Processing high-volume geospatial data

A case of monitoring heavy haul railway operations

Prajwol Sangat, Maria Indrawan-Santiago, David Taniar, Beng Oh, Paul Reichl

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

3 Citations (Scopus)

Abstract

Sensor technology such as GPS can be used in the mapping of transportation networks (e.g., road, rail). However, GPS suffers from errors in positional accuracy due to factors such as signal arrival time. In railway systems, positional accuracy is of utmost importance to identify state of track and wagons for safety and maintenance. Along with GPS, the numerous lightweight sensors installed in each wagon produce a high-velocity geospatial data that needs to be processed continuously and the traditional data processing and storage applications can not handle it. We propose efficient algorithms and a suitable data structure to achieve rapid and accurate location mappings. Our large-scale evaluation demonstrates that the system is accurate and capable of real-time performance.

Original languageEnglish
Title of host publicationProcedia Computer Science
Subtitle of host publicationInternational Conference on Computational Science (ICCS 2016)
EditorsIlkay Altintas, Michael Norman, Jack Dongarra, Valeria V. Krzhizhanovskaya, Michael Lees, Peter M. A. Sloot
Place of PublicationAmsterdam, Netherlands
PublisherElsevier
Pages2221-2225
Number of pages5
Volume80
DOIs
Publication statusPublished - 2016
EventInternational Conference on Computational Science 2016 - San Diego, United States of America
Duration: 6 Jun 20168 Jun 2016
Conference number: 16th

Conference

ConferenceInternational Conference on Computational Science 2016
Abbreviated titleICCS
CountryUnited States of America
CitySan Diego
Period6/06/168/06/16

Keywords

  • Batch data processing
  • Big data
  • Geospatial data

Cite this

Sangat, P., Indrawan-Santiago, M., Taniar, D., Oh, B., & Reichl, P. (2016). Processing high-volume geospatial data: A case of monitoring heavy haul railway operations. In I. Altintas, M. Norman, J. Dongarra, V. V. Krzhizhanovskaya, M. Lees, & P. M. A. Sloot (Eds.), Procedia Computer Science: International Conference on Computational Science (ICCS 2016) (Vol. 80, pp. 2221-2225). Amsterdam, Netherlands: Elsevier. https://doi.org/10.1016/j.procs.2016.05.385
Sangat, Prajwol ; Indrawan-Santiago, Maria ; Taniar, David ; Oh, Beng ; Reichl, Paul. / Processing high-volume geospatial data : A case of monitoring heavy haul railway operations. Procedia Computer Science: International Conference on Computational Science (ICCS 2016). editor / Ilkay Altintas ; Michael Norman ; Jack Dongarra ; Valeria V. Krzhizhanovskaya ; Michael Lees ; Peter M. A. Sloot. Vol. 80 Amsterdam, Netherlands : Elsevier, 2016. pp. 2221-2225
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title = "Processing high-volume geospatial data: A case of monitoring heavy haul railway operations",
abstract = "Sensor technology such as GPS can be used in the mapping of transportation networks (e.g., road, rail). However, GPS suffers from errors in positional accuracy due to factors such as signal arrival time. In railway systems, positional accuracy is of utmost importance to identify state of track and wagons for safety and maintenance. Along with GPS, the numerous lightweight sensors installed in each wagon produce a high-velocity geospatial data that needs to be processed continuously and the traditional data processing and storage applications can not handle it. We propose efficient algorithms and a suitable data structure to achieve rapid and accurate location mappings. Our large-scale evaluation demonstrates that the system is accurate and capable of real-time performance.",
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author = "Prajwol Sangat and Maria Indrawan-Santiago and David Taniar and Beng Oh and Paul Reichl",
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booktitle = "Procedia Computer Science",
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}

Sangat, P, Indrawan-Santiago, M, Taniar, D, Oh, B & Reichl, P 2016, Processing high-volume geospatial data: A case of monitoring heavy haul railway operations. in I Altintas, M Norman, J Dongarra, VV Krzhizhanovskaya, M Lees & PMA Sloot (eds), Procedia Computer Science: International Conference on Computational Science (ICCS 2016). vol. 80, Elsevier, Amsterdam, Netherlands, pp. 2221-2225, International Conference on Computational Science 2016, San Diego, United States of America, 6/06/16. https://doi.org/10.1016/j.procs.2016.05.385

Processing high-volume geospatial data : A case of monitoring heavy haul railway operations. / Sangat, Prajwol; Indrawan-Santiago, Maria; Taniar, David; Oh, Beng; Reichl, Paul.

Procedia Computer Science: International Conference on Computational Science (ICCS 2016). ed. / Ilkay Altintas; Michael Norman; Jack Dongarra; Valeria V. Krzhizhanovskaya; Michael Lees; Peter M. A. Sloot. Vol. 80 Amsterdam, Netherlands : Elsevier, 2016. p. 2221-2225.

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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AU - Taniar, David

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AU - Reichl, Paul

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AB - Sensor technology such as GPS can be used in the mapping of transportation networks (e.g., road, rail). However, GPS suffers from errors in positional accuracy due to factors such as signal arrival time. In railway systems, positional accuracy is of utmost importance to identify state of track and wagons for safety and maintenance. Along with GPS, the numerous lightweight sensors installed in each wagon produce a high-velocity geospatial data that needs to be processed continuously and the traditional data processing and storage applications can not handle it. We propose efficient algorithms and a suitable data structure to achieve rapid and accurate location mappings. Our large-scale evaluation demonstrates that the system is accurate and capable of real-time performance.

KW - Batch data processing

KW - Big data

KW - Geospatial data

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M3 - Conference Paper

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SP - 2221

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BT - Procedia Computer Science

A2 - Altintas, Ilkay

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Sangat P, Indrawan-Santiago M, Taniar D, Oh B, Reichl P. Processing high-volume geospatial data: A case of monitoring heavy haul railway operations. In Altintas I, Norman M, Dongarra J, Krzhizhanovskaya VV, Lees M, Sloot PMA, editors, Procedia Computer Science: International Conference on Computational Science (ICCS 2016). Vol. 80. Amsterdam, Netherlands: Elsevier. 2016. p. 2221-2225 https://doi.org/10.1016/j.procs.2016.05.385