Data Quality-Reference-Cited by-同舟云学术

Data Quality

Published:2018-02-22 Issue:4 Volume:46 Page:35-43
ISSN:0163-5808
Container-title:ACM SIGMOD Record
language:en
Short-container-title:SIGMOD Rec.

Author:

Sadiq Shazia¹,Dasu Tamraparni²,Dong Xin Luna³,Freire Juliana⁴,Ilyas Ihab F.⁵,Link Sebastian⁶,Miller Miller J.⁷,Naumann Felix⁸,Zhou Xiaofang¹,Srivastava Divesh²

Affiliation:

1. The University of Queensland, Queensland, Australia

2. AT&T Labs-Research, Bedminster, NJ, USA

3. Amazon, Seattle, WA, USA

4. New York University, NYC, NY, USA

5. University of Waterloo, Waterloo, Canada

6. The University of Auckland, Auckland, New Zealand

7. University of Toronto, Toronto, Canada

8. Hasso Plattner Institute, University of Potsdam, Potsdam, Germany

Abstract

We outline a call to action for promoting empiricism in data quality research. The action points result from an analysis of the landscape of data quality research. The landscape exhibits two dimensions of empiricism in data quality research relating to type of metrics and scope of method. Our study indicates the presence of a data continuum ranging from real to synthetic data, which has implications for how data quality methods are evaluated. The dimensions of empiricism and their inter-relationships provide a means of positioning data quality research, and help expose limitations, gaps and opportunities.

Publisher

Association for Computing Machinery (ACM)

Subject

Information Systems,Software

Link

https://dl.acm.org/doi/pdf/10.1145/3186549.3186559

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