The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLVIII-4/W3-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4/W3-2022, 21–27, 2022
https://doi.org/10.5194/isprs-archives-XLVIII-4-W3-2022-21-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4/W3-2022, 21–27, 2022
https://doi.org/10.5194/isprs-archives-XLVIII-4-W3-2022-21-2022
 
02 Dec 2022
02 Dec 2022

BIG TRAJECTORY DATA: A DISTRIBUTED COMPUTING PERSPECTIVE

B. Anbaroğlu1 and Y. Alter2,3 B. Anbaroğlu and Y. Alter
  • 1Dept. of Geomatics Engineering, Hacettepe University, Ankara, Türkiye
  • 2Graduate School of Science and Engineering, Hacettepe University, Ankara, Türkiye
  • 3Aselsan Inc., Ankara, Türkiye

Keywords: Distributed computing, big spatial data, cloud computing, trajectory, 3V

Abstract. Trajectory data constitute location of objects at specified time intervals. The continuous availability of GNSS signals, or discrete availability of sensor systems such as license plate recognition cameras are used to generate trajectory data. Consequently, in a smart city context, big trajectory data are being generated on a daily basis. The analysis of big trajectory data entails the use of a distributed environment to conduct analysis, and at least two data sources. The literature review conducted in this paper shows that the two Vs of big data, Volume and Variety, may not be satisfied since researchers usually rely on a centralised computing environment, and analyse data coming from a single data source. Out of the 17 papers published from 2020 in Scopus, only five of them relied on a distributed computing environment, and two of them utilised more than one data source.