Why workflows break — Understanding and combating decay in Taverna workflows

Abstract

Workflows provide a popular means for preserving scientific methods by explicitly encoding their process. However, some of them are subject to a decay in their ability to be reexecuted or reproduce the same results over time, largely due to the volatility of the resources required for workflow executions. This paper provides an analysis of the root causes of workflow decay based on an empirical study of a collection of Taverna workflows from the myExperiment repository. Although our analysis was based on a specific type of workflow, the outcomes and methodology should be applicable to workflows from other systems, at least those whose executions also rely largely on accessing third-party resources. Based on our understanding about decay we recommend a minimal set of auxiliary resources to be preserved together with the workflows as an aggregation object and provide a software tool for end-users to create such aggregations and to assess their completeness.

Four level provenance support to achieve portable reproducibility of scientific workflows

“…Zhao et al [12] in their paper investigate the cause of the so called workflow decay, which means that year by year the ability and success of the re-execution of any workflow significantly reduces. They examined 92 Taverna workflows submitted in the period from 2007 to 2012 and found four major causes: 1.…”

Reproducibility Analysis of Scientific Workflows

“…Zhao et al [23] in their paper investigated the cause of the so called workflow decay. They examined 92 Taverna workflows submitted in the period between 2007 and 2012 and found four major causes: 1) Missing volatile third party resources 2) Missing example data 3) Missing execution environment (requirement of special local services) and 4) Insufficient descriptions about workflows.…”

“…Unfortunately experiences have showed that many workflows failed on occasion of a later re-execution. Zhao et al [23] [11] investigated the main purposes of the so-called workflow decay, which means that year by year the ability and success of the re-execution of any workflow significantly reduces. They found four main causes which have prevented the re-execution: 1. the missing environmental parameters, 2. missing third party resources; 3. missing descriptions about the workflows; 4. the missing samples of the experiments or the inputs and outputs of the workflows.…”

Classification of scientific workflows based on reproducibility analysis

“…Zhao et al [9] in their paper investigate the cause of the so called workflow decay, which means that year by year the ability and success of the re-execution of any workflow significantly reduces. They examined 92 Taverna workflows submitted in the period from 2007 to 2012 and found four major causes: 1.…”