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Visualizing Sensor Data Hauptseminar Information Visualization - - - PowerPoint PPT Presentation

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Visualizing Sensor Data Hauptseminar Information Visualization - Wintersemester 2008/2009" Christian Richter LFE Medieninformatik 16.02.2008 LMU Department of Media Informatics | Hauptseminar WS 2008/2009 |

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LMU Department of Media Informatics | Hauptseminar WS 2008/2009 | Christian.Hans.Richter@stud.ifi.lmu.de

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Visualizing Sensor Data

Hauptseminar “Information Visualization - Wintersemester 2008/2009"

Christian Richter LFE Medieninformatik 16.02.2008

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LMU Department of Media Informatics | Hauptseminar WS 2008/2009 | Christian.Hans.Richter@stud.ifi.lmu.de

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Challenges and Problems

Unique properties of sensor data

Large amount of data Multidimensionality of data Real time data

Reliability of sensors Lifetime of the sensor network Different IDs for the same object from different sensors Answering of “on-the-fly” queries not reliable Fitting visualization according to user’s ideas

[15][16][17]

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LMU Department of Media Informatics | Hauptseminar WS 2008/2009 | Christian.Hans.Richter@stud.ifi.lmu.de

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Sensors – Taxonomy(I)

Measurands

[1][2][3][11][14][18][19][20]

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Sensors – Taxonomy(II)

Field of application

[8][11][18][19][20]

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LMU Department of Media Informatics | Hauptseminar WS 2008/2009 | Christian.Hans.Richter@stud.ifi.lmu.de

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Sensors – Taxonomy(III)

Additional taxonomies:

Active & passive sensors (electrical / no electrical impulse) Absolute & relative sensors (fixed / relative scale)

[1][13][18][19][20]

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LMU Department of Media Informatics | Hauptseminar WS 2008/2009 | Christian.Hans.Richter@stud.ifi.lmu.de

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Sensors - Data Fusion

Problem: Large data sets & multidimensionality Solution: Data Fusion

Feature Extraction Data Cleaning Data Reduction Dimension Reduction

[15][21][22]

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LMU Department of Media Informatics | Hauptseminar WS 2008/2009 | Christian.Hans.Richter@stud.ifi.lmu.de

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Sensors - Space & Time

Additional important information of sensor data: space and time Space

Relative (e.g. door sensor) Absolute (e.g. position sensor)

Time

Momentary (e.g. sonar) Continuous (e.g. heart rate)

[11][23][24]

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LMU Department of Media Informatics | Hauptseminar WS 2008/2009 | Christian.Hans.Richter@stud.ifi.lmu.de

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Visualizations – Taxonomy (I)

Classification by Data Type (Shneiderman):

1-dimensional 2-dimensional 3-dimensional Temporal

[5][6][25][26]

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LMU Department of Media Informatics | Hauptseminar WS 2008/2009 | Christian.Hans.Richter@stud.ifi.lmu.de

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Visualizations – Taxonomy (II)

Classification by Data Type (Shneiderman):

Multi-dimensional Tree Network

[11][12][25][26]

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LMU Department of Media Informatics | Hauptseminar WS 2008/2009 | Christian.Hans.Richter@stud.ifi.lmu.de

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Perception

Visualizations – Guidelines (I)

Common Sense

[4][7][27][28]

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LMU Department of Media Informatics | Hauptseminar WS 2008/2009 | Christian.Hans.Richter@stud.ifi.lmu.de

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Visualizations – Guidelines (II)

Interface User

[8][9][27][28]

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LMU Department of Media Informatics | Hauptseminar WS 2008/2009 | Christian.Hans.Richter@stud.ifi.lmu.de

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Starting Points

Task itself as a starting point Number of sensors and dimension of the data Shneiderman‘s „Visual Information Seeking Mantra“

Overview first Zoom and filter Details on demand

[25]

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Mapping

[25][26]

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LMU Department of Media Informatics | Hauptseminar WS 2008/2009 | Christian.Hans.Richter@stud.ifi.lmu.de

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Examples (I)

Oximeter measurements: Records SpO2 concentration and heart rate Data is 1-dimensional and relative-continuous

[29]

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LMU Department of Media Informatics | Hauptseminar WS 2008/2009 | Christian.Hans.Richter@stud.ifi.lmu.de

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Weather map showing Europe Data is 1-dimensional and absolute-momentary

Examples (II)

[4]

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LMU Department of Media Informatics | Hauptseminar WS 2008/2009 | Christian.Hans.Richter@stud.ifi.lmu.de

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Examples (III)

Showing map of a navigation system Data is 2-dimensional and relative-momentary

[10]

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LMU Department of Media Informatics | Hauptseminar WS 2008/2009 | Christian.Hans.Richter@stud.ifi.lmu.de

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Summary & Conclusion

A lot of possibilities to classify sensors Necessity of data fusion Importance of space and time Taxonomy of visualizations according to Shneiderman Guidelines (perception, common sense, interface, user) Check of the mapping with examples Mapping hard because of the development in hard- and software Visualizations look and feel according to every day life visualizations

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LMU Department of Media Informatics | Hauptseminar WS 2008/2009 | Christian.Hans.Richter@stud.ifi.lmu.de

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Questions? Questions?

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LMU Department of Media Informatics | Hauptseminar WS 2008/2009 | Christian.Hans.Richter@stud.ifi.lmu.de

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References (I)

[1] http://www.elderly.com/ (Micro) [2] http://de.wiktionary.org/wiki/Thermometer (Thermometer) [3] http://www.solarenergy-shop.ch/ (Bewegungsmelder) [4] http://www.mir-co.net/wetter/wetterkarten.htm (Wetterkarte) [5] http://www.buechertransportdienst.de/ (Karte) [6] http://www.dinosaurisle.com/ (Timeline) [7] http://www.jux.de (Ampel) [8] http://www.fit.fraunhofer.de/ (PDA) [9] http://www.wiedervermarktung.de/notebooks.html (Notebook) [10] http://www.connect.de/ (Navi) [11] http://de.wikibooks.org/wiki/Hauptseite (Baum, EKG, Geigerzähler, Wüste, Nordpol, Tag, Nacht) [12] http://mein-messebett.de/bilder.htm (Netzwerk) [13] http://www.codeproject.com (Radar) [14] http://www.palintest.com.au/ (ph-Meter)

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LMU Department of Media Informatics | Hauptseminar WS 2008/2009 | Christian.Hans.Richter@stud.ifi.lmu.de

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References (II)

[15] C. Chong and S. Kumar. Sensor networks: evolution, opportunities, and challenges. Proceedings of the IEEE, 91(8):1247–1256, 2003. [16] D. Cook. Making Sense of Sensor Data. IEEE PERVASIVE COMPUTING, pages 105–108, 2007. [17] C. Plaisant. The challenge of information visualization evaluation. In Proceedings of the working conference on Advanced visual interfaces, pages 109–116. ACM New York, NY, USA, 2004. [18] J. Fraden. Handbook of Modern Sensors: Physics, Designs, and Applications. Springer, 2004. [19] R. Luo, C. Yih, and K. Su. Multisensor fusion and integration: approaches, applications, andfuture research

  • directions. Sensors Journal, IEEE, 2(2):107–119, 2002.

[20] R. White. A Sensor Classification Scheme. Ultrasonics, Ferroelectrics and Frequency Control, IEEE Transactions

  • n, 34(2):124–126, 1987.

[21] D. Ganesan, D. Estrin, and J. Heidemann. DIMENSIONS: Why do we need a new Data Handling architecture for Sensor Networks? [22] P. Tan. Knowledge Discovery from Sensor Data. SENSORSPETERBOROUGH-, 23(3):14, 2006. [23] D. Niculescu et al. Positioning in ad hoc sensor networks. IEEE Network, 18(4):24–29, 2004. [24] K. R¨omer and F. Mattern. Towards a unified view on space and time in sensor networks. Computer Communications, 28(13):1484–1497, 2005. [25] B. Shneiderman. The eyes have it: a task by data type taxonomy for informationvisualizations. In Visual Languages, 1996. Proceedings., IEEE Symposium on, pages 336–343, 1996.

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LMU Department of Media Informatics | Hauptseminar WS 2008/2009 | Christian.Hans.Richter@stud.ifi.lmu.de

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References (III)

[26] B. Shneiderman, S. Card, J. Mackinlay, and B. Shneiderman. Readings in Information Visualization: Using Vision to Think. Morgan Kaufmann, 1999. [27] C. G. Healey. Perception in Visualization. 2007. [28] B. Hull, V. Bychkovsky, Y. Zhang, K. Chen, M. Goraczko, A. Miu, E. Shih, H. Balakrishnan, and S. Madden. CarTel: a distributed mobile sensor computing system. In Proceedings of the 4th international conference on Embedded networked sensor systems, pages 125–138. ACM Press New York, NY, USA, 2006. [29] F. Michahelles, P. Matter, A. Schmidt, and B. Schiele. Applying wearable sensors to avalanche rescue. Computers & Graphics, 27(6):839–847, 2003.