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SPEAR: Dynamic Spatio-Temporal Query Processing over High Velocity Data Streams.

Furqan Baig1, Dejun Teng1, Jun Kong2

  • 1Dept. of Computer Science, Stony Brook University.

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|May 16, 2022
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Summary
This summary is machine-generated.

This study introduces a novel approach for dynamic spatio-temporal query processing over high-velocity big data streams. It enhances system responsiveness by enabling real-time query adjustments without restarts, crucial for IoT and 5G data.

Keywords:
distributed-streamreal-time-spatio-temporalspatial-streamspatio-temporalspatio-temporal-streamstream-processing

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Area of Science:

  • Data Science
  • Computer Science
  • Geospatial Analysis

Background:

  • The Internet of Things (IoT) and 5G generate massive real-time spatio-temporal data streams.
  • Existing systems struggle with dynamic query adjustments, leading to system unresponsiveness and outdated results.
  • Optimization is needed for objects with changing motion states in distributed spatio-temporal data management.

Purpose of the Study:

  • To propose a dynamic spatio-temporal query processing strategy for high-velocity big data streams.
  • To enhance the responsiveness and accuracy of spatio-temporal data stream management systems.
  • To address the limitations of the specified-once-applied-continuously query model.

Main Methods:

  • Adaptation of principles from streaming databases, spatial data management, and distributed computing.
  • Formulation of spatio-temporal data types and functions for dynamic query state handling.
  • Development of streaming spatio-temporal querying methods and geohash-based dynamic spatial partitioning.
  • Implementation of a prototype on Apache Flink for in-memory stream processing.

Main Results:

  • The proposed strategy effectively handles dynamic spatio-temporal query processing.
  • Maintained high and consistent processing rates for both stationary and moving queries.
  • Demonstrated effectiveness over high-velocity spatio-temporal big data streams.

Conclusions:

  • The developed approach significantly improves real-time analysis of dynamic spatio-temporal data.
  • The geohash-based partitioning enables effective parallel processing for big data streams.
  • This work provides a robust solution for modern data stream management challenges.