Uncertainty Management in Bioinformatics Workflow Provenance Databases

• Gustavo Tallarida Fluminense Federal University
• Kary Ocaña National Laboratory for Scientific Computing
• Aline Paes Fluminense Federal University
• Vanessa Braganholo Fluminense Federal University https://orcid.org/0000-0002-1184-8192
• Daniel de Oliveira Fluminense Federal University

DOI: https://doi.org/10.5753/sbbd.2016.24325

Abstract

Provenance databases play an essential role in scientific experiments. The models considered to represent such data assume that there is a certainty in all the provenance relations. However, several experiments are not deterministic, which makes their results to be associated with uncertainties. Analyze provenance data in the presence of such uncertainties is not trivial. In this paper, we address the management of non-deterministic provenance data by relying on an extractor component that stores both provenance data and its corresponding uncertainty values in a probabilistic database. Experiments show an acceptable overhead of 3% in the workflow runtime and 16% in the time spent to process a query.

Keywords: Provenance Databases, Uncertainty Management

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