@inproceedings{90f3e82eee2f4ff6a9f8abc83b35df28,
title = "Ishikawa, JESS, and Visual Analytics for Engineering",
abstract = "Domain-specific big data poses issues in knowledge discovery and decision support given its many Vs such as volume, variety, and visualization. In this work, we propose solutions via Ishikawa diagrams, JESS (Java Expert System Shell) with rule discovery, as well as visual analytics. We deploy data science and domain models, targeting engineering and scientific domains. Our results yield high accuracy, efficiency and cost-effectiveness.",
keywords = "Causal analysis, Vs of big data, decision support, expert systems, predictive modeling, scientific data mining",
author = "Varde, {Aparna S.} and Jianyu Liang and Zhaotong Yang and Sisson, {Richard D.}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Big Data, Big Data 2022 ; Conference date: 17-12-2022 Through 20-12-2022",
year = "2022",
doi = "10.1109/BigData55660.2022.10020637",
language = "English",
series = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6824--6826",
editor = "Shusaku Tsumoto and Yukio Ohsawa and Lei Chen and {Van den Poel}, Dirk and Xiaohua Hu and Yoichi Motomura and Takuya Takagi and Lingfei Wu and Ying Xie and Akihiro Abe and Vijay Raghavan",
booktitle = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",
}