Visual analytics of heterogeneous data for criminal event analysis: VAST challenge 2015: Grand challenge

Junghoon Chae; Guizhen Wang; Benjamin Ahlbrand; Mahesh Babu Gorantla; Jiawei Zhang; Siqaio Chen; Hanye Xu; Jieqiong Zhao; William Hatton; Abish Malik; Sungahn Ko; David S. Ebert

doi:10.1109/VAST.2015.7347654

Visual analytics of heterogeneous data for criminal event analysis: VAST challenge 2015: Grand challenge

Junghoon Chae, Guizhen Wang, Benjamin Ahlbrand, Mahesh Babu Gorantla, Jiawei Zhang, Siqaio Chen, Hanye Xu, Jieqiong Zhao, William Hatton, Abish Malik, Sungahn Ko, David S. Ebert

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

3 Scopus citations

Abstract

We developed a visual analytics system to analyze the provided heterogeneous 2015 VAST Challenge data. This system utilized several analytic models and visualization techniques. Currently, the underlying data models and clustering techniques have limitations in processing the large volume of data in real time. Therefore, for future work, we will improve the scalability of our system to support real time interactivity and analysis.

Original language	English
Title of host publication	2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings
Editors	Min Chen, Gennady Andrienko
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	149-150
Number of pages	2
ISBN (Electronic)	9781467397834
DOIs	https://doi.org/10.1109/VAST.2015.7347654
State	Published - Dec 4 2015
Externally published	Yes
Event	10th IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Chicago, United States Duration: Oct 25 2015 → Oct 30 2015

Publication series

Name	2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings

Conference

Conference	10th IEEE Conference on Visual Analytics Science and Technology, VAST 2015
Country/Territory	United States
City	Chicago
Period	10/25/15 → 10/30/15

Funding

Acknowledgements: This work was partially funded by the U.S. Department of Homeland Security's VACCINE Center under Award Number 2009-ST- 061-CI0006.

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10.1109/VAST.2015.7347654

Cite this

Chae, J., Wang, G., Ahlbrand, B., Gorantla, M. B., Zhang, J., Chen, S., Xu, H., Zhao, J., Hatton, W., Malik, A., Ko, S., & Ebert, D. S. (2015). Visual analytics of heterogeneous data for criminal event analysis: VAST challenge 2015: Grand challenge. In M. Chen, & G. Andrienko (Eds.), 2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings (pp. 149-150). Article 7347654 (2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VAST.2015.7347654

Chae, Junghoon ; Wang, Guizhen ; Ahlbrand, Benjamin et al. / Visual analytics of heterogeneous data for criminal event analysis : VAST challenge 2015: Grand challenge. 2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings. editor / Min Chen ; Gennady Andrienko. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 149-150 (2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings).

@inproceedings{4f133c8e66cb4c54a3db68e12b600a05,

title = "Visual analytics of heterogeneous data for criminal event analysis: VAST challenge 2015: Grand challenge",

abstract = "We developed a visual analytics system to analyze the provided heterogeneous 2015 VAST Challenge data. This system utilized several analytic models and visualization techniques. Currently, the underlying data models and clustering techniques have limitations in processing the large volume of data in real time. Therefore, for future work, we will improve the scalability of our system to support real time interactivity and analysis.",

author = "Junghoon Chae and Guizhen Wang and Benjamin Ahlbrand and Gorantla, {Mahesh Babu} and Jiawei Zhang and Siqaio Chen and Hanye Xu and Jieqiong Zhao and William Hatton and Abish Malik and Sungahn Ko and Ebert, {David S.}",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 10th IEEE Conference on Visual Analytics Science and Technology, VAST 2015 ; Conference date: 25-10-2015 Through 30-10-2015",

year = "2015",

month = dec,

day = "4",

doi = "10.1109/VAST.2015.7347654",

language = "English",

series = "2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings",

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

pages = "149--150",

editor = "Min Chen and Gennady Andrienko",

booktitle = "2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings",

}

Chae, J, Wang, G, Ahlbrand, B, Gorantla, MB, Zhang, J, Chen, S, Xu, H, Zhao, J, Hatton, W, Malik, A, Ko, S & Ebert, DS 2015, Visual analytics of heterogeneous data for criminal event analysis: VAST challenge 2015: Grand challenge. in M Chen & G Andrienko (eds), 2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings., 7347654, 2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 149-150, 10th IEEE Conference on Visual Analytics Science and Technology, VAST 2015, Chicago, United States, 10/25/15. https://doi.org/10.1109/VAST.2015.7347654

Visual analytics of heterogeneous data for criminal event analysis: VAST challenge 2015: Grand challenge. / Chae, Junghoon; Wang, Guizhen; Ahlbrand, Benjamin et al.
2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings. ed. / Min Chen; Gennady Andrienko. Institute of Electrical and Electronics Engineers Inc., 2015. p. 149-150 7347654 (2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings).

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

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AU - Chae, Junghoon

AU - Wang, Guizhen

AU - Ahlbrand, Benjamin

AU - Gorantla, Mahesh Babu

AU - Zhang, Jiawei

AU - Chen, Siqaio

AU - Xu, Hanye

AU - Zhao, Jieqiong

AU - Hatton, William

AU - Malik, Abish

AU - Ko, Sungahn

AU - Ebert, David S.

PY - 2015/12/4

Y1 - 2015/12/4

N2 - We developed a visual analytics system to analyze the provided heterogeneous 2015 VAST Challenge data. This system utilized several analytic models and visualization techniques. Currently, the underlying data models and clustering techniques have limitations in processing the large volume of data in real time. Therefore, for future work, we will improve the scalability of our system to support real time interactivity and analysis.

AB - We developed a visual analytics system to analyze the provided heterogeneous 2015 VAST Challenge data. This system utilized several analytic models and visualization techniques. Currently, the underlying data models and clustering techniques have limitations in processing the large volume of data in real time. Therefore, for future work, we will improve the scalability of our system to support real time interactivity and analysis.

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Chae J, Wang G, Ahlbrand B, Gorantla MB, Zhang J, Chen S et al. Visual analytics of heterogeneous data for criminal event analysis: VAST challenge 2015: Grand challenge. In Chen M, Andrienko G, editors, 2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2015. p. 149-150. 7347654. (2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings). doi: 10.1109/VAST.2015.7347654

Visual analytics of heterogeneous data for criminal event analysis: VAST challenge 2015: Grand challenge

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