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A Distance-Window Based Real-Time Processing of Spatial Data Streams

EasyChair Preprint no. 1548

9 pagesDate: September 22, 2019


Real-time and continuous processing of citywide spatial data is an essential requirement of smart cities to guarantee the delivery of basic life necessities to its residents and to maintain law and order in it. With the availability of low cost 3D scanners recently, citywide 3D spatial data can be obtained easily. 3D spatial data contains a wealth of information including images, point-cloud, GPS/IMU measurements, etc., and can be of potential use if integrated, processed and analyzed in real-time. The 3D spatial data is generated as continuous data stream, however traditionally it is processed offline. Many smart city applications require real-time integration, processing and analysis of spatial stream, for-instance, forest fire management, real-time road traffic analysis, disaster engulfed areas monitoring, people flow analysis, etc., however they suffer from slow offline processing of traditional systems. To make the most of this wealthy data resource, it must be processed and analyzed in real-time. This paper presents a framework for the continuous and real-time processing and analysis of 3D spatial streams. Furthermore, we propose a distance-based window for the continuous queries over 3D spatial streams. An experimental evaluation is also presented to prove the effectiveness of the proposed framework and the distance-based window.

Keyphrases: 3D spatial data, continuous query, data stream, Distance-based window, point cloud data, real-time processing

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Salman Shaikh and Akiyoshi Matono and Kyoung-Sook Kim},
  title = {A Distance-Window Based Real-Time Processing of Spatial Data Streams},
  howpublished = {EasyChair Preprint no. 1548},

  year = {EasyChair, 2019}}
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