Temporal Treemaps: Static Visualization of Evolving Trees

Köpp, Wiebke; Weinkauf, Tino

doi:10.1109/tvcg.2018.2865265

Cited by 20 publications

(44 citation statements)

References 36 publications

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“…[19,21,44,46,48,52]), and dynamic topology (e.g. [26,29]). Cui et al employ dynamic simplification of hierarchies evolving over time, with the ability to interactively zoom to lower levels of detail [7].…”

Section: Visual Comparisonmentioning

confidence: 99%

Selection Bias Tracking and Detailed Subset Comparison for High-Dimensional Data

Borland

Wang

Zhang

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. Exploratory cohort selection in high-dimensional datasets can lead to selection bias-unintended side-effects in variable distributions-that may go unnoticed by the user. Our selection bias tracking system and detailed cohort comparison visualizations, deployed in a medical temporal event sequence visual analytics tool, include (a) a cohort provenance tree to keep track of created cohorts and indicate when selection bias may have occurred, (b-d) a suite of high-dimensional cohort comparison visualizations that employ hierarchical aggregation to display the differences between two cohorts in detail, and (e) data-type dependent comparisons of individual variable distributions.Abstract-The collection of large, complex datasets has become common across a wide variety of domains. Visual analytics tools increasingly play a key role in exploring and answering complex questions about these large datasets. However, many visualizations are not designed to concurrently visualize the large number of dimensions present in complex datasets (e.g. tens of thousands of distinct codes in an electronic health record system). This fact, combined with the ability of many visual analytics systems to enable rapid, ad-hoc specification of groups, or cohorts, of individuals based on a small subset of visualized dimensions, leads to the possibility of introducing selection bias-when the user creates a cohort based on a specified set of dimensions, differences across many other unseen dimensions may also be introduced. These unintended side effects may result in the cohort no longer being representative of the larger population intended to be studied, which can negatively affect the validity of subsequent analyses. We present techniques for selection bias tracking and visualization that can be incorporated into high-dimensional exploratory visual analytics systems, with a focus on medical data with existing data hierarchies. These techniques include: (1) tree-based cohort provenance and visualization, including a user-specified baseline cohort that all other cohorts are compared against, and visual encoding of cohort "drift", which indicates where selection bias may have occurred, and (2) a set of visualizations, including a novel icicle-plot based visualization, to compare in detail the per-dimension differences between the baseline and a user-specified focus cohort. These techniques are integrated into a medical temporal event sequence visual analytics tool. We present example use cases and report findings from domain expert user interviews.

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Section: Visual Comparisonmentioning

confidence: 99%

Selection Bias Tracking and Detailed Subset Comparison for High-Dimensional Data

Borland

Wang

Zhang

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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“…The GO-NTG algorithm then processes these abstractions during post hoc analysis to compute NTGs, where the layout is derived with the algorithm of Lukasczyk et al [19]. Recently, Köpp et al [16] proposed an improved layout algorithm that could be used instead.…”

Section: Related Workmentioning

confidence: 99%

“…GenerateImages( K, f , T t , φ t , ψ t , C, L, P, n ) 1 // Get branches sorted by persistence in descending order 2 B ← ComputeBranchDecomposition( T , ψ t ) 3 // Create groups for the first n most persistent branches 4 G ← / 0 5 for i ← 0 to n − 1 do 6 NewGroup( G, B i ) 7 // Add remaining branches to closest group8 for i ← n to |B| do ← GetMostPersistentAttachedBranch( B, B i , ψ t ) ← GetGroup( G, B ) 11AddToGroup( G, B i ) 12 // Generate group images for all persistence intervals and levels13 foreach group G ∈ G do14 foreach threshold p i ∈ P where p i = max(P) do Filter grouped branches by persistence16 …”

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confidence: 99%

Dynamic Nested Tracking Graphs

Lukasczyk

Garth

Weber

et al. 2020

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. The topology-based visual analytics framework supports the feature-centered navigation of Cinema databases consisting of image and analysis products generated during large-scale simulation runs, where numerous features are organized into a manageable amount of hierarchical groups that can be explored in a level-of-detail approach. Here, the interface shows an ensemble member of the viscous finger dataset, where colors encode individual fingers for salt concentration level 30. The prime interaction device of the framework is a nested tracking graph (NTG) that simultaneously displays the temporal evolution of superlevel set components for multiple levels (bottom). The NTG is used to navigate through time and retrieve component images from the database (top left), whereas the split tree (top center) and persistence diagram (top right) support the user in selecting important levels and filter criteria.Abstract-This work describes an approach for the interactive visual analysis of large-scale simulations, where numerous superlevel set components and their evolution are of primary interest. The approach first derives, at simulation runtime, a specialized Cinema database that consists of images of component groups, and topological abstractions. This database is processed by a novel graph operation-based nested tracking graph algorithm (GO-NTG) that dynamically computes NTGs for component groups based on size, overlap, persistence, and level thresholds. The resulting NTGs are in turn used in a feature-centered visual analytics framework to query specific database elements and update feature parameters, facilitating flexible post hoc analysis.

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“…17,18 A static visualization for dynamic trees, based on the popular treemap convention is proposed by Köpp and Weinkauf. 19…”

Section: Related Workmentioning

confidence: 99%

Stable visualization of connected components in dynamic graphs

Giacomo

Didimo

Kaufmann

et al. 2020

Information Visualization

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One of the primary goals of many systems for the visual analysis of dynamically changing networks is to maintain the stability of the drawing throughout the sequence of graph changes. We investigate the scenario where the changes are determined by a stream of events, each being either an edge addition or an edge removal. The visualization must be updated immediately after each new event is received. Our main goal is to provide the user with an intuitive visualization that highlights the different connected components of the graph while preserving the user’s mental map after each event. The drawing stability is measured in terms of changes in the orthogonal relationships between vertices of two consecutive drawings. We describe two different visualization models, one for the 1-dimensional space and the other for the 2-dimensional space. In both models the connected components are drawn inside rectangular regions. To validate our approach, we report the results of an experimental analysis that compares the drawing stability of the online algorithm with that of an offline algorithm that knows in advance the whole sequence of events. We also present a case study of our online algorithm on a collaboration network.

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Temporal Treemaps: Static Visualization of Evolving Trees

Cited by 20 publications

References 36 publications

Selection Bias Tracking and Detailed Subset Comparison for High-Dimensional Data

Selection Bias Tracking and Detailed Subset Comparison for High-Dimensional Data

Dynamic Nested Tracking Graphs

Stable visualization of connected components in dynamic graphs

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