Abstract

This work considers a scientific experiment as a computational workflow. Provenance models store details of each workflow execution, including produced data, computational tools parameters and their versions, among others. This way, scientists can review details of a particular workflow execution, compare information generated among different executions and plan new ones efficiently. In the bioinformatics domain, particularly in the presence of large volumes of data, persistency of those data generated during the workflow execution is still a research challenge. In this article, we consider a study on provenance data storage for bioinformatics in a document-oriented NoSQL database system. We present data modeling issues and discuss an actual implementation into MongoDB.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.