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Cleanix: a Parallel Big Data Cleaning System

Authors:

Jiacheng ZhangAuthors Info & Claims

ACM SIGMOD Record, Volume 44, Issue 4

Pages 35 - 40

https://doi.org/10.1145/2935694.2935702

Published: 09 May 2016 Publication History

Abstract

For big data, data quality problem is more serious. Big data cleaning system requires scalability and the abilityof handling mixed errors. Motivated by this, we develop Cleanix, a prototype system for cleaning relational Big Data. Cleanix takes data integrated from multiple data sources and cleans them on a shared-nothing machine cluster. The backend system is built on-top-of an extensible and flexible data-parallel substrate the Hyracks framework. Cleanix supports various data cleaning tasks such as abnormal value detection and correction, incomplete data filling, de-duplication, and conflict resolution. In this paper, we show the organization, data cleaning algorithms as well as the design of Cleanix.

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Cited By

Zhao SChen KXiong BGuo CDang Z(2024)Prediction of adsorption of metal cations by clay minerals using machine learningScience of The Total Environment10.1016/j.scitotenv.2024.171733924(171733)Online publication date: May-2024
https://doi.org/10.1016/j.scitotenv.2024.171733
Yu SChen THan LDemartini GSadiq S(2023)DataOps-4G: On Supporting Generalists in Data Quality DiscoveryIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.315160535:5(4668-4681)Online publication date: 1-May-2023
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Pointner AHarrer M(2023)A Rule Based Data Cleansing Pipeline for Automated Data Import in the Context of Social Clubs2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)10.1109/ICECCME57830.2023.10253136(1-6)Online publication date: 19-Jul-2023
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Information & Contributors

Information

Published In

cover image ACM SIGMOD Record

ACM SIGMOD Record Volume 44, Issue 4

December 2015

59 pages

ISSN:0163-5808

DOI:10.1145/2935694

Editors:
Yanlei Diao
University of Massachusetts Amherst
,
Pablo Barceló
Universidad de Chile
,
Vanessa Braganholo
Universidade Federal Fluminense
,
Marco Brambilla
Politecnico di Milano
,
Chee Yong Chan
National University of Singapore
,
Rada Chirkova
North Carolina State University
,
Anastasios Kementsietsidis
Google Research
,
Olga Papaemmanoui
Brandeis Univesity
,
Aditya Parameswaran
University of Illinois
,
Anish Das Sarma
Google Research
,
Alkis Simitsis
HP Labs
,
Nesime Tatbul
ETH Zurich
,
Marianne Winslett
University of Illinois
,
Jun Yang
Duke University

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Copyright © 2016 Authors.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 May 2016

Published in SIGMOD Volume 44, Issue 4

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Cited By

Zhao SChen KXiong BGuo CDang Z(2024)Prediction of adsorption of metal cations by clay minerals using machine learningScience of The Total Environment10.1016/j.scitotenv.2024.171733924(171733)Online publication date: May-2024
https://doi.org/10.1016/j.scitotenv.2024.171733
Yu SChen THan LDemartini GSadiq S(2023)DataOps-4G: On Supporting Generalists in Data Quality DiscoveryIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.315160535:5(4668-4681)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1109/TKDE.2022.3151605
Pointner AHarrer M(2023)A Rule Based Data Cleansing Pipeline for Automated Data Import in the Context of Social Clubs2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)10.1109/ICECCME57830.2023.10253136(1-6)Online publication date: 19-Jul-2023
https://doi.org/10.1109/ICECCME57830.2023.10253136
Singh VSharma KSur S(2023)A survey on preprocessing and classification techniques for acoustic sceneExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120520229:PAOnline publication date: 13-Jul-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.120520
Ciavotta MCutrona VDe Paoli FNikolov NPalmonari MRoman D(2022)Supporting Semantic Data Enrichment at ScaleTechnologies and Applications for Big Data Value10.1007/978-3-030-78307-5_2(19-39)Online publication date: 29-Apr-2022
https://doi.org/10.1007/978-3-030-78307-5_2
Hosseinzadeh MAzhir EAhmed OGhafour MAhmed SRahmani AVo B(2021)Data cleansing mechanisms and approaches for big data analytics: a systematic studyJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-021-03590-214:1(99-111)Online publication date: 17-Nov-2021
https://doi.org/10.1007/s12652-021-03590-2
Sun MShao CZhuge CWang PYang XWang S(2021)Uncovering travel and charging patterns of private electric vehicles with trajectory data: evidence and policy implicationsTransportation10.1007/s11116-021-10216-149:5(1409-1439)Online publication date: 29-Jul-2021
https://doi.org/10.1007/s11116-021-10216-1
Li PDai CWang W(2019)When Considering More Elements: Attribute Correlation in Unsupervised Data Cleaning under BlockingSymmetry10.3390/sym1104057511:4(575)Online publication date: 19-Apr-2019
https://doi.org/10.3390/sym11040575
Li PDai CWang W(2019)Application of Attribute Correlation in Unsupervised Data CleaningProceedings of the 5th International Conference on e-Society, e-Learning and e-Technologies10.1145/3312714.3312717(45-51)Online publication date: 10-Jan-2019
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Ridzuan FWan Zainon W(2019)A Review on Data Cleansing Methods for Big DataProcedia Computer Science10.1016/j.procs.2019.11.177161(731-738)Online publication date: 2019
https://doi.org/10.1016/j.procs.2019.11.177
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