Scipediacontent: Scipediacontent moved page Draft Content 213112238 to Shasha et al 2020b

2021-02-03T17:01:43Z

Scipediacontent moved page Draft Content 213112238 to Shasha et al 2020b

Scipediacontent: Created page with " == Abstract == Data analysis for scientific experiments and enterprises, large-scale simulations, and machine learning tasks all entail the use of complex computational pipe..."

2021-02-03T17:01:40Z

Created page with " == Abstract == Data analysis for scientific experiments and enterprises, large-scale simulations, and machine learning tasks all entail the use of complex computational pipe..."

New page

== 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.

Comment: To appear in SIGMOD 2020. arXiv admin note: text overlap with arXiv:2002.04640

== Original document ==

The different versions of the original document can be found in:

* [http://arxiv.org/abs/2004.06530 http://arxiv.org/abs/2004.06530]

* [http://arxiv.org/pdf/2004.06530 http://arxiv.org/pdf/2004.06530]

* [https://dblp.uni-trier.de/db/conf/sigmod/sigmod2020.html#LourencoFS20 https://dblp.uni-trier.de/db/conf/sigmod/sigmod2020.html#LourencoFS20],
: [https://arxiv.org/abs/2004.06530 https://arxiv.org/abs/2004.06530],
: [https://export.arxiv.org/pdf/2004.06530 https://export.arxiv.org/pdf/2004.06530],
: [https://nyuscholars.nyu.edu/en/publications/bugdoc-algorithms-to-debug-computational-processes https://nyuscholars.nyu.edu/en/publications/bugdoc-algorithms-to-debug-computational-processes],
: [https://aps.arxiv.org/pdf/2004.06530 https://aps.arxiv.org/pdf/2004.06530],
: [https://arxiv.org/pdf/2004.06530.pdf https://arxiv.org/pdf/2004.06530.pdf],
: [https://jp.arxiv.org/abs/2004.06530 https://jp.arxiv.org/abs/2004.06530],
: [https://au.arxiv.org/pdf/2004.06530 https://au.arxiv.org/pdf/2004.06530],
: [https://aps.arxiv.org/abs/2004.06530 https://aps.arxiv.org/abs/2004.06530],
: [https://fr.arxiv.org/abs/2004.06530 https://fr.arxiv.org/abs/2004.06530],
: [https://export.arxiv.org/abs/2004.06530 https://export.arxiv.org/abs/2004.06530],
: [https://au.arxiv.org/abs/2004.06530 https://au.arxiv.org/abs/2004.06530],
: [https://il.arxiv.org/pdf/2004.06530 https://il.arxiv.org/pdf/2004.06530],
: [https://jp.arxiv.org/pdf/2004.06530 https://jp.arxiv.org/pdf/2004.06530],
: [https://za.arxiv.org/pdf/2004.06530 https://za.arxiv.org/pdf/2004.06530],
: [https://it.arxiv.org/abs/2004.06530 https://it.arxiv.org/abs/2004.06530],
: [https://za.arxiv.org/abs/2004.06530 https://za.arxiv.org/abs/2004.06530],
: [https://il.arxiv.org/abs/2004.06530 https://il.arxiv.org/abs/2004.06530],
: [https://fr.arxiv.org/pdf/2004.06530 https://fr.arxiv.org/pdf/2004.06530],
: [https://academic.microsoft.com/#/detail/3017205679 https://academic.microsoft.com/#/detail/3017205679]

* [https://dl.acm.org/doi/pdf/10.1145/3318464.3389763 https://dl.acm.org/doi/pdf/10.1145/3318464.3389763],
: [http://dx.doi.org/10.1145/3318464.3389763 http://dx.doi.org/10.1145/3318464.3389763] under the license http://www.acm.org/publications/policies/copyright_policy#Background

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Shasha et al 2020b - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 213112238 to Shasha et al 2020b

Scipediacontent: Created page with " == Abstract == Data analysis for scientific experiments and enterprises, large-scale simulations, and machine learning tasks all entail the use of complex computational pipe..."