Understanding the utility and fitness of Workflow Provenance for Experiment Reporting

Pınar Alper,

Supervisor: Carole A. Goble

1

2

LocalData

LocalTool

Results

Data

Research Reporting

ResultsResultsResultsResultsResults

Results

Tool

Analysis

Analysis

Analysis

ResultsResultsResultsResultsResults

ResultsResultsResultsResultsResults

Data

select

recollect

share

package

publish

Build a citation string

Package results by origin

Document important run parameteresC. Tenopir, S. Allard, et al. Data sharing by scientists: Practices and perceptions. PLoS ONE,

6(6):e21101, 06 2011.

3

Provenance we have• WF description • Execution provenance

Pros

pecti

ve

Retr

ospe

ctive

Generic information:Data artefacts, consumption/production relationsExecution times/status

4

Provenance that is reported

– Origin – Methodological context – Scientific Context

Scientific Data Provenance

5

Motifs

D Garijo, P Alper, K Belhajjame, O Corcho, Y Gil, C Goble, Common motifs in scientific workflows: An empirical analysis, Future Generation Computer Systems. ISSN 0167-739X.

Minority (~30%) Data-creation

Majority (~70%)Data-preparation (value-copying)

Workflows as implementation artefacts:

• 240 Workflows, 4 Systems 10 domains

• A domain independent characterization of activities • ~90% characterizable

http://purl.org/net/wf-motifs#

Research Framework

WF Summaries

Labeling WF

II III

WF Motifs

I

Minimal additional design-time information

High-level categorization, as Semantic Annotations

Based on empirical evidence

Process Model for labeling

Motifs inform when to collect when to propagate labels

Novelty: Dynamic, domain specific

Novelty: Partial transparency

Graph Re-write primitives

Configurable filters

More informed abstraction wMotifs

Novelty: Declarative abstraction and contextual grouping

6

Grey-box

Groundtruth –user behavior

P Alper, K Belhajjame, C Goble, P Karagoz, Small Is Beautiful: Summarizing Scientific Workflows Using Semantic Annotations, IEEE Big Data, July 2013.

P Alper, C Goble, and K Belhajjame. 2013. On assisting scientific data curation in collection-based dataflows using labels. In Proceedings of the 8th Workshop on Workflows in Support of Large-Scale Science (WORKS '13). ACM, New York, NY, USA, 7-16. DOI=10.1145/2534248.2534249

7

How do I use TavernaWorkbenchscufl2-api

make a wfInquire about details

Scufl2-wfdescwe operate on abstract wf

description

IssuesAdditional characteristics (port depths, itertion config)

Annotation support @UI w key-value pairs

List handling representation

Resource uniqueness

8

Thank you!

Carole A. GOBLEUniversity of Manchester

Khalid BELHAJJAMEUniversité Paris Dauphine

Pinar KARAGOZMiddle East Technical University

Pinar ALPERUniversity of Manchester