Ishikawa, JESS, and Visual Analytics for Engineering

Aparna S. Varde, Jianyu Liang, Zhaotong Yang, Richard D. Sisson

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
EditorsShusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6824-6826
Number of pages3
ISBN (Electronic)9781665480451
DOIs
StatePublished - 2022
Event2022 IEEE International Conference on Big Data, Big Data 2022 - Osaka, Japan
Duration: 17 Dec 202220 Dec 2022

Publication series

NameProceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

Conference

Conference2022 IEEE International Conference on Big Data, Big Data 2022
Country/TerritoryJapan
CityOsaka
Period17/12/2220/12/22

Keywords

  • Causal analysis
  • Vs of big data
  • decision support
  • expert systems
  • predictive modeling
  • scientific data mining

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