Munin: A Peer-to-Peer Middleware for Ubiquitous Analytics and Visualization Spaces

Badam, Sriram Karthik; Fisher, Eli; Elmqvist, Niklas

doi:10.1109/tvcg.2014.2337337

Cited by 24 publications

(10 citation statements)

References 55 publications

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“…Recent work has examined movement or proxemics as an input to visualization environments [BMG10, JHKH13]. as well as multi‐touch [JH14] and an ecology of devices [BFE15, MAN*14, HBED18].…”

Section: Related Workmentioning

confidence: 99%

Many At Once: Capturing Intentions to Create And Use Many Views At Once In Large Display Environments

Aurisano

Kumar

Alsaiari

et al. 2020

Computer Graphics Forum

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This paper describes results from an observational, exploratory study of visual data exploration in a large, multi‐view, flexible canvas environment. Participants were provided with a set of data exploration sub‐tasks associated with a local crime dataset and were instructed to pose questions to a remote mediator who would respond by generating and organizing visualizations on the large display. We observed that participants frequently posed requests to cast a net around one or several subsets of the data or a set of data attributes. They accomplished this directly and by utilizing existing views in unique ways, including by requesting to copy and pivot a group of views collectively and posing a set of parallel requests on target views expressed in one command. These observed actions depart from multi‐view flexible canvas environments that typically provide interfaces in support of generating one view at a time or actions that operate on one view at a time. We describe how participants used these ‘cast‐a‐net’ requests for tasks that spanned more than one view and describe design considerations for multi‐view environments that would support the observed multi‐view generation actions.

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Section: Related Workmentioning

confidence: 99%

Many At Once: Capturing Intentions to Create And Use Many Views At Once In Large Display Environments

Aurisano

Kumar

Alsaiari

et al. 2020

Computer Graphics Forum

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“…The support for cross‐device collaboration also has the potential to improve multidisciplinary and cross‐domain analysis by enabling actors from diverse backgrounds (e.g., scientists, domain experts, and laypersons or citizen scientists) to participate without requiring specialized devices or specific hardware (Badam, Fisher, & Elmqvist, ; Elmqvist & Irani, ). To provide effective support for cross‐device collaboration, the user interface needs to take advantage of the unique characteristics of diverse types of device.…”

Section: Research Challengesmentioning

confidence: 99%

Enabling collaborative GeoVisual analytics: Systems, techniques, and research challenges

Garcia-Chapeton

Ostermann

et al. 2018

Transactions in GIS

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Collaboration across disciplines is recognized as one of the great challenges for research in visual analysis of geographic information (GeoVisual Analytics, GVA). Considering the increasing availability of geodata and the complexity of analytical problems, the need to advance the support for collaborative work is becoming more pressing and prominent. This article contributes to this objective by reviewing the state‐of‐the‐art of the support for collaborative work in GVA systems and by identifying research challenges and proposing strategies to address them. We conducted a systematic review, resulting in the identification of 13 collaborative systems, 6 distinct collaborative techniques, and 3 research challenges. We conclude that GVA is moving toward more effective support of multidisciplinary and cross‐domain collaborative analysis. However, to materialize this potential, research is needed to improve the support for hybrid collaborative scenarios, cross‐device collaboration, and support for time‐critical and long‐term analysis.

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“…P2P networks are ideal for this purpose 35,36 as they do not set a hierarchy among the devices and they do not require a dedicated server infrastructure to create clusters.…”

Section: Design Framework: Ad Hoc Computational Clusters For Web-basementioning

confidence: 99%

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

Cui

Sen

Badam

et al. 2018

Information Visualization

Self Cite

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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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Munin: A Peer-to-Peer Middleware for Ubiquitous Analytics and Visualization Spaces

Cited by 24 publications

References 55 publications

Many At Once: Capturing Intentions to Create And Use Many Views At Once In Large Display Environments

Many At Once: Capturing Intentions to Create And Use Many Views At Once In Large Display Environments

Enabling collaborative GeoVisual analytics: Systems, techniques, and research challenges

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

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