A Model and Software System for Coordinated and Multiple Views in Exploratory Visualization

Boukhelifa, Nadia; Rodgers, Peter

doi:10.1057/palgrave.ivs.9500057

Cited by 33 publications

(15 citation statements)

References 19 publications

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“…The problem of visual integration of multiple data sources is not new (see, for example, [3,40,41]) and is likely to remain a formidable problem for the foreseeable future. The increasing focus on systems biology creates new ultra-scale integrative visualomics challenges, in terms of the increasing heterogeneity and multitude of data sources as well as the growing size and complexity of individual data types.…”

Section: B Integrative Visual-omicsmentioning

confidence: 99%

An outlook into ultra-scale visualization of large-scale biological data

Samatova

Breimyer

Hendrix

et al. 2008

2008 Workshop on Ultrascale Visualization

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As bioinformatics has evolved from a reductionistic approach to a complementary multi-scale integrative approach, new challenges in ultra-scale visualization have arisen. Even though visualization is a critical component to large-scale biological data analysis, the ultra-scale nature of systems biology has given rise to novel problems in visualization that are not addressed by existing methods. Visualization is a rich and actively researched domain, and there are many open research questions pertaining to the increasing demands of visualization in bioinformatics. In this paper, we present several broadly important ultra-scale visualization challenges and discuss specific examples of ultrascale applications in systems biology.

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Section: B Integrative Visual-omicsmentioning

confidence: 99%

An outlook into ultra-scale visualization of large-scale biological data

Samatova

Breimyer

Hendrix

et al. 2008

2008 Workshop on Ultrascale Visualization

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“…To overcome this visual complexity, coordinated multiple views (CMV) were developed (Roberts 2007). CMV provides multiple views to visualize different aspects of the data being studied and the coordination may reveal insightful relationships in the data that otherwise might remain hidden (Boukhelifa andRodgers 2003, Robinson 2011). Several studies in various domains such as Keefe et al (2009), Zhang et al (2013) and Wang and Yuan (2014) have implemented CMV for their data exploratory tasks.…”

Section: Introductionmentioning

confidence: 99%

Interactive discovery of sequential patterns in time series of wind data

Yusof

Zurita-Milla²,

Kraak³

et al. 2016

International Journal of Geographical Information Science

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Wind speed and direction vary over space and time due to the interactions between different pressures and temperature gradients within the atmospheric layers. Near the earth's surface, these interactions are modulated by topography and artificial structures. Hence, characterizing wind behaviour over large areas and long periods is a complex but essential task for various energy-related applications. In this study, we present a novel approach to discover wind patterns by integrating sequential pattern mining and interactive visualization techniques. The approach relies on the use of the Linear time Closed pattern Miner sequence algorithm in conjunction with a time sliding window that allows the discovery of all sequential patterns present in the data. These patterns are then visualized using integrated 2D and 3D coordinated multiple views and visually explored to gain insight into the characteristics of the wind from a spatial, temporal and attribute (type of wind pattern) point of view. This proposed approach is used to analyse 10 years of hourly wind speed and direction data for 29 weather stations in the Netherlands. The results show that there are 15 main sequential patterns in the data. The spatial task shows that weather stations located in the same region do not necessarily experience similar wind pattern. For within the selected time interval, similar wind patterns can be observed in different stations and in the same station at different times of occurrence. The attribute task discovered that the repetitive occurrences of chosen pattern indicate as regular wind behaviour at different weather stations that persisted continuously over time. The results of these tasks show that the proposed interactive discovery facilitates the understanding of wind dynamics in space and time.ARTICLE HISTORY

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“…In the coordination model prototyped in CViews [5], explicit coordination objects in a coordination space manage visual parameters and access data using a dataflow model to define a particular type of coordination, such as brushing. Views connect to coordination objects through translation functions.…”

Section: Related Systemsmentioning

confidence: 99%

Building Highly-Coordinated Visualizations in Improvise

Weaver

IEEE Symposium on Information Visualization

137

102

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Improvise is a fully-implemented system in which users build and browse multiview visualizations interactively using a simple shared-object coordination mechanism coupled with a flexible, expression-based visual abstraction language. By coupling visual abstraction with coordination, users gain precise control over how navigation and selection in a visualization affects the appearance of data in individual views. As a result, it is practical to build visualizations with more views and richer coordination in Improvise than in other visualization systems. Building and browsing activities are integrated in a single, live user interface that lets users alter visualizations quickly and incrementally during data exploration.

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A Model and Software System for Coordinated and Multiple Views in Exploratory Visualization

Cited by 33 publications

References 19 publications

An outlook into ultra-scale visualization of large-scale biological data

An outlook into ultra-scale visualization of large-scale biological data

Interactive discovery of sequential patterns in time series of wind data

Building Highly-Coordinated Visualizations in Improvise

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