RagRug: A Toolkit for Situated Analytics

Philipp Fleck; Aimee Sousa Calepso; Sebastian Hubenschmid; Michael Sedlmair; Dieter Schmalstieg

doi:10.1109/TVCG.2022.3157058

RagRug: A Toolkit for Situated Analytics

Philipp Fleck, Aimee Sousa Calepso, Sebastian Hubenschmid, Michael Sedlmair, Dieter Schmalstieg

Institute of Computer Graphics and Vision (7100)

Research output: Contribution to journal › Article › peer-review

Abstract

We present RagRug, an open-source toolkit for situated analytics. The abilities of RagRug go beyond previous immersive analytics toolkits by focusing on specific requirements emerging when using augmented reality (AR) rather than virtual reality. RagRug combines state of the art visual encoding capabilities with a comprehensive physical-virtual model, which lets application developers systematically describe the physical objects in the real world and their role in AR. We connect AR visualization with data streams from the Internet of Things using distributed dataflow. To this aim, we use reactive programming patterns so that visualizations become context-aware, i.e., they adapt to events coming in from the environment. The resulting authoring system is low-code; it emphasises describing the physical and the virtual world and the dataflow between the elements contained therein. We describe the technical design and implementation of RagRug, and report on five example applications illustrating the toolkit's abilities.

Original language	English
Journal	IEEE Transactions on Visualization and Computer Graphics
Early online date	7 Mar 2022
DOIs	https://doi.org/10.1109/TVCG.2022.3157058
Publication status	E-pub ahead of print - 7 Mar 2022

Keywords

Augmented Reality
Computational modeling
Data models
Data visualization
Encoding
Immersive Analytics
Situated Analytics
Solid modeling
Three-dimensional displays
Visual Analytics
Visualization

ASJC Scopus subject areas

Software
Signal Processing
Computer Vision and Pattern Recognition
Computer Graphics and Computer-Aided Design

Access to Document

10.1109/TVCG.2022.3157058

https://ieeexplore.ieee.org/document/9729627

Cite this

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title = "RagRug: A Toolkit for Situated Analytics",

abstract = "We present RagRug, an open-source toolkit for situated analytics. The abilities of RagRug go beyond previous immersive analytics toolkits by focusing on specific requirements emerging when using augmented reality (AR) rather than virtual reality. RagRug combines state of the art visual encoding capabilities with a comprehensive physical-virtual model, which lets application developers systematically describe the physical objects in the real world and their role in AR. We connect AR visualization with data streams from the Internet of Things using distributed dataflow. To this aim, we use reactive programming patterns so that visualizations become context-aware, i.e., they adapt to events coming in from the environment. The resulting authoring system is low-code; it emphasises describing the physical and the virtual world and the dataflow between the elements contained therein. We describe the technical design and implementation of RagRug, and report on five example applications illustrating the toolkit's abilities.",

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author = "Philipp Fleck and {Sousa Calepso}, Aimee and Sebastian Hubenschmid and Michael Sedlmair and Dieter Schmalstieg",

year = "2022",

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day = "7",

doi = "10.1109/TVCG.2022.3157058",

language = "English",

journal = "IEEE Transactions on Visualization and Computer Graphics",

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AU - Fleck, Philipp

AU - Sousa Calepso, Aimee

AU - Hubenschmid, Sebastian

AU - Sedlmair, Michael

AU - Schmalstieg, Dieter

PY - 2022/3/7

Y1 - 2022/3/7

N2 - We present RagRug, an open-source toolkit for situated analytics. The abilities of RagRug go beyond previous immersive analytics toolkits by focusing on specific requirements emerging when using augmented reality (AR) rather than virtual reality. RagRug combines state of the art visual encoding capabilities with a comprehensive physical-virtual model, which lets application developers systematically describe the physical objects in the real world and their role in AR. We connect AR visualization with data streams from the Internet of Things using distributed dataflow. To this aim, we use reactive programming patterns so that visualizations become context-aware, i.e., they adapt to events coming in from the environment. The resulting authoring system is low-code; it emphasises describing the physical and the virtual world and the dataflow between the elements contained therein. We describe the technical design and implementation of RagRug, and report on five example applications illustrating the toolkit's abilities.

AB - We present RagRug, an open-source toolkit for situated analytics. The abilities of RagRug go beyond previous immersive analytics toolkits by focusing on specific requirements emerging when using augmented reality (AR) rather than virtual reality. RagRug combines state of the art visual encoding capabilities with a comprehensive physical-virtual model, which lets application developers systematically describe the physical objects in the real world and their role in AR. We connect AR visualization with data streams from the Internet of Things using distributed dataflow. To this aim, we use reactive programming patterns so that visualizations become context-aware, i.e., they adapt to events coming in from the environment. The resulting authoring system is low-code; it emphasises describing the physical and the virtual world and the dataflow between the elements contained therein. We describe the technical design and implementation of RagRug, and report on five example applications illustrating the toolkit's abilities.

KW - Augmented Reality

KW - Computational modeling

KW - Data models

KW - Data visualization

KW - Encoding

KW - Immersive Analytics

KW - Situated Analytics

KW - Solid modeling

KW - Three-dimensional displays

KW - Visual Analytics

KW - Visualization

UR - https://github.com/philfleck/ragrug

UR - http://www.scopus.com/inward/record.url?scp=85126319038&partnerID=8YFLogxK

U2 - 10.1109/TVCG.2022.3157058

DO - 10.1109/TVCG.2022.3157058

M3 - Article

SN - 1077-2626

JO - IEEE Transactions on Visualization and Computer Graphics

JF - IEEE Transactions on Visualization and Computer Graphics

ER -

RagRug: A Toolkit for Situated Analytics

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

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FWF - RagRug - Exploring the Design Space of Situated Analytics