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

Laska, Marius; Herle, Stefan; Klamma, Ralf; Blankenbach, Jörg

doi:10.3390/ijgi7070238

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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

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Geodetic Institute and Chair for Computing in Civil Engineering & Geo Information Systems, RWTH Aachen University, Mies-van-der-Rohe-Str. 1, 52074 Aachen, Germany

Advanced Community Information Systems Group (ACIS), RWTH Aachen University, Lehrstuhl Informatik 5, Ahornstr. 55, 52074 Aachen, Germany

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

Received: 12 March 2018 / Revised: 7 June 2018 / Accepted: 19 June 2018 / Published: 21 June 2018

(This article belongs to the Special Issue Geospatial Applications of the Internet of Things (IoT))

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Abstract

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. View Full-Text

Keywords: stream processing; IoT; spatiotemporal; data mining; map matching

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Laska, M.; Herle, S.; Klamma, R.; Blankenbach, J. A Scalable Architecture for Real-Time Stream Processing of Spatiotemporal IoT Stream Data—Performance Analysis on the Example of Map Matching. ISPRS Int. J. Geo-Inf. 2018, 7, 238. https://doi.org/10.3390/ijgi7070238

AMA Style

Laska M, Herle S, Klamma R, Blankenbach J. A Scalable Architecture for Real-Time Stream Processing of Spatiotemporal IoT Stream Data—Performance Analysis on the Example of Map Matching. ISPRS International Journal of Geo-Information. 2018; 7(7):238. https://doi.org/10.3390/ijgi7070238

Chicago/Turabian Style

Laska, Marius, Stefan Herle, Ralf Klamma, and Jörg Blankenbach. 2018. "A Scalable Architecture for Real-Time Stream Processing of Spatiotemporal IoT Stream Data—Performance Analysis on the Example of Map Matching" ISPRS International Journal of Geo-Information 7, no. 7: 238. https://doi.org/10.3390/ijgi7070238

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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

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