Abstract
Domain knowledge is very important to support the development of analytic models. However, in today's data science projects, domain knowledge is typically documented, but not captured and integrated with the actual analytic model. This raises problems in interoperability and traceability of the relevant domain knowledge that is used to develop an analytic model. To address this challenge, this paper proposes a Knowledge Enriched Analytic Model (KEAM) to enrich analytic models with domain knowledge. To explore the proposed methodology and its benefits, a case study explores the utilization of KEAM to support the development of a Bayesian Network model within the smart manufacturing domain. The case study shows that the efficiency in developing an analytic model is improved by using the proposed KEAM.
Original language | English (US) |
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Title of host publication | Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018 |
Editors | Yang Song, Bing Liu, Kisung Lee, Naoki Abe, Calton Pu, Mu Qiao, Nesreen Ahmed, Donald Kossmann, Jeffrey Saltz, Jiliang Tang, Jingrui He, Huan Liu, Xiaohua Hu |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 2828-2837 |
Number of pages | 10 |
ISBN (Electronic) | 9781538650356 |
DOIs | |
State | Published - Jan 22 2019 |
Event | 2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States Duration: Dec 10 2018 → Dec 13 2018 |
Publication series
Name | Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018 |
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Conference
Conference | 2018 IEEE International Conference on Big Data, Big Data 2018 |
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Country | United States |
City | Seattle |
Period | 12/10/18 → 12/13/18 |
Keywords
- analytic model
- interoperability
- knowledge
- smart manufacturing
- traceability
ASJC Scopus subject areas
- Computer Science Applications
- Information Systems