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Migration of a Vehicle Tracking System Running on Relational Database to Big Data Environment

Year 2024, Volume: 19 Issue: 1, 161 - 168, 28.03.2024
https://doi.org/10.55525/tjst.1364046

Abstract

Building a high-performance and scalable system has always been a challenge in tracking systems. At the root of this problem lies the excessive and real-time data overload. This paper aims to replace traditional approaches with big data approaches. In this study, a new big data ecosystem design for vehicle tracking system architecture is presented. The aim is to process real-time and extremely fast-generated location/tracking data very fast and increase the overall system performance. The process speed performance of the newly developed big data ecosystem is compared with the table query speed performance of a relational database. As a result of the comparison, the query speed of the big data ecosystem was found to be much faster than that of the relational database management system.

References

  • Giusto D, Iera A, Morabito G, Atzori L. (Eds.). The internet of things: 20th Tyrrhenian workshop on digital communications. New York, USA: Springer Science & Business Media, 2010.
  • Goes PB. Design science research in top information systems journals. MIS Q.: Manag. Inf. Syst. 2014; 38(1): iii-viii.
  • Cox, M., & Ellsworth, D. Application-controlled demand paging for out-of-core visualization. In: Proceedings. Visualization'97 (Cat. No. 97CB36155); 1997 October; Phoenix, AZ, U.S.A. New York, USA: IEEE. pp. 235-244.
  • Koca B, Ceylan A. Uydu konum belirleme sistemlerindeki (GNSS) güncel durum ve son gelişmeler [Current Status and Recent Developments in Global Positioning Satellite Systems (GNSS)]. Geomatik 2018; 3(1); 63-73.
  • Eger Ö. “Big Data’nın (Büyük Veri) Endüstriyel Kullanımı”. Türkiyenin endüstri 4.0 platformu. 2017. https://www.endustri40.com/big-datanin-buyuk-veri-endustriyel-kullanimi (accessed March 28, 2018).
  • Kumar, M. Sandeep. "Comparison of NoSQL database and traditional database-an emphatic analysis." JOIV: International Journal on Informatics Visualization 2.2 (2018): 51-55.
  • Aydemir F, Cetin A. Designing a Pipeline with Big Data Technologies for Border Security. Mugla Journal of Science and Technology 2016; 2(1): 98-101.
  • Aydemir F. Sınır Güvenliği İçin Büyük Veri Teknik ve Teknolojileri ile Boru Hattı Tasarımı [Designing a pipeline with big data Techniques and technologies for border security] (Unpublished Master Thesis). Gazi University, Ankara, Turkey, 2012.
  • Apache Active MQ, 2015. https://activemq.apache.org (accessed October 28,2018).
  • Apache Spark. Apache Spark-Unified Analytics Enginefor Big Data. Apache software foundation. 2018. https://spark.apache.org/ (accessed November 11, 2018).
  • Javed Awan M, Mohd Rahim MS, Nobanee H, Yasin A, Khalaf OI. A big data approach to black friday sales. Intell. Autom. Soft Comput. 2021; 27(3): 785-797.
  • Linux foundation. Node.js. 2018. https://nodejs.org (accessed March 29, 2018).
  • Öztürk S, Atmaca HE. İlişkisel ve ilişkisel olmayan (NoSQL) veri tabanı sistemleri mimari performansının yönetim bilişim sistemleri kapsamında incelenmesi. [The examination of relationaland non-relational (NoSQL) database system's architectural performances in terms of management of information systems]. Bilişim Teknolojileri Dergisi [Journal of Information Technologies] 2017; 10(2); 199-209.
  • Aktan E. Büyük veri: Uygulama alanları, analitiği ve güvenlik boyutu [Big data: Application areas, analytics and security dimension]. Ankara Üniversitesi Bilgi Yönetimi Dergisi [Ankara University Journal of Information Management] 2018; 1(1): 1-22.
  • Apache Cassandra. “Apache Cassandra Unified Analytics Engine for Big Data”. Apache software foundation. 2016. http://cassandra.apache.org/ (accessed November 29, 2018).

İlişkisel Veri Tabanında Çalışan Araç Takip Sisteminin Büyük Veri Ortamına Taşınması

Year 2024, Volume: 19 Issue: 1, 161 - 168, 28.03.2024
https://doi.org/10.55525/tjst.1364046

Abstract

Yüksek performanslı ve ölçeklenebilir bir sistem oluşturmak, takip sistemlerinde her zaman bir sorun olmuştur. Bu sorunun temelinde aşırı ve gerçek zamanlı veri yoğunluğu yatmaktadır. Bu makale geleneksel yaklaşımların yerine büyük veri yaklaşımlarını uygulamayı amaçlamaktadır. Bu çalışmada araç takip sistemi mimarisi için geleneksel yöntemlerin dışına çıkarak yeni bir büyük veri ekosistem tasarım ortaya koyulmuştur. Bunu yapmadaki amaç; gerçek zamanlı ve aşırı hızlı üretilen konum/takip verilerinin çok hızlı işlenmesi ve genel sistem performansının arttırılmasıdır. Yeni geliştirilen büyük veri ekosisteminin hız performansı, ilişkisel veri tabanın tablo sorgulama hız performansıyla karşılaştırılmıştır. Karşılaştırma sonucunda büyük veri ekosisteminin sorgulama hızının ilişkisel veri tabanı yönetim sistemine kıyasla çok daha hızlı olduğu görülmüştür.

References

  • Giusto D, Iera A, Morabito G, Atzori L. (Eds.). The internet of things: 20th Tyrrhenian workshop on digital communications. New York, USA: Springer Science & Business Media, 2010.
  • Goes PB. Design science research in top information systems journals. MIS Q.: Manag. Inf. Syst. 2014; 38(1): iii-viii.
  • Cox, M., & Ellsworth, D. Application-controlled demand paging for out-of-core visualization. In: Proceedings. Visualization'97 (Cat. No. 97CB36155); 1997 October; Phoenix, AZ, U.S.A. New York, USA: IEEE. pp. 235-244.
  • Koca B, Ceylan A. Uydu konum belirleme sistemlerindeki (GNSS) güncel durum ve son gelişmeler [Current Status and Recent Developments in Global Positioning Satellite Systems (GNSS)]. Geomatik 2018; 3(1); 63-73.
  • Eger Ö. “Big Data’nın (Büyük Veri) Endüstriyel Kullanımı”. Türkiyenin endüstri 4.0 platformu. 2017. https://www.endustri40.com/big-datanin-buyuk-veri-endustriyel-kullanimi (accessed March 28, 2018).
  • Kumar, M. Sandeep. "Comparison of NoSQL database and traditional database-an emphatic analysis." JOIV: International Journal on Informatics Visualization 2.2 (2018): 51-55.
  • Aydemir F, Cetin A. Designing a Pipeline with Big Data Technologies for Border Security. Mugla Journal of Science and Technology 2016; 2(1): 98-101.
  • Aydemir F. Sınır Güvenliği İçin Büyük Veri Teknik ve Teknolojileri ile Boru Hattı Tasarımı [Designing a pipeline with big data Techniques and technologies for border security] (Unpublished Master Thesis). Gazi University, Ankara, Turkey, 2012.
  • Apache Active MQ, 2015. https://activemq.apache.org (accessed October 28,2018).
  • Apache Spark. Apache Spark-Unified Analytics Enginefor Big Data. Apache software foundation. 2018. https://spark.apache.org/ (accessed November 11, 2018).
  • Javed Awan M, Mohd Rahim MS, Nobanee H, Yasin A, Khalaf OI. A big data approach to black friday sales. Intell. Autom. Soft Comput. 2021; 27(3): 785-797.
  • Linux foundation. Node.js. 2018. https://nodejs.org (accessed March 29, 2018).
  • Öztürk S, Atmaca HE. İlişkisel ve ilişkisel olmayan (NoSQL) veri tabanı sistemleri mimari performansının yönetim bilişim sistemleri kapsamında incelenmesi. [The examination of relationaland non-relational (NoSQL) database system's architectural performances in terms of management of information systems]. Bilişim Teknolojileri Dergisi [Journal of Information Technologies] 2017; 10(2); 199-209.
  • Aktan E. Büyük veri: Uygulama alanları, analitiği ve güvenlik boyutu [Big data: Application areas, analytics and security dimension]. Ankara Üniversitesi Bilgi Yönetimi Dergisi [Ankara University Journal of Information Management] 2018; 1(1): 1-22.
  • Apache Cassandra. “Apache Cassandra Unified Analytics Engine for Big Data”. Apache software foundation. 2016. http://cassandra.apache.org/ (accessed November 29, 2018).
There are 15 citations in total.

Details

Primary Language English
Subjects Big Data
Journal Section TJST
Authors

Ferhat Koçer 0000-0002-5313-7903

Selim Bayraklı 0000-0003-3115-6721

Publication Date March 28, 2024
Submission Date September 21, 2023
Published in Issue Year 2024 Volume: 19 Issue: 1

Cite

APA Koçer, F., & Bayraklı, S. (2024). Migration of a Vehicle Tracking System Running on Relational Database to Big Data Environment. Turkish Journal of Science and Technology, 19(1), 161-168. https://doi.org/10.55525/tjst.1364046
AMA Koçer F, Bayraklı S. Migration of a Vehicle Tracking System Running on Relational Database to Big Data Environment. TJST. March 2024;19(1):161-168. doi:10.55525/tjst.1364046
Chicago Koçer, Ferhat, and Selim Bayraklı. “Migration of a Vehicle Tracking System Running on Relational Database to Big Data Environment”. Turkish Journal of Science and Technology 19, no. 1 (March 2024): 161-68. https://doi.org/10.55525/tjst.1364046.
EndNote Koçer F, Bayraklı S (March 1, 2024) Migration of a Vehicle Tracking System Running on Relational Database to Big Data Environment. Turkish Journal of Science and Technology 19 1 161–168.
IEEE F. Koçer and S. Bayraklı, “Migration of a Vehicle Tracking System Running on Relational Database to Big Data Environment”, TJST, vol. 19, no. 1, pp. 161–168, 2024, doi: 10.55525/tjst.1364046.
ISNAD Koçer, Ferhat - Bayraklı, Selim. “Migration of a Vehicle Tracking System Running on Relational Database to Big Data Environment”. Turkish Journal of Science and Technology 19/1 (March 2024), 161-168. https://doi.org/10.55525/tjst.1364046.
JAMA Koçer F, Bayraklı S. Migration of a Vehicle Tracking System Running on Relational Database to Big Data Environment. TJST. 2024;19:161–168.
MLA Koçer, Ferhat and Selim Bayraklı. “Migration of a Vehicle Tracking System Running on Relational Database to Big Data Environment”. Turkish Journal of Science and Technology, vol. 19, no. 1, 2024, pp. 161-8, doi:10.55525/tjst.1364046.
Vancouver Koçer F, Bayraklı S. Migration of a Vehicle Tracking System Running on Relational Database to Big Data Environment. TJST. 2024;19(1):161-8.