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) |
|---|---|
| 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 |
|---|
Conference
| Conference | 2018 IEEE International Conference on Big Data, Big Data 2018 |
|---|---|
| Country | United States |
| City | Seattle |
| Period | 12/10/18 → 12/13/18 |
Fingerprint
Keywords
- analytic model
- interoperability
- knowledge
- smart manufacturing
- traceability
ASJC Scopus subject areas
- Computer Science Applications
- Information Systems
Cite this
Improving Data Science Projects by Enriching Analytical Models with Domain Knowledge. / Zhang, Heng; Roy, Utpal; Saltz, Jeffrey.
Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. ed. / 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. Institute of Electrical and Electronics Engineers Inc., 2019. p. 2828-2837 8622364 (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Improving Data Science Projects by Enriching Analytical Models with Domain Knowledge
AU - Zhang, Heng
AU - Roy, Utpal
AU - Saltz, Jeffrey
PY - 2019/1/22
Y1 - 2019/1/22
N2 - 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.
AB - 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.
KW - analytic model
KW - interoperability
KW - knowledge
KW - smart manufacturing
KW - traceability
UR - http://www.scopus.com/inward/record.url?scp=85062588148&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062588148&partnerID=8YFLogxK
U2 - 10.1109/BigData.2018.8622364
DO - 10.1109/BigData.2018.8622364
M3 - Conference contribution
AN - SCOPUS:85062588148
T3 - Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
SP - 2828
EP - 2837
BT - Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
A2 - Song, Yang
A2 - Liu, Bing
A2 - Lee, Kisung
A2 - Abe, Naoki
A2 - Pu, Calton
A2 - Qiao, Mu
A2 - Ahmed, Nesreen
A2 - Kossmann, Donald
A2 - Saltz, Jeffrey
A2 - Tang, Jiliang
A2 - He, Jingrui
A2 - Liu, Huan
A2 - Hu, Xiaohua
PB - Institute of Electrical and Electronics Engineers Inc.
ER -