A Multi-comparable visual analytic approach for complex hierarchical data. (August 2018)

• Record Type: Journal Article
• Authors: Chen, Yi; Dong, Yu; Sun, Yuehong; Liang, Jie
• Is Part Of: Journal of visual languages & computing. Volume 47(2018)
• Page Start: 19
• Page End: 30
• Publication Date: 2018-08
• Subjects: Visual analysis -- Hierarchy comparison -- Multi-dimensional data -- Evaluation metric -- MRL standard; 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.2018.02.003
• Languages: English
• ISSNs: 1045-926X

Abstract

Abstract: Maximum residue limit (MRL) standard which specifies the highest level of every pesticide residue in different agricultural products plays a critical role in food safety. However, such standards which related to the characteristics of pesticides and the classification of agricultural products which organized into a hierarchical structure are complex and vary widely across different regions or countries. So it is a big challenge to compare multi-regional MRL standard data comprehensively. In this paper, we present a multi-comparable visual analytic approach for complex hierarchical data and a visual analytics system (McVA) to support multiple comparison and evaluation of MRL standard. With a cooperative multi-view visual design, our proposed approach links the hierarchies of MRL datasets and provides the capacity for comparison at different levels and dimensions. We also introduce a metric model for evaluating the completeness and strictness of MRL standards quantitatively. The case study of real problems and the positive feedback from domain experts demonstrate the effectiveness of this approach.