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Details

Autor(en) / Beteiligte
Titel
A Scalable Architecture for Real-Time Stream Processing of Spatiotemporal IoT Stream Data—Performance Analysis on the Example of Map Matching
Ist Teil von
  • ISPRS international journal of geo-information, 2018-07, Vol.7 (7), p.238
Ort / Verlag
Basel: MDPI AG
Erscheinungsjahr
2018
Link zum Volltext
Quelle
EZB Free E-Journals
Beschreibungen/Notizen
  • 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.
Sprache
Englisch
Identifikatoren
ISSN: 2220-9964
eISSN: 2220-9964
DOI: 10.3390/ijgi7070238
Titel-ID: cdi_doaj_primary_oai_doaj_org_article_6ee2480f0b5e4cc3894485e3ce3b7638

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