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.
Highlights
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 integrate spatiotemporal internet of things (IoT) data streams
We simulated a subset of taxi trajectories gathered by Yuan et al [25,26] and measured the latency inside the stream processing framework using the evaluation data provided by Storm, as well as the complete period, which a trajectory point spends in the whole architecture
Summary
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. Due to the wide adoption of GNSS components in sensing devices, the data that is generated from so-called spatiotemporal data streams [1], where each event is tagged with a timestamp and a location. Generating these spatiotemporal data streams results in challenges regarding the knowledge extraction, real-time processing, scalability and data integration. The range of applications in the area of real-time processing of spatiotemporal data streams reaches from vehicle tracking applications such as fleet tracking to the abnormal event detection in sensor networks that monitor dike characteristics to prevent the bursting of a dike
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