Year: 2020
Type: conference-paper
Abstract: Data analysis for scientific experiments and enterprises, large-scale simulations, and machine learning tasks all entail the use of complex computational pipelines to reach quantitative and qualitative conclusions. If some of the activities in a pipeline produce erroneous outputs, the pipeline may fail to execute or produce incorrect results. Inferring the root cause(s) of such failures is challenging, usually requiring time and much human thought, while still being error-prone. We propose a new approach that makes use of iteration and provenance to automatically infer the root causes and derive succinct explanations of failures. Through a detailed experimental evaluation, we assess the cost, precision, and recall of our approach compared to the state of the art. Our experimental data and processing software is available for use, reproducibility, and enhancement.
Authors: Raoni Lourenço, Juliana Freire, Dennis Shasha
Institution: New York University
Language: English
Subfield: Software
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Awards: G1578773895, Prospecting for Resources: A Systems Integration of Local and Systemic Nutrient Signaling, G4087385277, G4446790230, G6549679978, NutriNet: A Network Inspired Approach to Improving Nutrient Use Efficiency (NUE) in Crop Plants, A Systems Approach to the NPK Nutriome and its Effect on Biomass
OpenAlex ID: W3017205679