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An Approach of data-driven framework alignment to knowledge base

Published: 02 May 2018 Publication History

Abstract

When we talk about quality, we cannot do without mentioning the cost of quality and non-quality, the cost increases if the quality also increases; to maintain quality in small data is easier than huge data like big data or knowledge base.
Companies tend to use the knowledge base to perfect and facilitate their work, thus satisfying the end customer, however the non-quality of these bases will penalize the company, so it is necessary to improve the quality, the question is when and why to improve quality, our proposal is based on the cost and impact of this improvement, if the impact is greater than the cost then it is recommended to improve completeness in our case study.

References

[1]
J. Maqboul, B. Bounabat. 2017. Towards a Completeness Prediction Based on the Complexity and Impact. In Proceeding of international Conference -- on Information Technology and Communication Systems (ITCS'17), Khouribga Morocco, 5 pages.
[2]
M. Belhiah, B. Bounabat and S. Achchab. 2015. The impact of data accuracy on user-perceived business service's quality. In Proceeding of the 10th Iberian Conference on Information Systems and Technologies (CISTI'15), Cáceres, Spain, 4 pages.
[3]
M. Belhiah, M. S. Benqatla, B. Bounabat and S. Achchab. Towards a context-aware framework for assessing and optimizing data quality projects
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http://searchcio.techtarget.com/definition/business-process.
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http://www.what-is-bpm.com/get_started/bpm_methodology.html.
[6]
https://en.wikipedia.org/wiki/Business_process_management
[7]
Edwards W. Deming, Quality, Productivity and Competitive Position, Cambridge, 1982, pages 229.
[8]
M. Juran and Juran, Leadership For Quality, pages 15.
[9]
http://www.edureka.co/blog/introduction-to-project-quality-management
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http://iaidq.org/main/glossary.shtml#I.
[11]
Y.Richard. Wang and Diane and M. Strong, Beyond Accuracy: What Data Quality Means to Data Consumers, in Journal of Management Information Systems Vol. 12, No. 4 (Spring, 1996), pages 5--33.
[12]
L. L. Pipino and Y. W. Lee and R. Y. Wang, Data quality assessment, in Communications of the ACM. 2002. vol. 45, no. 4, pages 211--218.
[13]
Ackoff, R. L., "From Data to Wisdom", Journal of Applies Systems Analysis, Volume 16, 1989 p 3--9.
[14]
Ackoff, R. L. Ackoff's Best. New York: John Wiley & Sons. 1999. Pages 170---172.
[15]
https://www.nibusinessinfo.co.uk/content/what-knowledge-business
[16]
https://en.wikipedia.org/wiki/Knowledge_base
[17]
https://www.techopedia.com/definition/2511/knowledge-base-klog

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LOPAL '18: Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications
May 2018
357 pages
ISBN:9781450353045
DOI:10.1145/3230905
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: 02 May 2018

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Author Tags

  1. Business process
  2. Knowledge
  3. completeness
  4. complexity
  5. data quality
  6. framework
  7. impact
  8. java EE
  9. knowledge Base
  10. prediction

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LOPAL '18
LOPAL '18: Theory and Applications
May 2 - 5, 2018
Rabat, Morocco

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LOPAL '18 Paper Acceptance Rate 61 of 141 submissions, 43%;
Overall Acceptance Rate 61 of 141 submissions, 43%

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