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Data Standards Challenges for Interoperable and Quality Data

Authors:

Hongwei Zhu,

Yang W. Lee,

Arnon S. RosenthalAuthors Info & Claims

Journal of Data and Information Quality (JDIQ), Volume 7, Issue 1-2

Article No.: 4, Pages 1 - 3

https://doi.org/10.1145/2903723

Published: 16 May 2016 Publication History

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References

[1]

Philip A. Bernstein and Laura M. Haas. 2008. Information integration in the enterprise. Commun. ACM 51, 9, 72--79.

Digital Library

Google Scholar

[2]

Carl F. Cargill and Sherrie Bolin. 2007. Standardization: A failing paradigm. In Standards and Public Policy, S. Greenstein and V. Stango (Eds.). Cambridge University Press, Cambridge, 296--328.

Google Scholar

[3]

Dazhi Chong and Hongwei Zhu. 2012. Firm clustering based on financial statements. In Proceedings of the 22nd Workshop on Information Technology and Information Systems (WITS’12).

Google Scholar

[4]

Erwin Folmer. 2012. Quality of Semantic Standards. Ph.D. Dissertation. University of Twente, The Netherlands.

Google Scholar

[5]

Yang Lee. 2003. Crafting rules: Context-reflective data quality problem solving. J. Manag. Informat. Syst. 20, 3, 93--119

Digital Library

Google Scholar

[6]

Yang Lee, Leo Pipino, James Funk, and Richard Wang. 2006. Journey to Data Quality. MIT Press, Cambridge, MA.

Crossref

Google Scholar

[7]

Xitong Li and Hongwei Zhu. 2015. Data standardization and quality degradation of human-readable data. In Proceedings of the 36th International Conference on Information Systems.

Google Scholar

[8]

Kalle Lyytinen and John L. King. 2006. Standard making: A critical research frontier for information systems research. Manag. Informat. Syst. Quart. 30, 405--411.

Digital Library

Google Scholar

[9]

Stuart E. Madnick, Richard Y. Wang, Yang W. Lee, and Hongwei Zhu. 2009. Overview and framework for data and information quality research. ACM J. Data Informat. Qual. 1, 1, Article #2.

Digital Library

Google Scholar

[10]

M. Lynne Markus, Charles W. Steinfield, Rolf T. Wigand, and Gabe Minton. 2006. Industry-wide information systems standardization as collective action: The case of the U.S. residential mortgage industry. Manag. Informat. Syst. Quart. 30, 439--465.

Digital Library

Google Scholar

[11]

Arnon Rosenthal, Len Seligman, and Scott Renner. 2004. From semantic integration to semantics management: Case studies and a way forward. ACM SIGMOD Rec. 33, 4, 44--50.

Digital Library

Google Scholar

[12]

Arnon Rosenthal, Len Seligman, M. David Allen, Adriane Chapman, and Hongwei Zhu. 2014. Fit for purpose: Engineering principles for selecting an appropriate type of data exchange standard. Informat. Syst. E-Bus. Manag. 12, 4, 495--515.

Digital Library

Google Scholar

[13]

Besiki Stvilia, Les Gasser, Michael B. Twidale, and Linda C. Smith. 2007. A framework for information quality assessment. J. Am. Soc. Informat. Sci. Technol. 58, 12, 1720--1733.

Digital Library

Google Scholar

[14]

Richard Y. Wang and Diane M. Strong. 1996. Beyond accuracy: What data quality means to data consumers. J. Manag. Informat. Syst. 12, 4, 5--33.

Digital Library

Google Scholar

[15]

Richard Y. Wang. 1998. A product perspective on total data quality management. Commun. ACM 41, 2, 58--63.

Digital Library

Google Scholar

[16]

Michael D. Wybo and Dale L. Goodhue. 1995. Using interdependence as a predictor of data standards: Theoretical and measurement issues. Informat. Manag. 29, 6, 377--329.

Digital Library

Google Scholar

[17]

Kexin Zhao and Mu Xia. 2014. Forming interoperability through interorganizational systems standards. J. Manag. Informat. Syst. 30, 4, 269--298.

Crossref

Google Scholar

[18]

Hongwei Zhu and Harris Wu. 2014. Assessing the quality of large-scale data standards: A case of XBRL GAAP taxonomy. Decision Support Syst. 59, 351--360.

Digital Library

Google Scholar

Cited By

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Neto NMadnick SPaula ABorges N(2021)Developing a Global Data Breach Database and the Challenges EncounteredJournal of Data and Information Quality10.1145/343987313:1(1-33)Online publication date: 15-Jan-2021
https://dl.acm.org/doi/10.1145/3439873
Kashyap RRahamatkar S(2019)Medical Image SegmentationEarly Detection of Neurological Disorders Using Machine Learning Systems10.4018/978-1-5225-8567-1.ch015(292-321)Online publication date: 2019
https://doi.org/10.4018/978-1-5225-8567-1.ch015
Kashyap R(2019)Computational Healthcare System With Image AnalysisComputational Models for Biomedical Reasoning and Problem Solving10.4018/978-1-5225-7467-5.ch004(89-127)Online publication date: 2019
https://doi.org/10.4018/978-1-5225-7467-5.ch004
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Index Terms

Data Standards Challenges for Interoperable and Quality Data
1. Information systems
  1. Data management systems
    1. Information integration
      1. Data exchange
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory
      1. Data modeling

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Published In

Journal of Data and Information Quality Volume 7, Issue 1-2

Challenge Papers, Regular Papers and Experience Paper

June 2016

111 pages

ISSN:1936-1955

EISSN:1936-1963

DOI:10.1145/2888577

Editor:
Louiqa Raschid
University of Maryland, College Park, USA

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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 May 2016

Accepted: 01 March 2016

Revised: 01 January 2016

Received: 01 August 2015

Published in JDIQ Volume 7, Issue 1-2

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Neto NMadnick SPaula ABorges N(2021)Developing a Global Data Breach Database and the Challenges EncounteredJournal of Data and Information Quality10.1145/343987313:1(1-33)Online publication date: 15-Jan-2021
https://dl.acm.org/doi/10.1145/3439873
Kashyap RRahamatkar S(2019)Medical Image SegmentationEarly Detection of Neurological Disorders Using Machine Learning Systems10.4018/978-1-5225-8567-1.ch015(292-321)Online publication date: 2019
https://doi.org/10.4018/978-1-5225-8567-1.ch015
Kashyap R(2019)Computational Healthcare System With Image AnalysisComputational Models for Biomedical Reasoning and Problem Solving10.4018/978-1-5225-7467-5.ch004(89-127)Online publication date: 2019
https://doi.org/10.4018/978-1-5225-7467-5.ch004
Pertheban SNarayana Samy GShanmugam B(2019)A Systematic Literature Review: Information Accuracy Practices in TourismJournal of Quality Assurance in Hospitality & Tourism10.1080/1528008X.2018.1563016(1-30)Online publication date: 6-Jan-2019
https://doi.org/10.1080/1528008X.2018.1563016

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Data standards quality measured for achieving enterprise interoperability: the case of the SETU standard for flexible staffing

Assessing the quality of large-scale data standards: A case of XBRL GAAP Taxonomy

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Data standards quality measured for achieving enterprise interoperability: the case of the SETU standard for flexible staffing

Assessing the quality of large-scale data standards: A case of XBRL GAAP Taxonomy

The Case for Formal Methods in Standards

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