The PBase Scientific Workflow Provenance Repository

Author:

Cuevas-Vicenttín Víctor,Kianmajd Parisa,Ludäscher Bertram,Missier Paolo,Chirigati Fernando,Wei Yaxing,Koop David,Dey Saumen

Abstract

Scientific workflows and their supporting systems are becoming increasingly popular for compute-intensive and data-intensive scientific experiments. The advantages scientific workflows offer include rapid and easy workflow design, software and data reuse, scalable execution, sharing and collaboration, and other advantages that altogether facilitate “reproducible science”. In this context, provenance – information about the origin, context, derivation, ownership, or history of some artifact – plays a key role, since scientists are interested in examining and auditing the results of scientific experiments. However, in order to perform such analyses on scientific results as part of extended research collaborations, an adequate environment and tools are required. Concretely, the need arises for a repository that will facilitate the sharing of scientific workflows and their associated execution traces in an interoperable manner, also enabling querying and visualization. Furthermore, such functionality should be supported while taking performance and scalability into account. With this purpose in mind, we introduce PBase: a scientific workflow provenance repository implementing the ProvONE proposed standard, which extends the emerging W3C PROV standard for provenance data with workflow specific concepts. PBase is built on the Neo4j graph database, thus offering capabilities such as declarative and efficient querying. Our experiences demonstrate the power gained by supporting various types of queries for provenance data. In addition, PBase is equipped with a user friendly interface tailored for the visualization of scientific workflow provenance data, making the specification of queries and the interpretation of their results easier and more effective.

Publisher

Edinburgh University Library

Cited by 14 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. S-ProvFlow. Storing and Exploring Lineage Data as a Service;Data Intelligence;2022

2. Towards integration of data-driven agronomic experiments with data provenance;Computers and Electronics in Agriculture;2019-06

3. Provenance Analytics for Workflow-Based Computational Experiments;ACM Computing Surveys;2019-05-31

4. Polyflow;Proceedings of the XV Brazilian Symposium on Information Systems;2019-05-20

5. The MASi repository service — Comprehensive, metadata-driven and multi-community research data management;Future Generation Computer Systems;2019-05

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