From the desktop to the grid and cloud: conversion of KNIME workflows to WS-PGRADE
- Published
- Accepted
- Subject Areas
- Bioinformatics, Distributed and Parallel Computing
- Keywords
- WS-PGRADE, KNIME, conversion, fine-grained interoperability, workflow
- Copyright
- © 2017 de la Garza et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2017. From the desktop to the grid and cloud: conversion of KNIME workflows to WS-PGRADE. PeerJ Preprints 5:e2849v1 https://doi.org/10.7287/peerj.preprints.2849v1
Abstract
Computational analyses for research usually consist of a complicated orchestration of data flows, software libraries, visualization, selection of adequate parameters, etc. Structuring these complex activities into a collaboration of simple, reproducible and well defined tasks brings down complexity and increases reproducibility. This is the basic notion of workflows. Workflow engines allow users to create and execute workflows, each having unique features. In some cases, certain features offered by platforms are royalty-based, hindering use in the scientific community. We present our efforts to convert whole workflows created in the Konstanz Information Miner Analytics Platform to the Web Services Parallel Grid Runtime and Developer Environment. We see the former as a great workflow editor due to its considerable user base and user-friendly graphical interface. We deem the latter as a great backend engine able to interact with most major distributed computing interfaces. We introduce work that provides a platform-independent tool representation, thus assisting in the conversion of whole workflows. We also present the challenges inherent to workflow conversion across systems, as well as the ones posed by the conversion between the chosen workflow engines, along with our proposed solution to overcome these challenges. The combined features of these two platforms (i.e., intuitive workflow design on a desktop computer and execution of workflows on distributed high performance computing interfaces) greatly benefit researchers and minimize time spent in technical chores not directly related to their area of research.
Author Comment
This is a preprint submission to PeerJ Preprints - 8th International Workshop on Science Gateways – IWSG 2016