Name
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.jvlc.2018.02.003 | Journal of Visual Languages & Computing |
Keywords | Field | DocType |
Visual analysis,Hierarchy comparison,Multi-dimensional data,Evaluation metric,MRL standard | Data mining,Communication design,Maximum Residue Limit,Computer science,Visual analytics,Multiple comparisons problem,Hierarchy,Completeness (statistics),Hierarchical database model | Journal |
Volume | ISSN | Citations |
47 | 1045-926X | 1 |
PageRank | References | Authors |
0.35 | 34 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Chen | 1 | 98 | 25.29 |
| Yu Dong | 2 | 1 | 0.69 |
| Yuehong Sun | 3 | 2 | 2.41 |
| Jie Liang | 4 | 86 | 10.85 |