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SSTRESED: Scalable Semantic Trajectory Extraction for Simple Event Detection over Streaming Movement Data (Extended Abstract)

Author Nikos Giatrakos



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Nikos Giatrakos
  • Technical University of Crete, Chania, Greece
  • Athena RC, Marousi, Greece

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Nikos Giatrakos. SSTRESED: Scalable Semantic Trajectory Extraction for Simple Event Detection over Streaming Movement Data (Extended Abstract). In 30th International Symposium on Temporal Representation and Reasoning (TIME 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 278, pp. 15:1-15:4, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.TIME.2023.15

Abstract

We describe SSTRESED, a prototype focused on the real-time, online detection of simple, durative events over streaming movement data. It is the first prototype that establishes a direct connection between semantic trajectory extraction and simple event detection. SSTRESED is highly scalable by incorporating parallel processing in two separate, but connected, training and event detection pipelines implemented on state-of-the-art platforms, directly deployable in cloud environments.

Subject Classification

ACM Subject Classification
  • Computer systems organization → Architectures
Keywords
  • Semantic Trajectory
  • Event Processing
  • Data Streams

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References

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