iTree: Exploring time-varying data using indexable tree

Graphs represent general node‐link diagrams and have long been utilized in scientific visualization for data organization and management. However, using graphs as a visual representation and interface for navigating and exploring scientific data sets has a much shorter history, yet the amount of work along this direction is clearly on the rise in recent years. In this paper, we take a holistic perspective and survey graph‐based representations and techniques for scientific visualization. Specifically, we classify these representations and techniques into four categories, namely partition‐wise, relationship‐wise, structure‐wise and provenance‐wise. We survey related publications in each category, explaining the roles of graphs in related work and highlighting their similarities and differences. At the end, we reexamine these related publications following the graph‐based visualization pipeline. We also point out research trends and remaining challenges in graph‐based representations and techniques for scientific visualization.

show abstract

Section: Relationship‐wise Representations and Techniquesmentioning

confidence: 99%

“…Gu and Wang [GW13] introduced iTree for time-varying data visualization, which integrates data classifying, indexing and compacting into a single framework. They utilized the symbolic aggregate approximation (SAX) for data compacting and indexable SAX (iSAX) for indexing.…”

Section: Hierarchical Clustering or Partitioning Treesmentioning

confidence: 99%

Graphs in Scientific Visualization: A Survey

Wang

Tao

2016

Computer Graphics Forum

Self Cite

View full text Add to dashboard Cite

show abstract

“…Their interface enables intuitive data brushing in 2D and connection with the underlying 3D data. Gu and Wang designed two representations, TransGraph [11] and iTree [12], for time-varying data visualization. TransGraph [11] encodes hierarchical transition relationships for a time-varying data set to guide relationship exploration and tracking.…”

Section: Related Workmentioning

confidence: 99%

“…TransGraph [11] encodes hierarchical transition relationships for a time-varying data set to guide relationship exploration and tracking. iTree [12] integrates efficient data compacting, indexing, and classification into a single framework. A hyperbolic layout algorithm is employed to draw the iTree with a large number of nodes and focus+context visualization is provided for interaction.…”

Section: Related Workmentioning

confidence: 99%

Mining Graphs for Understanding Time-Varying Volumetric Data

Wang

Peterka

et al. 2016

IEEE Trans. Visual. Comput. Graphics

Self Cite

View full text Add to dashboard Cite

A notable recent trend in time-varying volumetric data analysis and visualization is to extract data relationships and represent them in a low-dimensional abstract graph view for visual understanding and making connections to the underlying data. Nevertheless, the ever-growing size and complexity of data demands novel techniques that go beyond standard brushing and linking to allow significant reduction of cognition overhead and interaction cost. In this paper, we present a mining approach that automatically extracts meaningful features from a graph-based representation for exploring time-varying volumetric data. This is achieved through the utilization of a series of graph analysis techniques including graph simplification, community detection, and visual recommendation. We investigate the most important transition relationships for time-varying data and evaluate our solution with several time-varying data sets of different sizes and characteristics. For gaining insights from the data, we show that our solution is more efficient and effective than simply asking users to extract relationships via standard interaction techniques, especially when the data set is large and the relationships are complex. We also collect expert feedback to confirm the usefulness of our approach.

show abstract

“…These include sophisticated visualization and interaction techniques that allow users to freely explore the data at various levels of aggregation [3,14,22,51,54]. However, effective interaction with spatio-temporal visualizations remains a challenge [25,49] and even using these techniques domain experts may still need to examine a prohibitively large number of spatio-temporal slices to discover interesting patterns that represent both regular and irregular behavior. Contributions.…”

Section: Introductionmentioning

confidence: 99%