Specifying Latent Factors with a Domain Model for Personal Data Analysis
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.
2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech)