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An Unified Spatial Index and Visualization Method for the Trajectory and Grid Queries in Internet of Things

  • Han, Jinju (Dept. of Software Convergence Engineering, Kunsan National University) ;
  • Na, Chul-Won (Dept. of Software Convergence Engineering, Kunsan National University) ;
  • Lee, Dahee (Dept. of Software Convergence Engineering, Kunsan National University) ;
  • Lee, Do-Hoon (Dept. of Software Convergence Engineering, Kunsan National University) ;
  • On, Byung-Won (Dept. of Software Convergence Engineering, Kunsan National University) ;
  • Lee, Ryong (Research Data Sharing Center, Division of National Science and Technology Data, Korea Institute of Science and Technology Information) ;
  • Park, Min-Woo (Research Data Sharing Center, Division of National Science and Technology Data, Korea Institute of Science and Technology Information) ;
  • Lee, Sang-Hwan (Research Data Sharing Center, Division of National Science and Technology Data, Korea Institute of Science and Technology Information)
  • Received : 2019.08.14
  • Accepted : 2019.09.06
  • Published : 2019.09.30

Abstract

Recently, a variety of IoT data is collected by attaching geosensors to many vehicles that are on the road. IoT data basically has time and space information and is composed of various data such as temperature, humidity, fine dust, Co2, etc. Although a certain sensor data can be retrieved using time, latitude and longitude, which are keys to the IoT data, advanced search engines for IoT data to handle high-level user queries are still limited. There is also a problem with searching large amounts of IoT data without generating indexes, which wastes a great deal of time through sequential scans. In this paper, we propose a unified spatial index model that handles both grid and trajectory queries using a cell-based space-filling curve method. also it presents a visualization method that helps user grasp intuitively. The Trajectory query is to aggregate the traffic of the trajectory cells passed by taxi on the road searched by the user. The grid query is to find the cells on the road searched by the user and to aggregate the fine dust. Based on the generated spatial index, the user interface quickly summarizes the trajectory and grid queries for specific road and all roads, and proposes a Web-based prototype system that can be analyzed intuitively through road and heat map visualization.

Keywords

References

  1. Ki Yong Lee, Minju Seo, Ryong Lee, Minwoo Park, Sang-Hwan Lee, "An Efficient Method of Processing Spatio-Temporal Joins in IoT (Internet of Things) Environments," Journal of KIISE, Vol. 46, No.1, pp. 88-96, Jan, 2019.
  2. Dong-Un Lee, Yunseok Rhee, “A Multi-dimensional Query Processing Scheme for Stream Data using Range Query Indexing,” Journal of the Korea Society of Computer and Information, Vol. 14, No. 2, pp. 69-77, Feb, 2009.
  3. Eun-young Park, Young-ho Park, "A Transforming Method for Natural Environment Data Collected from IoT Sensors," Proceedings of KIIT Conference, pp. 457-460, Jun, 2018.
  4. Ianqiu Xu, "Range Queries on Multi-Attribute Trajectories," IEEE Transactions on Knowledge and Data Engineering, VoL. 30, No. 6, pp. 1206-1211, Dec, 2018. https://doi.org/10.1109/TKDE.2017.2787711
  5. Guttman, "R-Trees: A Dynamic Index Structure for Spatial Searching," Proc. ACM SIGMOD, pp. 47-57, Jun, 1984.
  6. Zheng Wu, “Travel Time Estimation using Spatio-Temporal Index Based on Cassandra,” ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, Vol. 4, No. 4, pp. 232-242, Sep, 2018.
  7. K. Dhanasree, C. Shobabindu, "A survey on OLAP," IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), May, 2016.
  8. Joya A. Deri, Franz Franchetti, and Jose M.F. Moura, "Big Data Computation of Taxi Movement in New York City", IEEE International Conference on Big Data (Big Data), pp. 2616-2625, Dec, 2016.
  9. Namshik Choi, Athita Onuean, Hanmin Jung, "On Visualization of Trajectory Data for Traffic Flow Simulation of Urban-scale," Proceedings of the Korean Institute of Information and Commucation Sciences Conference, pp.582-585, Oct, 2018.
  10. Lee Minsoo, Kim Yearn Jeong, Yoon Hyejung, “Multi-query Indexing Technique for Efficient Query Processing on Stream Data in Sensor Networks,” Journal of Korea Multimedia Society, Vol. 10, No. 11, pp. 1367-1383, Nov, 2007.
  11. Road data, http://data.nsdi.go.kr/dataset/12902
  12. QGIS program, https://ko.wikipedia.org/wiki/QGIS
  13. Hilburt curve, https://en.wikipedia.org/wiki/Hilbert_curve
  14. Euclidian distance, https://ko.wikipedia.org/wiki/Euclidiandistance