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BugDoc: A System for Debugging Computational Pipelines
DE PAULA LOURENCO, Raoni; Freire, Juliana; Shasha, Dennis
2020In SIGMOD 2020 - Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data
Peer reviewed
 

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Keywords :
provenance; workflow debugging; Error prones; Large scale simulations; New approaches; Root cause; Scientific experiments; Software; Information Systems
Abstract :
[en] 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 recently proposed a new approach that makes provenance to automatically and iteratively infer root causes and derive succinct explanations of failures; such an approach was implemented in our prototype, BugDoc. In this demonstration, we will illustrate BugDoc's capabilities to debug pipelines using few configuration instances.
Disciplines :
Computer science
Author, co-author :
DE PAULA LOURENCO, Raoni  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SerVal ; NYU - New York University [US-NY]
Freire, Juliana;  New York University, New York, United States
Shasha, Dennis;  New York University, New York, United States
External co-authors :
yes
Language :
English
Title :
BugDoc: A System for Debugging Computational Pipelines
Publication date :
14 June 2020
Event name :
Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data
Event place :
Portland, Usa
Event date :
14-06-2020 => 19-06-2020
Audience :
International
Main work title :
SIGMOD 2020 - Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data
Publisher :
Association for Computing Machinery
ISBN/EAN :
978-1-4503-6735-6
Peer reviewed :
Peer reviewed
Funders :
ACM SIGMOD
Funding text :
Acknowledgments. This work has been supported in part by NSF grants MCB-1158273, IOS-1339362, and MCB-1412232, CNPq (Brazil) grant 209623/2014-4, the DARPA D3M program, and NYU WIRELESS. Any opinions, findings, and conclusions or recommendations expressed in this material are
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