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

EasyChair Preprint 1548

Abstract

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, Distance-based window, continuous query, data stream, point cloud data, real-time processing

@booklet{EasyChair:1548,
  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 1548},
  year      = {EasyChair, 2019}}