SaaS for Automated Job Performance Appraisals Using Service Technologies and Big Data Analytics

I-Ling YEN, Farokh BASTANI, Yongtao HUANG, Yuqun ZHANG, Xin YAO

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

4 Citations (Scopus)

Abstract

In this paper, we present a new SaaS (software as a service) design for employee job performance appraisals, SaaS-JPA. We use IoT and computer systems to collect data related to the daily works of employees. A semantic model is developed to guide the data collection process, facilitate data interpretation and interoperation, and enable big data analysis to make job performance appraisal decisions. We also propose two new performance assessment models: The similarity-based relative performance model and the revenue-based performance model. These performance models are enabled by the service technologies and big data analytics. Finally, we discuss the design of SaaS-JPA. © 2017 IEEE.
Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 24th International Conference on Web Services, ICWS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages412-419
Number of pages8
ISBN (Print)9781538607527
DOIs
Publication statusPublished - Jun 2017
Externally publishedYes

Keywords

  • Job performance appraisal
  • revenue-based performance
  • semantic model for performance appraisal
  • similarity-based relative performance
  • software as a service (SaaS)

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