article

Feature-Driven Visual Analytics of Chaotic Parameter-Dependent Movement

Authors:

C. TominskiAuthors Info & Claims

Computer Graphics Forum, Volume 34, Issue 3

Pages 421 - 430

https://doi.org/10.1111/cgf.12654

Published: 01 June 2015 Publication History

Abstract

Analyzing movements in their spatial and temporal context is a complex task. We are additionally interested in understanding the movements' dependency on parameters that govern the processes behind the movement. We propose a visual analytics approach combining analytic, visual, and interactive means to deal with the added complexity. The key idea is to perform an analytical extraction of features that capture distinct movement characteristics. Different parameter configurations and extracted features are then visualized in a compact fashion to facilitate an overview of the data. Interaction enables the user to access details about features, to compare features, and to relate features back to the original movement. We instantiate our approach with a repository of more than twenty accepted and novel features to help analysts in gaining insight into simulations of chaotic behavior of thousands of entities over thousands of data points. Domain experts applied our solution successfully to study dynamic groups in such movements in relation to thousands of parameter configurations.

References

[1]

<label>{AAB*13a}</label> Andrienko G., Andrienko N., Bak P., Keim D., Wrobel S.: Visual Analytics of Movement. Springer, 2013. 1, 2

Digital Library

[2]

<label>{AAB*13b}</label> Andrienko N., Andrienko G., Barrett L., Dostie M., Henzi P.: Space Transformation for Understanding Group Movement. IEEE Trans. on Visualization and Computer Graphics Volume 19, Issue 12 2013. 2

Digital Library

[3]

<label>{BHMU11}</label> Bittig A.T., Haack F., Maus C., Uhrmacher A.M.: Adapting Rule-based Model Descriptions for Simulating in Continuous and Hybrid Space. In CMSB 2011, ACM. 7

Digital Library

[4]

<label>{BM10}</label> Bruckner S., Möller T.: Result-Driven Exploration of Simulation Parameter Spaces for Visual Effects Design. IEEE Trans. on Visualization and Computer Graphics Volume 16, Issue 6 2010. 2

Digital Library

[5]

<label>{BPFG11}</label> Berger W., Piringer H., Filzmoser P., Gröller E.: Uncertainty-Aware Exploration of Continuous Parameter Spaces Using Multivariate Prediction. Computer Graphics Forum Volume 30, Issue 3 2011. 2

[6]

<label>{BSM*13}</label> Bergner S., Sedlmair M., Möller T., Abdolyousefi S.N., Saad A.: ParaGlide: Interactive Parameter Space Partitioning for Computer Simulations. IEEE Trans. on Visualization and Computer Graphics Volume 19, Issue 9 2013. 2

Digital Library

[7]

<label>{BvLA*11}</label> Bremm S., von Landesberger T., Andrienko G., Andrienko N., Schreck T.: Interactive Analysis of Object Group Changes over Time</otherTitle>. In <otherTitle>EuroVA 2011. 3

[8]

<label>{DV10}</label> Demšar U., Virrantaus K.: Space-Time Density of Trajectories: Exploring Spatio-Temporal Patterns in Movement Data. Int. Journal of Geographical Information Science Volume 24, Issue 10 2010. 4

Digital Library

[9]

<label>{GH97}</label> Garland M., Heckbert P.S.: Surface simplification using quadric error metrics. In SIGGRAPH 1997, ACM. 5

Digital Library

[10]

<label>{GYHZ13}</label> Guo H., Yuan X., Huang J., Zhu X.: Coupled Ensemble Flow Line Advection and Analysis. IEEE Trans. on Visualization and Computer Graphics Volume 19, Issue 12 2013. 3

Digital Library

[11]

<label>{HB03}</label> Harrower M.A., Brewer C.A.: "http://ColorBrewer.org": An Online Tool for Selecting Color Schemes for Maps. The Cartographic Journal Volume 40, Issue 1 2003. 5

[12]

<label>{HBRU13}</label> Haack F., Burrage K., Redmer R., Uhrmacher A.: Studying the Role of Lipid Rafts on Protein Receptor Bindings with Cellular Automata. IEEE/ACM Trans. on Computational Biology and Bioinformatics Volume 10, Issue 3 2013. 2

Digital Library

[13]

<label>{JYJ11}</label> Jeung H., Yiu M.L., Jensen C.S.: Trajectory Pattern Mining. In Computing with Spatial Trajectories. Springer, 2011. 4

[14]

<label>{Kho06}</label> Kholodenko B.N.: Cell-Signalling Dynamics in Time and Space. Nature Reviews Molecular Cell Biology Volume 7, Issue 3 2006. 2

[15]

<label>{KYS09}</label> Kikuchi A., Yamamoto H., Sato A.: Selective Activation Mechanisms of Wnt Signaling Pathways. Trends in Cell Biology Volume 19, Issue 3 2009. 2

[16]

<label>{LMK07}</label> Lam H., Munzner T., Kincaid R.: Overview Use in Multiple Visual Information Resolution Interfaces. IEEE Trans. on Visualization and Computer Graphics Volume 13, Issue 6 2007. 6

Digital Library

[17]

<label>{LRHS14}</label> Luboschik M., Rybacki S., Haack F., Schulz H.-J.: Supporting the Integrated Visual Analysis of Input Parameters and Simulation Trajectories. Computers & Graphics Volume 39 2014. 2, 8

[18]

<label>{LTB*12}</label> Luboschik M., Tominski C., Bittig A.T., Uhrmacher A.M., Schumann H.: Towards Interactive Visual Analysis of Microscopic-Level Simulation Data. In SIGRAD 2012, Linköping University Electronic Press. 8

[19]

<label>{MAB*97}</label> Marks J., Andalman B., Beardsley P.A., Freeman W., Gibson S., Hodgins J., Kang T., Mirtich B., Pfister H., Ruml W., Ryall K., Seims J., Shieber S.: Design Galleries: A General Approach to Setting Parameters for Computer Graphics and Animation. In SIGGRAPH 1997, ACM. 2

Digital Library

[20]

<label>{MGS*14}</label> Matković K., Gračanin D., Splechtna R., Jelović M., Stehno B., Hauser H., Purgathofer W.: Visual Analytics for Complex Engineering Systems: Hybrid Visual Steering of Simulation Ensembles. IEEE Trans. on Visualization and Computer Graphics Volume 20, Issue 12 2014. 3

[21]

<label>{NBPH06}</label> Nicolau D.V., Burrage K., Parton R.G., Hancock J.F.: Identifying Optimal Lipid Raft Characteristics Required To Promote Nanoscale Protein-Protein Interactions on the Plasma Membrane. Molecular and Cellular Biology Volume 26, Issue 1 2006. 2

[22]

<label>{OJ14}</label> Obermaier H., Joy K.: Future Challenges for Ensemble Visualization. IEEE Computer Graphics and Applications Volume 34, Issue 3 2014. 1

[23]

<label>{PBK10}</label> Piringer H., Berger W., Krasser J.: Hyper-MoVal: Interactive Visual Validation of Regression Models for Real-Time Simulation. Computer Graphics Forum Volume 29, Issue 3 2010. 2

[24]

<label>{RPN*08}</label> Rinzivillo S., Pedreschi D., Nanni M., Giannotti F., Andrienko N., Andrienko G.: Visually Driven Analysis of Movement Data by Progressive Clustering. Information Visualization Volume 7, Issue 3-4 2008. 2

Digital Library

[25]

<label>{RPS01}</label> Reinders F., Post F.H., Spoelder H.J.: Visualization of Time-Dependent Data with Feature Tracking and Event Detection. The Visual Computer Volume 17, Issue 1 2001. 2

[26]

<label>{RTBW*09}</label> Reda K., Tantipathananandh C., Berger-Wolf T., Leigh J., Johnson A.: SocioScape - a Tool for Interactive Exploration of Spatio-Temporal Group Dynamics in Social Networks. Poster at InfoVis, 2009. 2

[27]

<label>{SC07}</label> Salvador S., Chan P.: Toward Accurate Dynamic Time Warping in Linear Time and Space. Intelligent Data Analysis Volume 11, Issue 5 2007. 5

Digital Library

[28]

<label>{SHB*14}</label> Sedlmair M., Heinzl C., Bruckner S., Piringer H., Möller T.: Visual parameter space analysis: A conceptual framework. IEEE Trans. on Visualization and Computer Graphics Volume 20, Issue 12 2014. 2

[29]

<label>{TPRH11}</label> Turkay C., Parulek J., Reuter N., Hauser H.: Interactive visual analysis of temporal cluster structures. Computer Graphics Forum Volume 30, Issue 3 2011. 3

[30]

<label>{TTNtW10}</label> Takahashi K., Tânase-Nicola S., ten Wolde P.R.: Spatio-temporal Correlations Can Drastically Change the Response of a MAPK Pathway. Proc. of the National Academy of Sciences Volume 107, Issue 6 2010. 2

[31]

<label>{TWSM*11}</label> Torsney-Weir T., Saad A., Möller T., Hege H.-C., Weber B., Verbavatz J.-M.: Tuner: Principled Parameter Finding for Image Segmentation Algorithms Using Visual Response Surface Exploration. IEEE Trans. on Visualization and Computer Graphics Volume 17, Issue 10 2011. 2

Digital Library

[32]

<label>{vLBSF14}</label> von Landesberger T., Bremm S., Schreck T., Fellner D.W.: Feature-Based Automatic Identification of Interesting Data Segments in Group Movement Data. Information Visualization Volume 13, Issue 3 2014. 1, 2, 3

[33]

<label>{VP04}</label> Verma V., Pang A.: Comparative Flow Visualization. IEEE Trans. on Visualization and Computer Graphics Volume 10, Issue 6 2004. 3

Digital Library

[34]

<label>{vPGL*14}</label> van Pelt R., Gasteiger R., Lawonn K., Meuschke M., Preim B.: Comparative blood flow visualization for cerebral aneurysm treatment assessment. Computer Graphics Forum Volume 33, Issue 3 2014. 3

Digital Library

[35]

<label>{WSD11}</label> Wood J., Slingsby A., Dykes J.: Visualizing the Dynamics of London's Bicycle-Hire Scheme. Cartographica Volume 46, Issue 4 2011. 2

[36]

<label>{WvdWvW09}</label> Willems N., van de Wetering H., van Wijk J.J.: Visualization of Vessel Movements. Computer Graphics Forum Volume 28, Issue 3 2009. 2

Cited By

Piccolotto NBögl MMuehlmann CNordhausen KFilzmoser PSchmidt JMiksch S(2024)Data Type Agnostic Visual Sensitivity AnalysisIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332720330:1(1106-1116)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3327203
Buchmüller JSchlegel UCakmak EKeim DDimara E(2021)SpatialRugsComputers and Graphics10.1016/j.cag.2021.08.003101:C(23-34)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1016/j.cag.2021.08.003
Vad VByška JJurčík AViola IGröller EHauser HMarques SDamborský JKozlíková BBruckner SHennemuth AKainz B(2017)WatergateProceedings of the Eurographics Workshop on Visual Computing for Biology and Medicine10.2312/vcbm.20171235(33-42)Online publication date: 7-Sep-2017
https://dl.acm.org/doi/10.2312/vcbm.20171235

Index Terms

Feature-Driven Visual Analytics of Chaotic Parameter-Dependent Movement
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Visual analytics

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Published In

cover image Computer Graphics Forum

Computer Graphics Forum Volume 34, Issue 3

June 2015

510 pages

ISSN:0167-7055

EISSN:1467-8659

Issue’s Table of Contents

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The Eurographs Association & John Wiley & Sons, Ltd.

Chichester, United Kingdom

Publication History

Published: 01 June 2015

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Cited By

Piccolotto NBögl MMuehlmann CNordhausen KFilzmoser PSchmidt JMiksch S(2024)Data Type Agnostic Visual Sensitivity AnalysisIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332720330:1(1106-1116)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3327203
Buchmüller JSchlegel UCakmak EKeim DDimara E(2021)SpatialRugsComputers and Graphics10.1016/j.cag.2021.08.003101:C(23-34)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1016/j.cag.2021.08.003
Vad VByška JJurčík AViola IGröller EHauser HMarques SDamborský JKozlíková BBruckner SHennemuth AKainz B(2017)WatergateProceedings of the Eurographics Workshop on Visual Computing for Biology and Medicine10.2312/vcbm.20171235(33-42)Online publication date: 7-Sep-2017
https://dl.acm.org/doi/10.2312/vcbm.20171235

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