Adaptive clutter-aware visualization for mobile data stream mining

M. Gaber; S. Krishnaswamy; B. Gillick; N. Nicoloudis; J. Liono; H. Al-Taiar; A. Zaslavsky

doi:10.1109/ICTAI.2010.116

Adaptive clutter-aware visualization for mobile data stream mining

M. Gaber
, S. Krishnaswamy
, B. Gillick
, N. Nicoloudis
, J. Liono
, H. Al-Taiar
, A. Zaslavsky

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

There is an emerging focus on real-time data stream analysis on mobile devices. A wide range of data stream processing applications are targeted to run on mobile handheld devices with limited computational capabilities such as patient monitoring, driver monitoring, providing real-time analysis and visualization for emergency and disaster management, real-time optimization for courier pick-up and delivery etc. There are many challenges in visualization of the analysis/data stream mining results on a mobile device. These include coping with the small screen real-estate and effective presentation of highly dynamic and real-time analysis. This paper proposes a generic theory for visualization on small screens that we term Adaptive Clutter Reduction ACR. Based on ACR, we have developed and experimentally validated a novel data stream clustering result visualization technique that we term Clutter-Aware Clustering Visualizer (CACV). Experimental results on both synthetic and real datasets using the Google Andriod platform are presented proving the effectiveness of the proposed techniques.

Original language	English
Title of host publication	2010 22nd international conference on tools with artificial intelligence (ICTAI). Vol. 2
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	304 -311
ISBN (Print)	9781424488179
DOIs	https://doi.org/10.1109/ICTAI.2010.116
Publication status	Published - 2010
Event	IEEE 22nd International Conference on Tools with Artificial Intelligence - Arras, France Duration: 27 Oct 2010 → 29 Oct 2010

Conference

Conference	IEEE 22nd International Conference on Tools with Artificial Intelligence
Abbreviated title	ICTAI 2010
Country/Territory	France
City	Arras
Period	27/10/10 → 29/10/10

Keywords

adaptive clutter aware visualization
computational capabilities
data stream processing applications
disaster management
mobile data stream mining
mobile devices
mobile handheld devices
real-time analysis
data mining
data visualisation
mobile computing
real-time systems

Access to Document

10.1109/ICTAI.2010.116

Cite this

Gaber, M., Krishnaswamy, S., Gillick, B., Nicoloudis, N., Liono, J., Al-Taiar, H., & Zaslavsky, A. (2010). Adaptive clutter-aware visualization for mobile data stream mining. In 2010 22nd international conference on tools with artificial intelligence (ICTAI). Vol. 2 (pp. 304 -311). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICTAI.2010.116

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title = "Adaptive clutter-aware visualization for mobile data stream mining",

abstract = "There is an emerging focus on real-time data stream analysis on mobile devices. A wide range of data stream processing applications are targeted to run on mobile handheld devices with limited computational capabilities such as patient monitoring, driver monitoring, providing real-time analysis and visualization for emergency and disaster management, real-time optimization for courier pick-up and delivery etc. There are many challenges in visualization of the analysis/data stream mining results on a mobile device. These include coping with the small screen real-estate and effective presentation of highly dynamic and real-time analysis. This paper proposes a generic theory for visualization on small screens that we term Adaptive Clutter Reduction ACR. Based on ACR, we have developed and experimentally validated a novel data stream clustering result visualization technique that we term Clutter-Aware Clustering Visualizer (CACV). Experimental results on both synthetic and real datasets using the Google Andriod platform are presented proving the effectiveness of the proposed techniques.",

keywords = "adaptive clutter aware visualization, computational capabilities, data stream processing applications, disaster management, mobile data stream mining, mobile devices, mobile handheld devices, real-time analysis, data mining, data visualisation, mobile computing, real-time systems",

author = "M. Gaber and S. Krishnaswamy and B. Gillick and N. Nicoloudis and J. Liono and H. Al-Taiar and A. Zaslavsky",

year = "2010",

doi = "10.1109/ICTAI.2010.116",

language = "English",

isbn = "9781424488179",

pages = "304 --311",

booktitle = "2010 22nd international conference on tools with artificial intelligence (ICTAI). Vol. 2",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "IEEE 22nd International Conference on Tools with Artificial Intelligence, ICTAI 2010 ; Conference date: 27-10-2010 Through 29-10-2010",

}

Gaber, M, Krishnaswamy, S, Gillick, B, Nicoloudis, N, Liono, J, Al-Taiar, H & Zaslavsky, A 2010, Adaptive clutter-aware visualization for mobile data stream mining. in 2010 22nd international conference on tools with artificial intelligence (ICTAI). Vol. 2. Institute of Electrical and Electronics Engineers Inc., Piscataway, pp. 304 -311, IEEE 22nd International Conference on Tools with Artificial Intelligence, Arras, France, 27/10/10. https://doi.org/10.1109/ICTAI.2010.116

Adaptive clutter-aware visualization for mobile data stream mining. / Gaber, M.; Krishnaswamy, S.; Gillick, B. et al.
2010 22nd international conference on tools with artificial intelligence (ICTAI). Vol. 2. Piscataway: Institute of Electrical and Electronics Engineers Inc., 2010. p. 304 -311.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

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AU - Gaber, M.

AU - Krishnaswamy, S.

AU - Gillick, B.

AU - Nicoloudis, N.

AU - Liono, J.

AU - Al-Taiar, H.

AU - Zaslavsky, A.

PY - 2010

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N2 - There is an emerging focus on real-time data stream analysis on mobile devices. A wide range of data stream processing applications are targeted to run on mobile handheld devices with limited computational capabilities such as patient monitoring, driver monitoring, providing real-time analysis and visualization for emergency and disaster management, real-time optimization for courier pick-up and delivery etc. There are many challenges in visualization of the analysis/data stream mining results on a mobile device. These include coping with the small screen real-estate and effective presentation of highly dynamic and real-time analysis. This paper proposes a generic theory for visualization on small screens that we term Adaptive Clutter Reduction ACR. Based on ACR, we have developed and experimentally validated a novel data stream clustering result visualization technique that we term Clutter-Aware Clustering Visualizer (CACV). Experimental results on both synthetic and real datasets using the Google Andriod platform are presented proving the effectiveness of the proposed techniques.

AB - There is an emerging focus on real-time data stream analysis on mobile devices. A wide range of data stream processing applications are targeted to run on mobile handheld devices with limited computational capabilities such as patient monitoring, driver monitoring, providing real-time analysis and visualization for emergency and disaster management, real-time optimization for courier pick-up and delivery etc. There are many challenges in visualization of the analysis/data stream mining results on a mobile device. These include coping with the small screen real-estate and effective presentation of highly dynamic and real-time analysis. This paper proposes a generic theory for visualization on small screens that we term Adaptive Clutter Reduction ACR. Based on ACR, we have developed and experimentally validated a novel data stream clustering result visualization technique that we term Clutter-Aware Clustering Visualizer (CACV). Experimental results on both synthetic and real datasets using the Google Andriod platform are presented proving the effectiveness of the proposed techniques.

KW - adaptive clutter aware visualization

KW - computational capabilities

KW - data stream processing applications

KW - disaster management

KW - mobile data stream mining

KW - mobile devices

KW - mobile handheld devices

KW - real-time analysis

KW - data mining

KW - data visualisation

KW - mobile computing

KW - real-time systems

U2 - 10.1109/ICTAI.2010.116

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M3 - Conference contribution

SN - 9781424488179

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EP - 311

BT - 2010 22nd international conference on tools with artificial intelligence (ICTAI). Vol. 2

PB - Institute of Electrical and Electronics Engineers Inc.

CY - Piscataway

T2 - IEEE 22nd International Conference on Tools with Artificial Intelligence

Y2 - 27 October 2010 through 29 October 2010

ER -

Adaptive clutter-aware visualization for mobile data stream mining

Abstract

Conference

Keywords

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