A «smart component» data model in PLM

Yunpeng Li, Utpal Roy, Seung Jun Shin, Y. Tina Lee

Research output: Chapter in Book/Entry/PoemConference contribution

5 Scopus citations


Physical products are becoming smarter because of their increased number of embedded sensors and their real-time information-processing capabilities. Data analytics, particularly predictive analytics, is one of the most important of these capabilities because it uses statistical or machine-learning techniques to determine causal relations between input and output parameters. Many researchers have addressed the challenges in creating and evaluating predictive models. Few, however, have discussed how to employ such models effectively throughout a product's life cycle. In this paper, we address this issue by extending Product Lifecycle Management (PLM) systems to include «Smart Component» data models that incorporate predictive models as «parts» or «services» of products in their master records in PLM. These smart-component data models can be modularized, composed, reused, traced, maintained, and replaced on demand. We describe a prototype system to demonstrate the feasibility of the proposed data models using an open-source PLM platform.

Original languageEnglish (US)
Title of host publicationProceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Print)9781479999255
StatePublished - Dec 22 2015
Event3rd IEEE International Conference on Big Data, IEEE Big Data 2015 - Santa Clara, United States
Duration: Oct 29 2015Nov 1 2015


Other3rd IEEE International Conference on Big Data, IEEE Big Data 2015
Country/TerritoryUnited States
CitySanta Clara


  • PLM
  • PMML
  • predictive analytics
  • smart component
  • Smart product

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Software


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