Specifying latent factors with a domain model for personal data analysis

Kiichi Tago, Kosuke Takagi, Kenichi Ito, Qun Jin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Personal data is data related to an individual, generated by an individual, or metadata about an individual. To analyze personal data comprehensively, it is needed to consider different types and sources of data. Moreover, it should be considered not only explicit attributes but also latent factors. In this study, to specify latent factors, we use Structural Equation Modeling (SEM) with a domain model for personal data analysis. The domain model represents the relationship between the latent factors and measures that are possible to be obtained by a wearable device. We construct an activeness model as the domain model and apply it for personal data analysis. The activeness level which is assumed as the latent factor is quantified by SEM. We verify the adaptability of the activeness model by comparing the case of classifying by the activeness factor with the case of not using latent factors. The result shows that the model has higher adaptability when personal data is classified by latent factors than only by labels.

Original languageEnglish
Title of host publicationProceedings - IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages292-299
Number of pages8
ISBN (Electronic)9781538675182
DOIs
Publication statusPublished - 2018 Oct 26
Event16th IEEE International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018 - Athens, Greece
Duration: 2018 Aug 122018 Aug 15

Publication series

NameProceedings - IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018

Other

Other16th IEEE International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018
CountryGreece
CityAthens
Period18/8/1218/8/15

Keywords

  • Latent factors
  • Personal data analysis
  • Structural Equation Modeling (SEM)

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Artificial Intelligence
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Control and Optimization

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  • Cite this

    Tago, K., Takagi, K., Ito, K., & Jin, Q. (2018). Specifying latent factors with a domain model for personal data analysis. In Proceedings - IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018 (pp. 292-299). [8511899] (Proceedings - IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00056