Common Motifs in Scientific Workflows: An Empirical Analysis
-
Upload
dgarijo -
Category
Technology
-
view
707 -
download
1
description
Transcript of Common Motifs in Scientific Workflows: An Empirical Analysis
Date: 10/11/2012
Common Motifs in Scientific Workflows: An Empirical
Analysis
Daniel Garijo *, Pinar Alper ⱡ, Khalid Belhajjame ⱡ, Oscar Corcho *, Yolanda Gil Ŧ, Carole Goble ⱡ
* Universidad Politécnica de Madrid,ⱡ University of Manchester,
Ŧ USC Information Sciences Institute
IEEE eScience 2012. Chicago, USA
2
Overview
• Empirical analysis on 177 workflow templates from Taverna and Wings
• Catalog of recurring patterns: scientific workflow motifs.
• Data Oriented Motifs
• Workflow Oriented Motifs
•Understandability and reuse
IEEE eScience 2012. Chicago, USA
Catalog
http://sensefinancial.com/wp-content/uploads/2012/02/contribution.jpg
3
Background
• Workflows as software artifacts that capture the scientific method• Addition to paper publication• Reuse
• Existing repositories of workflows (myExperiment)• Sharing workflows• Exploring existing workflows.
• PROBLEMS to address:• Sometimes workflows are difficult to understand• Workflow descriptions depend on tools/files
• Decay of workflows• Identify good practices for workflow design
IEEE eScience 2012. Chicago, USA
http://www.myexperiment.org
4
Approach
•Reverse-engineer the set of current practices in workflowdevelopment through an analysis of empirical evidence
•Identify workflow abstractions that would facilitateunderstandability and therefore effective re-use
IEEE eScience 2012. Chicago, USA
5
Taverna and Wings
IEEE eScience 2012. Chicago, USA
http://www.taverna.org.uk/
http://www.wings-workflows.org/
6
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. Workflow-oriented motifs: How does the workflow perform its operations?
•E.g.:• Stateful steps• Stateless steps• Human interactions• etc.
IEEE eScience 2012. Chicago, USA
WHAT?
HOW?
7
Data Oriented MotifsData-Oriented Motifs
Data Retrieval
Data Preparation
Format Transformation
Input Augmentation and Output Splitting
Data Organisation
Data Analysis
Data Curation/Cleaning
Data Moving
Data Visualisation
IEEE eScience 2012. Chicago, USA
8
Data Oriented MotifsData-Oriented Motifs
Data Retrieval
Data Preparation
Format Transformation
Input Augmentation and Output Splitting
Data Organisation
Data Analysis
Data Curation/Cleaning
Data Moving
Data Visualisation
IEEE eScience 2012. Chicago, USA
9
Data Oriented MotifsData-Oriented Motifs
Data Retrieval
Data Preparation
Format Transformation
Input Augmentation and Output Splitting
Data Organisation
Data Analysis
Data Curation/Cleaning
Data Moving
Data Visualisation
IEEE eScience 2012. Chicago, USA
10
Data Oriented MotifsData-Oriented Motifs
Data Retrieval
Data Preparation
Format Transformation
Input Augmentation and Output Splitting
Data Organisation
Data Analysis
Data Curation/Cleaning
Data Moving
Data Visualisation
IEEE eScience 2012. Chicago, USA
11
Data Oriented MotifsData-Oriented Motifs
Data Retrieval
Data Preparation
Format Transformation
Input Augmentation and Output Splitting
Data Organisation
Data Analysis
Data Curation/Cleaning
Data Moving
Data Visualisation
IEEE eScience 2012. Chicago, USA
12
Data Oriented MotifsData-Oriented Motifs
Data Retrieval
Data Preparation
Format Transformation
Input Augmentation and Output Splitting
Data Organisation
Data Analysis
Data Curation/Cleaning
Data Moving
Data Visualisation
IEEE eScience 2012. Chicago, USA
13
Workflow Oriented MotifsWorkflow-Oriented Motifs
Intra-Workflow Motifs
Stateful (Asynchronous) Invocations
Stateless (Synchronous) Invocations
Internal Macros
Human Interactions
Inter-Workflow Motifs
Atomic Workflows
Composite Workflows
Workflow Overloading
IEEE eScience 2012. Chicago, USA
14
Workflow Oriented MotifsWorkflow-Oriented Motifs
Intra-Workflow Motifs
Stateful (Asynchronous) Invocations
Stateless (Synchronous) Invocations
Internal Macros
Human Interactions
Inter-Workflow Motifs
Atomic Workflows
Composite Workflows
Workflow Overloading
IEEE eScience 2012. Chicago, USA
15
Workflow Oriented MotifsWorkflow-Oriented Motifs
Intra-Workflow Motifs
Stateful (Asynchronous) Invocations
Stateless (Synchronous) Invocations
Internal Macros
Human Interactions
Inter-Workflow Motifs
Atomic Workflows
Composite Workflows
Workflow Overloading
IEEE eScience 2012. Chicago, USA
16
Workflow Oriented MotifsWorkflow-Oriented Motifs
Intra-Workflow Motifs
Stateful (Asynchronous) Invocations
Stateless (Synchronous) Invocations
Internal Macros
Human Interactions
Inter-Workflow Motifs
Atomic Workflows
Composite Workflows
Workflow Overloading
IEEE eScience 2012. Chicago, USA
17
Workflow Oriented MotifsWorkflow-Oriented Motifs
Intra-Workflow Motifs
Stateful (Asynchronous) Invocations
Stateless (Synchronous) Invocations
Internal Macros
Human Interactions
Inter-Workflow Motifs
Atomic Workflows
Composite Workflows
Workflow Overloading
IEEE eScience 2012. Chicago, USA
18
Experiment setup
IEEE eScience 2012. Chicago, USA
•177 Workflow templates
• 111 from Taverna, sample from myExperiment• 66 from Wings, available in public server (now as Linked Data)• Diverse domains
Drug D
iscove
ry
Astronomy
Biodiversi
ty
ChemInformati
cs
Genomics
GeoInformati
cs
IST600
TextAnaly
tics05
10152025303540
TavernaWings
19
Result Summary: Data Oriented Motifs
IEEE eScience 2012. Chicago, USA
•Over 60% of the motifs are data preparation motifs• Of the 4 subcategories, the most common across domains are output
splitting, input augmentation, and reformatting steps.
•Data retrieval common in domains where curated databases exist
•Data analysis is often the main functionality of the workflow
Data organisation
20
Result Summary: Workflow Oriented Motifs
IEEE eScience 2012. Chicago, USA
• Around 40% composite workflows and internal macros• Workflow reuse is present even in some atomic workflows
•Human interactions steps increasingly used in some domains
21
Differences and commonalities of the workflow systems
IEEE eScience 2012. Chicago, USA
•Data moving/retrieval, stateful interactions and human interaction steps are not present in Wings• Web services (Taverna) versus software components (Wings)• Wings has layered execution through Pegasus
•Data preparation steps are common in both systems
•Use of sub workflows is high
22
Discussion
IEEE eScience 2012. Chicago, USAhttp://www.sandensconsulting.com/images/DataObfuscation.jpg
Our observations:
• Obfuscation of scientific workflows• The abundance of data preparation
steps make the functionality of the workflow unclear.
• Decay of scientific workflows • Create an abstract description.
• Good practices for workflow design• Sub-workflows
• Workflow overloading
Method in paperWorkflow
•Empirical analysis of scientific workflows177 workflows • 2 different systems • A variety of heterogeneous domains
•Workflow motif catalog• Data oriented motifs• Workflow oriented motifs
•Future work: automatic abstractions on workflowsTemplate analysis Trace analysis (provenance) Include other workflow systems
23
Conclusions and future work
IEEE eScience 2012. Chicago, USA
24
Who are we?
• Pinar AlperSchool of Computer Science, University of Manchester
• Khalid BelhajjameSchool of Computer Science, University of Manchester
• Oscar CorchoOntology Engineering Group, UPM
• Yolanda GilInformation Sciences Institute, USC
• Carole GobleSchool of Computer Science, University of Manchester
EU Wf4Ever project (270129) funded under EU FP7 (ICT- 2009.4.1). (http://www.wf4ever-project.org)
IEEE eScience 2012. Chicago, USA
Date: 10/11/2012
Common Motifs in Scientific Workflows: An Empirical
Analysis
Daniel Garijo *, Pinar Alper ⱡ, Khalid Belhajjame ⱡ, Oscar Corcho *, Yolanda Gil Ŧ, Carole Goble ⱡ
* Universidad Politécnica de Madrid,ⱡ University of Manchester,
Ŧ USC Information Sciences Institute
IEEE eScience 2012. Chicago, USA