BIG TRAJECTORY DATA: A DISTRIBUTED COMPUTING PERSPECTIVE
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 da...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2022-12-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVIII-4-W3-2022/21/2022/isprs-archives-XLVIII-4-W3-2022-21-2022.pdf |
| _version_ | 1811207476599586816 |
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| author | B. Anbaroğlu Y. Alter Y. Alter |
| author_facet | B. Anbaroğlu Y. Alter Y. Alter |
| author_sort | B. Anbaroğlu |
| collection | DOAJ |
| description | 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, <i>Volume</i> and <i>Variety</i>, 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. |
| first_indexed | 2024-04-12T04:04:51Z |
| format | Article |
| id | doaj.art-51d898084dae4e32b4be126ee5494168 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-04-12T04:04:51Z |
| publishDate | 2022-12-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-51d898084dae4e32b4be126ee54941682022-12-22T03:48:38ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342022-12-01XLVIII-4-W3-2022212710.5194/isprs-archives-XLVIII-4-W3-2022-21-2022BIG TRAJECTORY DATA: A DISTRIBUTED COMPUTING PERSPECTIVEB. Anbaroğlu0Y. Alter1Y. Alter2Dept. of Geomatics Engineering, Hacettepe University, Ankara, TürkiyeGraduate School of Science and Engineering, Hacettepe University, Ankara, TürkiyeAselsan Inc., Ankara, TürkiyeTrajectory 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, <i>Volume</i> and <i>Variety</i>, 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.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVIII-4-W3-2022/21/2022/isprs-archives-XLVIII-4-W3-2022-21-2022.pdf |
| spellingShingle | B. Anbaroğlu Y. Alter Y. Alter BIG TRAJECTORY DATA: A DISTRIBUTED COMPUTING PERSPECTIVE The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | BIG TRAJECTORY DATA: A DISTRIBUTED COMPUTING PERSPECTIVE |
| title_full | BIG TRAJECTORY DATA: A DISTRIBUTED COMPUTING PERSPECTIVE |
| title_fullStr | BIG TRAJECTORY DATA: A DISTRIBUTED COMPUTING PERSPECTIVE |
| title_full_unstemmed | BIG TRAJECTORY DATA: A DISTRIBUTED COMPUTING PERSPECTIVE |
| title_short | BIG TRAJECTORY DATA: A DISTRIBUTED COMPUTING PERSPECTIVE |
| title_sort | big trajectory data a distributed computing perspective |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVIII-4-W3-2022/21/2022/isprs-archives-XLVIII-4-W3-2022-21-2022.pdf |
| work_keys_str_mv | AT banbaroglu bigtrajectorydataadistributedcomputingperspective AT yalter bigtrajectorydataadistributedcomputingperspective AT yalter bigtrajectorydataadistributedcomputingperspective |