Four considerations for supporting visual analysis in display ecologies

Chung, Haeyong; North, Chris; Joshi, Sarang; Chen, Jian

doi:10.1109/vast.2015.7347628

Cited by 15 publications

(6 citation statements)

References 46 publications

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“…Elrod et al, 1992;Raskar et al, 1998;Izadi et al, 2003). Moreover, Chung et al (2015) assess that the use of multiple displays can enhance visual analysis through its discretized display space afforded by different screens.…”

Section: Related Workmentioning

confidence: 99%

Large-scale comparative visualisation of sets of multidimensional data

Vohl

Barnes

Fluke

et al. 2016

PeerJ Computer Science

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We present encube-a qualitative, quantitative and comparative visualisation and analysis system, with application to high-resolution, immersive three-dimensional environments and desktop displays. encube extends previous comparative visualisation systems by considering: (1) the integration of comparative visualisation and analysis into a unified system; (2) the documentation of the discovery process; and (3) an approach that enables scientists to continue the research process once back at their desktop. Our solution enables tablets, smartphones or laptops to be used as interaction units for manipulating, organising, and querying data. We highlight the modularity of encube, allowing additional functionalities to be included as required. Additionally, our approach supports a high level of collaboration within the physical environment. We show how our implementation of encube operates in a large-scale, hybrid visualisation and supercomputing environment using the CAVE2 at Monash University, and on a local desktop, making it a versatile solution. We discuss how our approach can help accelerate the discovery rate in a variety of research scenarios.

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Section: Related Workmentioning

confidence: 99%

Large-scale comparative visualisation of sets of multidimensional data

Vohl

Barnes

Fluke

et al. 2016

PeerJ Computer Science

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“…Addressing these two limitations, the goal of active research is to bring visual data analysis to advanced display environments that enable more data to be observed by more users. A natural step to achieve this goal is to combine several displays to form so-called display ecologies [Chung et al, 2015]. Display ecologies can facilitate visual data analysis in different ways.…”

Section: Introductionmentioning

confidence: 99%

Multi-display Visual Analysis: Model, Interface, and Layout Computation

Eichner,

Schumann,

Tominski

2019

Preprint

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Modern display environments offer great potential for involving multiple users in presentations, discussions, and data analysis sessions. By showing multiple views on multiple displays, information exchange can be improved, several perspectives on the data can be combined, and different analysis strategies can be pursued.In this report, we describe concepts to support display composition, information distribution, and analysis coordination for visual data analysis in multi-display environments. In particular, a basic model for layout modeling is introduced, a graphical interface for interactive generation of the model is presented, and a layout mechanism is described that arranges multiple views on multiple displays automatically. Furthermore, approaches to meta-analysis will be discussed. The developed approaches are demonstrated in a use case that focuses on parameter space analysis for the segmentation of time series data.

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“…A major gap in web-based visualization development remains: distribution of computation across multiple devices. A common ecosystem 4 of a web-based visualization involves a user that has access to multiple devices in their immediate physical surroundings. For example, if the user is accessing the visualization using a laptop, they may also have a smartphone in their pocket, a smartwatch on their wrist, a tablet in their backpack, and a personal computer in their office.…”

Section: Introductionmentioning

confidence: 99%

VisHive: Supporting web-based visualization through ad hoc computational clusters of mobile devices

Cui

Sen

Badam

et al. 2018

Information Visualization

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Current web-based visualizations are designed for single computers and cannot make use of additional devices on the client side, even if today’s users often have access to several, such as a tablet, a smartphone, and a smartwatch. We present a framework for ad hoc computational clusters that leverage these local devices for visualization computations. Furthermore, we present an instantiating JavaScript toolkit called VisHive for constructing web-based visualization applications that can transparently connect multiple devices—called cells—into such ad hoc clusters—called a hive—for local computation. Hives are formed either using a matchmaking service or through manual configuration. Cells are organized into a master–slave architecture, where the master provides the visual interface to the user and controls the slaves and the slaves perform computation. VisHive is built entirely using current web technologies, runs in the native browser of each cell, and requires no specific software to be downloaded on the involved devices. We demonstrate VisHive using four distributed examples: a text analytics visualization, a database query for exploratory visualization, a density-based spatial clustering of applications with noise clustering running on multiple nodes, and a principal component analysis implementation.

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Four considerations for supporting visual analysis in display ecologies

Cited by 15 publications

References 46 publications

Large-scale comparative visualisation of sets of multidimensional data

Large-scale comparative visualisation of sets of multidimensional data

Multi-display Visual Analysis: Model, Interface, and Layout Computation

VisHive: Supporting web-based visualization through ad hoc computational clusters of mobile devices

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