Agrawal, R. and Srikant, R. Fast algorithms for mining association rules in large databases. In Proceedings of the Intern. Conf. on Very Large Databases (1994), Morgan Kaufmann, 487--499.

Google Scholar

[4]

Angwin, J. et al. Machine bias. ProPublica (2016).

Google Scholar

[5]

Asudeh, A., Jin, Z., and Jagadish, H.V. Assessing and remedying coverage for a given dataset. IEEE 35^th Intern. Conf. on Data Engineering (2019), 554--565.

Crossref

Google Scholar

[6]

Baralis, E., Paraboschi, S., and Teniente, E. Materialized views selection in a multidimensional database. In Proceedings of the 23^rd Intern. Conf. on Very Large Databases (1997), Morgan Kaufmann, 156--165.

Digital Library

Google Scholar

[7]

Broussard, M. When algorithms give real students imaginary grades. The New York Times (2020); https://www.nytimes.com/2020/09/08/opinion/international-baccalaureate-algorithm-grades.html

Google Scholar

[8]

Cortez, P. and Silva, A.M.G. Using data mining to predict secondary school student performance. In Proceedings of the 5^th Annual Future Business Tech. Conf. (2008).

Google Scholar

[9]

Deshpande, A., Garofalakis, M.N., and Rastogi, R. Independence is good: Dependency-based histogram synopses for high-dimensional data. ACM SIGMOD (2001), 199--210.

Digital Library

Google Scholar

[10]

Guo, Y., Binnig, C., and Kraska, T. What you see is not what you get! Detecting Simpson's Paradoxes during data exploration. In Proceedings of the 2^nd Workshop on Human in the Loop Analytics (2017), 2:1--2:5.

Digital Library

Google Scholar

[11]

Jo, S. et al. Verifying text summaries of relational data sets. ACM SIGMOD (2019), 299--316.

Google Scholar

[12]

Li, J., Moskovitch, Y., and Jagadish, H.V. Denouncer: Detection of unfairness in classifiers. In Proceedings of the VLDB Endowment 14, 12 (2021), 2719--2722^

Digital Library

Google Scholar

[13]

Lin, Y. et al. On detecting cherry-picked generalizations. In Proceedings of the VLDB Endowment 15, 1 (2021), 59--71.

Digital Library

Google Scholar

[14]

Moskovitch, Y. and Jagadish, H.V. COUNTATA: Dataset labeling using pattern counts. In Proceedings of the VLDB Endowment 13, 12 (2020).

Digital Library

Google Scholar

[15]

Moskovitch, Y. and Jagadish, H.V. Patterns count-based labels for datasets. IEEE 35^th Intern. Conf. on Data Engineering (2019), 1961--1966.

Google Scholar

[16]

Müller, M., Moerkotte, G., and Kolb, O. Improved selectivity estimation by combining knowledge from sampling and synopses. In Proceedings of the VLDB Endowment 11, 9 (2018), 1016--1028.

Digital Library

Google Scholar

[17]

Rymon, R. Search through systematic set enumeration. Intern. Conf. on Principles of Knowledge Representation and Reasoning (1992), Morgan Kaufmann, 539--550.

Google Scholar

[18]

Salimi, B., Gehrke, J., and Suciu, D. Bias in OLAP queries: Detection, explanation, and removal. ACM SIGMOD (2018), 1021--1035.

Digital Library

Google Scholar

[19]

Sissons, B. What to know about ADHD misdiagnosis. Medical News Today (2019); https://www.medicalnewstoday.com/articles/325595#age-related-factors.

Google Scholar

[20]

Stoyanovich, J. and Howe, B. Nutritional labels for data and models. IEEE Data Engineering Bulletin 42, 3 (2019), 13--23.

Google Scholar

[21]

Verma, S. and Rubin, J. Fairness definitions explained. IEEE/ACM FairWare (2018), 1--7.

Digital Library

Google Scholar

[22]

Yang, Z. et al. Deep unsupervised cardinality estimation. In Proceedings of the VLDB Endowment 13, 3 (2019), 279--292.

Digital Library

Google Scholar

Cited By

View all

Shahbazi NAsudeh A(2024)Reliability evaluation of individual predictions: a data-centric approachThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-024-00857-w33:4(1203-1230)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1007/s00778-024-00857-w

Index Terms

Reliability at multiple stages in a data analysis pipeline

Recommendations

An Efficient Method for Improving Reliability of a Pipeline FFT

A technique is described whereby the reliability of a pipeline FFT structure can be significantly increased with a relatively modest increase in complexity/cost. The proposed method is shown to give a substantial reliability/cost improvement relative to ...
MPI Stages: Checkpointing MPI State for Bulk Synchronous Applications
EuroMPI '18: Proceedings of the 25th European MPI Users' Group Meeting

When an MPI program experiences a failure, the most common recovery approach is to restart all processes from a previous checkpoint and to re-queue the entire job. A disadvantage of this method is that, although the failure occurred within the main ...
Reliability analysis on competitive failure processes under fuzzy degradation data

Reliability is the ability of a system to perform its required functions under stated conditions for a specified period of time. Reliability analysis is an important tool to evaluate the performance of a system and make maintenance decision. In ...

Comments

Information & Contributors

Information

Published In

Communications of the ACM Volume 65, Issue 11

November 2022

130 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/3569027

Editor:
James Larus
Association for Computing Machinery, New York, NY

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 October 2022

Published in CACM Volume 65, Issue 11

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article
Popular
Refereed

Funding Sources

NSF (National Science Foundation)

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
910
Total Downloads

Downloads (Last 12 months)510
Downloads (Last 6 weeks)59

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Shahbazi NAsudeh A(2024)Reliability evaluation of individual predictions: a data-centric approachThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-024-00857-w33:4(1203-1230)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1007/s00778-024-00857-w

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Digital Edition

View this article in digital edition.

Digital Edition

Magazine Site

View this article on the magazine site (external)

Magazine Site

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Abstract

References

Cited By

Index Terms

Recommendations

An Efficient Method for Improving Reliability of a Pipeline FFT

MPI Stages: Checkpointing MPI State for Bulk Synchronous Applications

Reliability analysis on competitive failure processes under fuzzy degradation data

Comments

Information

Published In

Publisher

Publication History

Permissions

Check for updates

Qualifiers

Funding Sources

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

PDF

eReader

Digital Edition

Magazine Site

Login options

Full Access

Share

Share this Publication link

Share on social media

Affiliations