Ontology-based Semi-automatic Workflow Composition

Due to the growing complexity of scientific workflows, it is important to provide abstraction levels to aid scientists to compose these workflows. By doing this, we isolate scientists from infrastructure issues and let them focus on their domain of expertise when composing the workflow. Although using abstract workflows is a first step, there are many open issues, such as the ones related to semantics. Adding semantics to abstract workflows enables the explicit representation of which activities can be linked to each other, or which activities are similar to each other. Existing approaches address either the representation of abstract workflows or using domain ontologies to add semantics to workflow activities, but not both. In the latter case, these approaches focus only on adding semantics to executable workflows, instead of abstract ones. This makes it difficult to group executable workflows into a common abstract representation in the conceptual level. This article proposes coupling a workflow ontology, named SciFlow, to an abstract workflow representation named Experiment Line and implemented in the GExpLine tool. This is a step towards semantic mechanisms, helping scientists to identify equivalent activities or grouping executable activities into one abstract activity with the same semantics.

[1]  Marta Mattoso,et al.  Experiment Line: Software Reuse in Scientific Workflows , 2009, SSDBM.

[2]  Marta Mattoso,et al.  Towards supporting the life cycle of large scale scientific experiments , 2010, Int. J. Bus. Process. Integr. Manag..

[3]  Asunción Gómez-Pérez,et al.  Ontological Engineering: With Examples from the Areas of Knowledge Management, e-Commerce and the Semantic Web , 2004, Advanced Information and Knowledge Processing.

[4]  Carole A. Goble,et al.  The myGrid ontology: bioinformatics service discovery , 2007, Int. J. Bioinform. Res. Appl..

[5]  Dennis Gannon,et al.  Workflows for e-Science, Scientific Workflows for Grids , 2014 .

[6]  Carole A. Goble,et al.  Transparent access to multiple bioinformatics information sources , 2001, IBM Syst. J..

[7]  Bertram Ludäscher,et al.  Kepler: an extensible system for design and execution of scientific workflows , 2004, Proceedings. 16th International Conference on Scientific and Statistical Database Management, 2004..

[8]  Cláudio T. Silva,et al.  VisTrails: visualization meets data management , 2006, SIGMOD Conference.

[9]  Stefano Beco,et al.  OWL-WS: a workflow ontology for dynamic grid service composition , 2005, First International Conference on e-Science and Grid Computing (e-Science'05).

[10]  Carole A. Goble,et al.  Taverna: a tool for building and running workflows of services , 2006, Nucleic Acids Res..

[11]  Marta Mattoso,et al.  GExpLine: A Tool for Supporting Experiment Composition , 2010, IPAW.

[12]  Claes Wohlin,et al.  Using students as subjects - an empirical evaluation , 2008, ESEM '08.

[13]  Mario Cannataro,et al.  A Data Mining Ontology for Grid Programming , 2003 .

[14]  Bertram Ludäscher,et al.  A Calculus for Propagating Semantic Annotations Through Scientific Workflow Queries , 2006, EDBT Workshops.

[15]  Bertram Ludäscher,et al.  What Makes Scientific Workflows Scientific? , 2009, SSDBM.

[16]  Arie Shoshani The Scientific Data Management Center: Providing Technologies for Large Scale Scientific Exploration , 2009, SSDBM.

[17]  Irina Astrova,et al.  Storing OWL ontologies in SQL3 object-relational databases , 2008 .

[18]  Giancarlo Guizzardi Ontological Foundations for Conceptual Part-Whole Relations: The Case of Collectives and Their Parts , 2011, CAiSE.

[19]  Cláudio T. Silva,et al.  Provenance for Computational Tasks: A Survey , 2008, Computing in Science & Engineering.

[20]  Geoffrey C. Fox,et al.  Examining the Challenges of Scientific Workflows , 2007, Computer.

[21]  Bertram Ludäscher,et al.  Compiling abstract scientific workflows into Web service workflows , 2003, 15th International Conference on Scientific and Statistical Database Management, 2003..