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Cabinet Tree: an orthogonal enclosure approach to visualizing and exploring big data
- Source :
- Journal of Big Data. 2(1)
- Publisher :
- Springer Nature
-
Abstract
- Treemaps are well-known for visualizing hierarchical data. Most related approaches have been focused on layout algorithms and paid little attention to other display properties and interactions. Furthermore, the structural information in conventional Treemaps is too implicit for viewers to perceive. This paper presents Cabinet Tree, an approach that: i) draws branches explicitly to show relational structures, ii) adapts a space-optimized layout for leaves and maximizes the space utilization, iii) uses coloring and labeling strategies to clearly reveal patterns and contrast different attributes intuitively. We also apply the continuous node selection and detail window techniques to support user interaction with different levels of the hierarchies. Our quantitative evaluations demonstrate that Cabinet Tree achieves good scalability for increased resolutions and big datasets.
- Subjects :
- Information Systems and Management
Theoretical computer science
Tree drawing
Computer Networks and Communications
business.industry
Computer science
Big data
Enclosure
Quantitative Evaluations
computer.file_format
computer.software_genre
Hierarchical database model
Hardware and Architecture
Cabinet (file format)
Scalability
Computational Science and Engineering
Data mining
business
computer
Information Systems
Subjects
Details
- Language :
- English
- ISSN :
- 21961115
- Volume :
- 2
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Journal of Big Data
- Accession number :
- edsair.doi.dedup.....9c5a44c38ebbadb15d26732ab5d1a117
- Full Text :
- https://doi.org/10.1186/s40537-015-0022-3