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Visual insight of spatiotemporal IoT-generated contents

Published: 29 May 2018 Publication History

Abstract

The rapid evolution of the Internet of Things (IoT) and Big Data technology has been generating a large amount and variety of sensing contents, including numeric measured values (e.g., timestamps, geolocations, or sensor logs) and multimedia (e.g., images, audios, and videos). In analyzing and understanding heterogeneous types of IoT-generated contents better, data visualization is an essential component of exploratory data analyses to facilitate information perception and knowledge extraction. This study introduces a holistic approach of storing, processing, and visualizing IoT-generated contents to support context-aware spatiotemporal insight by combining deep learning techniques with a geographical map interface. Visualization is provided under an interactive web-based user interface to help the an efficient visual exploration considering both time and geolocation by easy spatiotemporal query user interface1.

References

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Xiaowei Zhou, Can Yang, and Weichuan Yu. Moving object detection by detecting contiguous outliers in the low-rank representation. IEEE Trans. Pattern Anal. Mach. Intell., 35(3):597--610, March 2013.
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Alper Yilmaz, Omar Javed, and Mubarak Shah. Object tracking: A survey. ACM Comput. Surv., 38(4), December 2006.
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Y. Yoo, K. Yun, S. Yun, J. Hong, H. Jeong, and J. Y. Choi. Visual path prediction in complex scenes with crowded moving objects. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2668--2677, June 2016.
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Bernd Resch, Ralf Wohlfahrt, and Christoph Wosniok. Web-based 4d visualization of marine geo-data using webgl. Cartography and Geographic Information Science, 41(3):235--247, 2014.
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R. Dietmar Muller, Xiaodong Qin, David T. Sandwell, Adriana Dutkiewicz, Simon E. Williams, Nicolas Flament, Stefan Maus, and Maria Seton. The gplates portal: Cloud-based interactive 3d visualization of global geophysical and geological data in a web browser. PLOS ONE, 11(3):1--17, 03 2016.
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Kyoung-Sook Kim, Dongmin Kim, Hyemi Jeong, and Hirotaka Ogawa. Stinuum: A holistic visual analysis of moving objects with open source software. In Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL'17, pages 85:1--85:4, New York, NY, USA, 2017. ACM.

Cited By

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  • (2020)A Visual Analytics Approach for Effective Radon Risk Perception in the IoT EraScience and Technologies for Smart Cities10.1007/978-3-030-51005-3_10(90-101)Online publication date: 28-Jul-2020

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Published In

cover image ACM Conferences
AVI '18: Proceedings of the 2018 International Conference on Advanced Visual Interfaces
May 2018
430 pages
ISBN:9781450356169
DOI:10.1145/3206505
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 May 2018

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Author Tags

  1. IoT-generated contents
  2. deep learning
  3. geovisualization
  4. object detection
  5. spatiotemporal analysis

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  • Demonstration

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AVI '18
AVI '18: 2018 International Conference on Advanced Visual Interfaces
May 29 - June 1, 2018
Grosseto, Castiglione della Pescaia, Italy

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AVI '18 Paper Acceptance Rate 19 of 77 submissions, 25%;
Overall Acceptance Rate 128 of 490 submissions, 26%

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Cited By

View all
  • (2020)A Visual Analytics Approach for Effective Radon Risk Perception in the IoT EraScience and Technologies for Smart Cities10.1007/978-3-030-51005-3_10(90-101)Online publication date: 28-Jul-2020

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