Zhang, H.
, Roy, U.
and Lee, Y.
(2019),
Enriching Analytics Models with Domain Knowledge for Smart Manufacturing Data Analysis, International Journal of Production Research, [online], https://doi.org/10.1080/00207543.2019.1680895, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=928834
(Accessed April 25, 2024)
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
Enriching Analytics Models with Domain Knowledge for Smart Manufacturing Data Analysis
Published
Author(s)
Heng Zhang, Utpal Roy,
Abstract
Today, data analytics plays an important role in Smart Manufacturing decision making. Domain knowledge is very important to support the development of analytics models. However, in today's data analytics projects, domain knowledge is only documented, but not properly captured and integrated with analytics models. This raises problems in interoperability and traceability of the relevant domain knowledge that is used to develop analytics models. To address these problems, this paper proposes a methodology to enrich analytics models with domain knowledge. To illustrate the proposed methodology, a case study is introduced to demonstrate the utilization of the enriched analytics model to support the development of a Bayesian Network model. The case study shows that the utilization of an enriched analytics model improves the efficiency in developing the Bayesian Network model.Citation
International Journal of Production Research
Volume
0
Pub Type
Journals
Download Paper
Keywords
Smart Manufacturing, Data Analytics, Domain Knowledge, Interoperability, Traceability, Bayesian Network
Citation
Created October 30, 2019, Updated October 12, 2021