Uncertainty-aware visual analytics for exploring human behaviors from heterogeneous spatial temporal data. (October 2018)

• Record Type: Journal Article
• Authors: Chen, Siming; Wang, Zuchao; Liang, Jie; Yuan, Xiaoru
• Is Part Of: Journal of visual languages & computing. Volume 48(2018)
• Page Start: 187
• Page End: 198
• Publication Date: 2018-10
• Subjects: Spatial-temporal data -- Uncertainty -- Visual analytics; 00-01 -- 99-00; Visual programming languages (Computer science) -- Periodicals; Visual programming (Computer science) -- Periodicals; Programming languages (Electronic computers) -- Semantics -- Periodicals; Langages de programmation visuelle -- Périodiques; Programmation visuelle -- Périodiques; Langages de programmation -- Sémantique -- Périodiques; Programming languages (Electronic computers) -- Semantics; Visual programming (Computer science); Visual programming languages (Computer science); Periodicals; Electronic journals; 005
• DOI: 10.1016/j.jvlc.2018.06.007
• Languages: English
• ISSNs: 1045-926X

Abstract

Abstract: When analyzing human behaviors, we need to construct the human behaviors from multiple sources of data, e.g. trajectory data, transaction data, identity data, etc. The problems we're facing are the data conflicts, different resolution, missing and conflicting data, which together lead to the uncertainty in the spatial temporal data. Such uncertainty in data leads to difficulties and even failure in the visual analytics task for analyzing people behavior, pattern and outliers. However, traditional automatic methods can not solve the problems in such complex scenario, where the uncertain and conflicting patterns are not well-defined. To solve the problems, we proposed a semi-automatic approach, for users to solve the conflicts and identify the uncertainties. To be general, we summarized five types of uncertainties and solutions to conduct the tasks of behavior analysis. Combined with the uncertainty-aware methods, we proposed a visual analytics system to analyze human behaviors, detect patterns and find outliers. Case studies from the IEEE VAST Challenge 2014 dataset confirm the effectiveness of our approach.