Understanding the utility and fitness of Workflow Provenance

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Understanding the utility and fitness of Workflow Provenance for Experiment Reporting Pınar Alper, Supervisor:

Understanding the utility and fitness of Workflow Provenance for Experiment Reporting Pınar Alper, Supervisor: Carole A. Goble 1

Research Local Data Analysis Tool Analysis Local Tool Reporting Results Results Results Results Data

Research Local Data Analysis Tool Analysis Local Tool Reporting Results Results Results Results Data share select Results package recollect publish Build a citation string Package results by origin C. Tenopir, S. Allard, et al. Data sharing by scientists: Practices and perceptions. PLo. S ONE, 6(6): e 21101, 06 2011. Document important run 2 parameteres

Provenance we have • Execution provenance Retrospective Prospective • WF description Generic information: Data

Provenance we have • Execution provenance Retrospective Prospective • WF description Generic information: Data artefacts, consumption/production relations 3 Execution times/status

Provenance that is reported Scientific Data Provenance – Origin – Methodological context – Scientific

Provenance that is reported Scientific Data Provenance – Origin – Methodological context – Scientific Context 4

Motifs Workflows as implementation artefacts: • 240 Workflows, 4 Systems 10 domains • A

Motifs Workflows as implementation artefacts: • 240 Workflows, 4 Systems 10 domains • A domain independent characterization of activities • ~90% characterizable Minority (~30%) Data-creation Majority (~70%) Data-preparation (value-copying http: //purl. org/net/wf-motifs# D Garijo, P Alper, K Belhajjame, O Corcho, Y Gil, C Goble, Common motifs in scientific workflows: An empirical analysis, Future Generation 5 Computer Systems. ISSN 0167 -739 X.

Research Framework Configurable filters Graph Re-write primitives More informed abstraction w. Motifs III II

Research Framework Configurable filters Graph Re-write primitives More informed abstraction w. Motifs III II WF Summaries Labeling WF WF Motifs I Groundtruth –user behavior Novelty: Declarative abstraction and contextual grouping Process Model for labeling Motifs inform when to collect when to propagate labels Novelty: Dynamic, domain specific Minimal additional design -time information High-level categorization, as Semantic Annotations Grey-box Based on empirical evidence Novelty: Partial transparency P Alper, C Goble, and K Belhajjame. 2013. On assisting scientific data curation in collection-based dataflows using labels. In Proceedings of the 8 th Workshop on Workflows in Su Large-Scale Science (WORKS '13). ACM, New York, NY, USA, 7 -16. DOI=10. 1145/2534248. 2534249 P Alper, K Belhajjame, C Goble, P Karagoz, Small Is Beautiful: Summarizing Scientific Workflows Using Semantic Annotations, IEEE Big Data, July 2013. 6

How do I use Taverna Workbench scufl 2 -api make a wf Inquire about

How do I use Taverna Workbench scufl 2 -api make a wf Inquire about details Scufl 2 -wfdesc we operate on abstract wf description Issues Additional characteristics (port depths, itertion config) Annotation support @UI w key-value pairs List handling representation Resource uniqueness 7

Thank you! Pinar ALPER University of Manchester Khalid BELHAJJAME Université Paris Dauphine Carole A.

Thank you! Pinar ALPER University of Manchester Khalid BELHAJJAME Université Paris Dauphine Carole A. GOBLE University of Manchester Pinar KARAGOZ Middle East Technical University 8