NP-complete)
•We use the SUBDUE algorithm [Holder et al 1994] (hierachical clustering)
•Graph Grammar learning
•The rules of the grammar are the workflow fragments
•Graph based hierarchical clustering
•Each cluster corresponds to a workflow fragment
•Iterative algorithm with two measures for compressing the graph:
•Minimum Description Length (MDL)
•Size
•Current tests with PAFI (http://glaros.dtc.umn.edu/gkhome/pafi/overview)
ongoing.
[Holder et al 1994]: Substructure Discovery in the SUBDUE System L. B. Holder, D. J. Cook, and S. Djoko. AAAI
Workshop on Knowledge Discovery, pages 169-180, 1994.
27](https://image.slidesharecdn.com/presentacionucm12-02-2014-140211113921-phpapp01/85/From-Scientific-Workflows-to-Research-Objects-Publication-and-Abstraction-of-Scientific-Experiments-27-320.jpg)
From Scientific Workflows to Research Objects: Publication and Abstraction of Scientific Experiments
- 1. From Scientific Workflows to Research Objects: Publication and Abstraction of Scientific Experiments Daniel Garijo Verdejo Supervisors: Oscar Corcho, Yolanda Gil Ontology Engineering Group Departamento de Inteligencia Artificial Facultad de Informática Universidad Politécnica de Madrid Date: 12/02/2014
- 2. Outline Index 1. 2. 3. 4. 5. Background What do I do? Motivation Overview Representing and publishing scientific workflows in the Web • • • Linked Data Templates and provenance traces Standards 6. Common motifs among scientific workflows • Workflow motif catalog 7. Detecting common fragments among scientific workflows 8. Workflows as part of an experiment: Research Objects 2
- 3. What are Scientific Workflows? •“Template defining the set of tasks needed to carry out a computational experiment” [1] •Inputs •Steps •Intermediate results •Outputs •Data driven, usually represented as Directed Acyclic Graphs (DAGs) [1] Ewa Deelman, Dennis Gannon, Matthew Shields, Ian Taylor, Workflows and e-science: an overview of workflow system features and capabilities, Future Generation Computer Systems 25 (5) (2009) 528–540. 3
- 4. How are scientific workflows created? Laboratory Protocol (recipe) Experiment Lab book Workflow Digital Log • Similar to Laboratory Protocols 4
- 5. What am I working on? •Workflow representation •Plan/template representation •Provenance trace representation •Link between templates and traces CH1: Can we export an abstract template of the method being represented? CH2: How do we interoperate with other workflow results? CH3: How do we access the workflow results? •Creation of abstractions/motifs in scientific workflows •Abstraction catalog •Find how different workflows are related CH4: How can we detect what are the typical operations in scientific workflows? CH5: How can we detect them automatically? •Understandability and reuse of scientific workflows •Relation between the workflows involved in the same experiment (Research Objects) CH6: Which workflow parts are related to each other? CH7: How do workflows depend on the other parts of the experiments? 5
- 6. Motivation •As a designer: Discovery •Workflows with similar functionality fragments/methods •Design based in previous templates. •As user/reuser: Understandability, Exploration •Search workflows by functionality •Commonalities between execution runs Workflow 1 •Component categorization 6
- 7. Overview Abstraction definitions and categorization Descriptions/ PSMs/Ontologies Data mining tools, graph analysis, etc. Algorithms for finding the different abstractions automatically RDF Stores Experiment publication Provenance representation Plan representation Vocabularies 7
- 9. Representing and publishing scientific workflows in the Web Representing and publishing scientific workflows in the Web A New Approach for Publishing Workflows: Abstractions, Standards, and Linked Data. Garijo, D.; and Gil, Y. In Proceedings of the 6th workshop on Workflows in support of large-scale science, page 47-56, Seattle, 2011. ACM 9
- 10. Overview Abstractions definitions and categorization Algorithms for finding the different abstractions automatically Experiment Publication Provenance representation Plan representation Virtuoso, Pubby, Wings (+Plugin) OPMW 10
- 11. What is Linked Data? 1. Use URIs as names for things. 2. Use HTTP URIs so that people can look up those names. 3. When someone looks up a URI, provide useful information. 4. Include links to other URIs. “Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/” 8
- 12. Publishing workflows: high level architecture Other workflow environments WINGS on local laptop Core Portal Workflow Template OPM export Workflow Instance Programatic access (external apps) WINGS on shared host Core Portal Workflow Template OPM export Workflow Instance Linked Data Publication WINGS on web server Core Portal Wings workflow generation Workflow Template Workflow Instance Interactive Browsing (Pubby frontend) OPM export OPM conversion Users Publication Share Reuse 12
- 15. Representing and publishing scientific workflows in the Web Standards PROV-O: The PROV Ontology. Lebo, T.; Sahoo, S.; McGuinness, D.; Belhajjame, K.; Corsar, D.; Cheney, J.; Garijo, D.; Soiland-Reyes, S.; Zednik, S.; and Zhao, J. W3C Consortium. 2012. 15
- 16. Overview Abstractions definitions and categorization Algorithms for finding the different abstractions automatically Experiment Publication Provenance representation Plan representation Virtuoso, Pubby, Wings (+Plugin) OPMW + PROV + P-PLAN 16
- 17. P-PLAN •Plans are not provenance •P-PLAN: Simple plan model for binding traces to template representations •Aligned with OPMW and PROV (W3C Provenance Standard) Augmenting PROV with Plans in P-PLAN: Scientific Processes as Linked Data. Garijo, D.; and Gil, Y. In Proceedings of the 2nd International Workshop on Linked Science 2012, Boston, 2012. 17
- 18. Representing and publishing scientific workflows in the Web Common motifs among scientific workflows Common motifs in scientific workflows: An empirical analysis. Garijo, D.; Alper, P.; Belhajjame, K.; Corcho, O.; Gil, Y.; and Goble, C. Future Generation Computer Systems, . 2013 18
- 19. Overview Abstractions definitions and categorization Motif Detection Algorithms for automatic matching Experiment Publication Provenance representation Plan representation Virtuoso, Pubby, Wings (+Plugin) OPMW 19
- 20. Overview • Empirical analysis on 260 workflow templates from Taverna, Wings, Galaxy and Vistrails • Catalog of recurring patterns: scientific workflow motifs. • Data Oriented Motifs • Workflow Oriented Motifs •Understandability and reuse http://sensefinancial.com/wp-content/uploads/2012/02/contribution.jpg 20
- 21. Approach •Reverse-engineer the set of current practices in workflow development through an analysis of empirical evidence •Identify workflow abstractions that would facilitate understandability and therefore effective re-use 21
- 22. Workflow Motifs •Workflow motif: Domain independent conceptual abstraction on the workflow steps. 1. Data-oriented motifs: What kind of manipulations does the workflow have? •E.g.: •Data retrieval •Data preparation • etc. 2. WHAT? Workflow-oriented motifs: How does the workflow perform its operations? •E.g.: •Stateful steps •Stateless steps •Human interactions •etc. HOW? 22
- 23. Motif Catalog Data-Oriented Motifs Workflow-Oriented Motifs Data Retrieval Data Preparation Intra-Workflow Motifs Stateful (Asynchronous) Invocations Format Transformation Stateless (Synchronous) Invocations Input Augmentation and Output Splitting Internal Macros Data Organisation Data Analysis Data Curation/Cleaning Data Moving Data Visualisation Human Interactions Inter-Workflow Motifs Atomic Workflows Composite Workflows Workflow Overloading Ontology Purl: http://purl.org/net/wf-motifs 23
- 24. Representing and publishing scientific workflows in the Web Detecting common fragments among scientific workflows (macro motifs) Detecting common scientific workflow fragments using execution provenance. Garijo, D.; Corcho, O.; and Gil, Y. In Proceedings of of the seventh international conference on Knowledge capture, page 33-40, Banff, 2013. ACM. 24
- 25. Summary: Work done at ISI Abstractions definitions and categorization Algorithms for automatic matching Macro abstraction detection Experiment Publication Provenance representation Plan representation Motif Detection SUBDUE + PAFI exploration and integration in RDF Virtuoso, Pubby, Wings (+Plugin) OPMW + PROV + P-PLAN 25
- 26. Macro abstraction detection Problem statement: Given a repository of workflow templates (either abstract or specific) or workflow execution traces, what are the workflow fragments I can deduce from it? Useful for: •Systems like Taverna and Wings: (Many templates, little annotation to relate them) •Finding relationships between workflows and sub-workflows. •Most used fragments, most executed, etc. •Systems like GenePattern and Galaxy: (Many runs, nearly no templates published) •Proposing new templates with the popular fragments. 26
- 27. Challenges: Common workflow fragment detection •Given a collection of workflows, which are the most common fragments? •Common sub-graphs among the collection •Sub-graph isomorphism (NP-complete) •We use the SUBDUE algorithm [Holder et al 1994] (hierachical clustering) •Graph Grammar learning •The rules of the grammar are the workflow fragments •Graph based hierarchical clustering •Each cluster corresponds to a workflow fragment •Iterative algorithm with two measures for compressing the graph: •Minimum Description Length (MDL) •Size •Current tests with PAFI (http://glaros.dtc.umn.edu/gkhome/pafi/overview) ongoing. [Holder et al 1994]: Substructure Discovery in the SUBDUE System L. B. Holder, D. J. Cook, and S. Djoko. AAAI Workshop on Knowledge Discovery, pages 169-180, 1994. 27
- 28. How does SUBDUE work? DatasetT1 DatasetT1 ProcessType1 ProcessType1 DatasetT2 DatasetT2 DatasetT2 ProcessType2 ProcessType2 ProcessType2 DatasetT3 DatasetT3 DatasetT3 ProcessType3 Input Graph DatasetT3 28
- 29. How does SUBDUE work? DatasetT1 DatasetT1 ProcessType1 ProcessType1 DatasetT2 DatasetT2 DatasetT2 ProcessType2 ProcessType2 ProcessType2 DatasetT3 DatasetT3 DatasetT3 ProcessType3 Fragment1 Iteration 1 DatasetT3 29
- 30. How does SUBDUE work? DatasetT1 DatasetT1 ProcessType1 ProcessType1 FRAG1 FRAG1 ProcessType3 DatasetT3 FRAG1 Iteration 1 result 30
- 31. How does SUBDUE work? DatasetT1 DatasetT1 ProcessType1 ProcessType1 FRAG1 FRAG1 ProcessType3 Fragment2 FRAG1 Iteration 2 DatasetT3 31
- 32. How does SUBDUE work? FRAG2 FRAG2 ProcessType3 DatasetT3 FRAG1 Iteration 2 result (STOP) 32
- 33. How does SUBDUE work? Results: Fragment 1 (FRAG1) : Fragment 2 (FRAG2): DatasetT2 DatasetT1 ProcessType2 DatasetT3 Occurrences: 3 times ProcessType1 FRAG1 2 times 33
- 34. Challenges: Generalization of workflows Stemmer Porter Stemmer Term Weighting Lovins Stemmer TF DF CF 34
- 35. Research Objects Workflow-Centric Research Objects: First Class Citizens in Scholarly Discourse. Belhajjame, K.; Corcho, O.; Garijo, D.; Zhao, J.; Missier, P.; Newman, D.; Palma, R.; Bechhofer, S.; Garcıa, E.; Manuel, .G. J.; Klyne, G.; Page, K.; Roos, M.; Ruiz, J. E.; Soiland-Reyes, S.; Verdes-Montenegro, L.; De Roure, D.; and Goble, C. In Proceedings of the Second International Conference on the Future of Scholarly Communication and Scientific Publishing Sepublica2012, page 1-12, Hersonissos, 2012 35
- 36. What is a Research Object? •Aggregation of resources that bundles together the contents of a research work: •Data •Experiments •Examples •Bibliography •Annotations •Provenance •ROs •Etc. 36
- 37. Research Objects: An Overview + Open Annotation •Tool support •Interoperability http://www.openannotation.org/spec/core/ 37
- 38. What can you find in a Research Object? A real example TPDL 2013, Valleta, Malta. 3
- 39. Thanks ! :oscarCorcho :yolandGil :supervises :idafenSantana :supervises Wf Infrastructure OEG :danielGarijo :collaboratesWith :olgaGiraldo :varunRatnakar :supervises :collaboratesWith :collaboratesWith :collaboratesWith :collaboratesWith Laboratory Protocols :collaboratesWith :caroleGoble :khalidBelhajjame :pinarAlper 39
- 40. From Scientific Workflows to Research Objects: Publication and Abstraction of Scientific Experiments Daniel Garijo Verdejo Supervisors: Oscar Corcho, Yolanda Gil Ontology Engineering Group Departamento de Inteligencia Artificial Facultad de Informática Universidad Politécnica de Madrid Date: 12/02/2014