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Workflow discovery in e-science

Workflow discovery in e-science. Antoon Goderis Peter Li Carole Goble University of Manchester, UK www.cs.man.ac.uk/~goderisa. Agenda. Web services in science Workflow re-use Workflow discovery Is workflow discovery a new problem? How do people match up workflows?

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Workflow discovery in e-science

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  1. Workflow discovery in e-science Antoon Goderis Peter Li Carole Goble University of Manchester, UK www.cs.man.ac.uk/~goderisa

  2. Agenda • Web services in science • Workflow re-use • Workflow discovery • Is workflow discovery a new problem? • How do people match up workflows? • Can we replicate the behaviour with tools? • Conclusions

  3. Workflows Web services

  4. Science is highly distributed and connected

  5. The Web has revolutionised science

  6. Web services about to do the same?

  7. Scientific workflows • e-science = supporting scientists to encode, enact, explain and share experimental procedures featuring lots of specialised data • Case study: bioinformatics • Understanding the DNA to behaviour link • 3000 bio-services via the Taverna workflow editor http://mygrid.org.uk/taverna • Re-use and repurposing of workflows • +/- 200 Taverna workflows shared at fffff

  8. Scientific workflows • e-science = supporting scientists to encode, enact, explain and share experimental procedures • Case study: bioinformatics • Understanding the DNA to life link • 3000 bio-services via the Taverna workflow editor http://mygrid.org.uk/taverna • Re-use and repurposing of workflow fragments • +/- 200 Taverna workflows shared at fffff

  9. Manchester, CS dept Manchester Biology dept Newcastle, CS dept

  10. Scientific workflows • e-science = supporting scientists to encode, enact, explain and share experimental procedures • Case study: bioinformatics • Understanding the DNA to life link • 3000 bio-services via the Taverna workflow editor http://mygrid.org.uk/taverna • Re-use and repurposing of workflow fragments • +/- 200 Taverna workflows shared at www.myExperiment.org

  11. One + Three questions • Can’t we just do it with ? • Keyword search doesn’t seem to cut it • Is workflow discovery a new problem? • How do people match up workflows? • Can we replicate the behaviour with tools?

  12. myExperiment.org my current workflow

  13. ? myExperiment.org my current workflow

  14. 1. Is workflow discovery a new problem? Source: survey of 21 myGrid/Taverna users

  15. 1. Is workflow discovery a new problem? Yes Workflow discovery subsumes service discovery

  16. ? 2. How do people match up workflows?

  17. ? 3. Can we replicate the behaviour with tools? 1 + 2 3 ... 1 2 3

  18. ? A user experiment with bioinformatics workflows +

  19. ? Workflow discovery task • Can I sensibly adapt an existing experimental procedure (workflow) with another one? • Extend Replace +

  20. Workflow corpus • 66 similar workflows for Graves’ disease done by single author • 1 + 5 workflows • Workflow diagram • No documentation • No annotation 1 + 5

  21. By the experts, for the experts • 9 bioinformaticians and 4 developers at a Taverna training day

  22. Matching strategies • Matching input workflow with 5 others 2 1 ? 3 4 5

  23. Human on-line matching strategies! • Traits • Scores of attraction • Yes or no

  24. Matching strategy: traits From an analysis of 30 000 profiles

  25. Matching strategy: scoring Score Percentile Confidencelevel www.AmIHotOrNot.com

  26. Matching strategy: yes or no

  27. Traits • Predicted trait

  28. Traits and score • Predicted trait • Score of similarity, usefulness and confidence E.g. [1 Identical – 9 Not similar]

  29. ? The gold standard • The collection of workflow similarity assessments • Predictive traits, possibly interacting 1 + 5 Traits/score

  30. 2. How do people match up workflows? • Difficulty of task • Biological relationship very difficult for 6 out of 9 • Shape similarity difficult for 4 out of 13 • Medium confidence • Consistency • Inter participant disagreement on how to order biological similarity and shape similarity [Spearman rank order test] • Predictive traits • No one trait dominant between and within participants [Levene homogeneity of variance test]

  31. Can we do better? • Simpler tasks and workflows • Taverna experienced users • Workflow documentation and annotation • Other factors in use, e.g. size difference • Fix allowed factors • Adopt black box approach: yes/no matching

  32. Automated discovery technique • Unattributed graph matcher implementation by Messmer and Bunke • Sub-isomorphism detection; exponential time complexity • DAGs and optimization for repository of graphs • Workflows parsed as graphs • Workflow input, workflow output andintermediate services as nodes • Data links as edges probeSetid databaseid AffyMapper_seq Blastx Results_Blastx

  33. Automated discovery technique • Ranking based on • shared nodes • difference in size between input graph and repository graphs

  34. Average similarity assessments across participants ? 3. Can we replicate the behaviour with tools? Kind of.. + 1 + 66 Traits/score

  35. ? OWL workflow ontology Current work Precision / recall Graph matching Text clustering 1 + 2 12 + 21 3 ... 1 2 3 Yes/no

  36. Take home • Scientists compose Web services for real – and share their results • Workflow discovery is a real problem, which subsumes service discovery • A range of matching strategies and techniques apply • Evaluation is a challenge - gold standards hard to build • Come and play at myExperiment.org • References at www.cs.man.ac.uk/~goderisa

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