@inproceedings{ef73abb3479e484cbd3aa38241d3edad,
title = "Improving Data Science Projects by Enriching Analytical Models with Domain Knowledge",
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.",
keywords = "analytic model, interoperability, knowledge, smart manufacturing, traceability",
author = "Heng Zhang and Utpal Roy and Jeffrey Saltz",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Big Data, Big Data 2018 ; Conference date: 10-12-2018 Through 13-12-2018",
year = "2019",
month = jan,
day = "22",
doi = "10.1109/BigData.2018.8622364",
language = "English (US)",
series = "Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2828--2837",
editor = "Yang Song and Bing Liu and Kisung Lee and Naoki Abe and Calton Pu and Mu Qiao and Nesreen Ahmed and Donald Kossmann and Jeffrey Saltz and Jiliang Tang and Jingrui He and Huan Liu and Xiaohua Hu",
booktitle = "Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018",
}