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A Scalable Architecture for Real-Time Stream Processing of Spatiotemporal IoT Stream Data—Performance Analysis on the Example of Map Matching

Year 2018
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Scalable real-time processing of large amounts of data has become a research topic of particular importance due to the continuously rising amount of data that is generated by devices equipped with sensing components. While existing approaches allow for fault-tolerant and scalable stream processing, we present a pipeline architecture that consists of well-known open source tools to specifically integrate spatiotemporal internet of things (IoT) data streams. In a case study, we utilize the architecture to tackle the online map matching problem, a pre-processing step for trajectory mining algorithms. Given the rising amount of vehicle location data that is generated on a daily basis, existing map matching algorithms have to be implemented in a distributed manner to be executable in a stream processing framework that provides scalability. We demonstrate how to implement state-of-the-art map matching algorithms in our distributed stream processing pipeline and analyze measured latencies.

Details

ISPRS Int. J. Geo-Inf. 2018, 7(7), 238; https://doi.org/10.3390/ijgi7070238

Authors

  • Marius Laska
  • Stefan Herle
  • Ralf Klamma
  • Jörg Blankenbach

Published in

ISPRS Int. J. Geo-Inf. , volume 7 , p. 238 .

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