Abstract
Workflows have found adoption in scientific domains particularly due to their automation and provenance features. Using workflows scientists can repeat analyses with different input parameters to later use provenance to access and compare results based on respective parameters. An assumption that is often made is that by designing an analysis as a workflow, we get parameter-to-result traceability for free with workflow provenance. This assumption holds for cases of coarse-grained traceability, where an entire workflow is subjected to repetition and all workflow parameters contribute to all results. On the other hand for cases requiring finer grained traceability, where a workflow is configured with collections of parameters and analyses within a workflow
are repeated with combinations of parameters from collections, this assumption is not guaranteed to hold. In this paper we identify two dimensions that affect fine-grained traceability as: 1) the level of granularity supported by a workflow system in modelling parameters/data in workflows and in provenance, which we name as the level of support for Factorial Design, and 2) the practice of scientists in successfully encoding Factorial Design into workflows. Taverna is a workflow system that provides extensive features for factorial design, meanwhile it provides an uncontrolled approach to workflow design; meaning scientists may create workflows, which, when run, could break traceability in provenance. Using a real-world Taverna workflow we show how broken traceability manifests in provenance and how it can render provenance practically useless for accessing workflow outputs derived from particular input parameters. In order to prevent broken traceability from occurring, we describe a rule-based static analysis technique, which operates over workflow descriptions and anticipates patterns in provenance. Our rules exploit the well-defined execution behaviour in the Taverna system. In order to understand Factorial Design support in workflow systems in general, we provide a comparative survey. We conclude that other workflow systems also provide constructs for Factorial Design, and, similar to Taverna, they too are prone to broken traceability.
are repeated with combinations of parameters from collections, this assumption is not guaranteed to hold. In this paper we identify two dimensions that affect fine-grained traceability as: 1) the level of granularity supported by a workflow system in modelling parameters/data in workflows and in provenance, which we name as the level of support for Factorial Design, and 2) the practice of scientists in successfully encoding Factorial Design into workflows. Taverna is a workflow system that provides extensive features for factorial design, meanwhile it provides an uncontrolled approach to workflow design; meaning scientists may create workflows, which, when run, could break traceability in provenance. Using a real-world Taverna workflow we show how broken traceability manifests in provenance and how it can render provenance practically useless for accessing workflow outputs derived from particular input parameters. In order to prevent broken traceability from occurring, we describe a rule-based static analysis technique, which operates over workflow descriptions and anticipates patterns in provenance. Our rules exploit the well-defined execution behaviour in the Taverna system. In order to understand Factorial Design support in workflow systems in general, we provide a comparative survey. We conclude that other workflow systems also provide constructs for Factorial Design, and, similar to Taverna, they too are prone to broken traceability.
| Original language | English |
|---|---|
| Pages (from-to) | 310-329 |
| Number of pages | 20 |
| Journal | Future Generation Computer Systems |
| Volume | 75 |
| Early online date | 16 Jan 2017 |
| DOIs | |
| Publication status | Published - Oct 2017 |