Data quality between promises and results

Paolo Papotti

doi:10.1109/ICDEW.2016.7495647

Data quality between promises and results

Paolo Papotti

Computing and Augmented Intelligence, School of (IAFSE-SCAI)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

Improving the quality of data is a crucial task for business, health, and scientific data. Several data cleaning algorithms have been translated into tools to identify and repair data errors such as outlying values, duplicate records, typos, missing values, and violations of rules in general [1], [2], [3], [4].

Original language	English (US)
Title of host publication	2016 IEEE 32nd International Conference on Data Engineering Workshops, ICDEW 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	200
Number of pages	1
ISBN (Electronic)	9781509021086
DOIs	https://doi.org/10.1109/ICDEW.2016.7495647
State	Published - Jun 20 2016
Event	32nd IEEE International Conference on Data Engineering Workshops, ICDEW 2016 - Helsinki, Finland Duration: May 16 2016 → May 20 2016

Other

Other	32nd IEEE International Conference on Data Engineering Workshops, ICDEW 2016
Country/Territory	Finland
City	Helsinki
Period	5/16/16 → 5/20/16

ASJC Scopus subject areas

Artificial Intelligence
Information Systems
Computational Theory and Mathematics
Computer Networks and Communications
Information Systems and Management
Computer Graphics and Computer-Aided Design

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10.1109/ICDEW.2016.7495647

Cite this

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title = "Data quality between promises and results",

abstract = "Improving the quality of data is a crucial task for business, health, and scientific data. Several data cleaning algorithms have been translated into tools to identify and repair data errors such as outlying values, duplicate records, typos, missing values, and violations of rules in general [1], [2], [3], [4].",

author = "Paolo Papotti",

year = "2016",

month = jun,

day = "20",

doi = "10.1109/ICDEW.2016.7495647",

language = "English (US)",

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booktitle = "2016 IEEE 32nd International Conference on Data Engineering Workshops, ICDEW 2016",

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note = "32nd IEEE International Conference on Data Engineering Workshops, ICDEW 2016 ; Conference date: 16-05-2016 Through 20-05-2016",

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AB - Improving the quality of data is a crucial task for business, health, and scientific data. Several data cleaning algorithms have been translated into tools to identify and repair data errors such as outlying values, duplicate records, typos, missing values, and violations of rules in general [1], [2], [3], [4].

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M3 - Conference contribution

AN - SCOPUS:84982806727

SP - 200

BT - 2016 IEEE 32nd International Conference on Data Engineering Workshops, ICDEW 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 32nd IEEE International Conference on Data Engineering Workshops, ICDEW 2016

Y2 - 16 May 2016 through 20 May 2016

ER -

Data quality between promises and results

Abstract

Other

ASJC Scopus subject areas

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