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A Unified Approach to Spatial Proximity Query Processing in Dynamic Spatial Networks.

Hyung-Ju Cho1

  • 1Department of Software, Kyungpook National University, 2559 Gyeongsang-daero, Sangju-si 37224, Gyeongsangbuk-do, Korea.

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|August 28, 2021
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Summary

This study introduces a unified batch algorithm (UBA) to efficiently process spatial proximity (SP) queries in dynamic networks. UBA clusters queries to reduce redundant calculations, outperforming sequential methods.

Keywords:
dynamic spatial networknearest neighbor queryrange queryspatial proximity queryunified batch algorithm

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Area of Science:

  • Computer Science
  • Database Systems
  • Spatial Computing

Background:

  • Nearest Neighbor (NN) and Range (RN) queries, collectively termed spatial proximity (SP) queries, are fundamental in spatial databases.
  • Location-based services (LBS) face challenges in simultaneously processing high volumes of SP queries during peak times.
  • Current sequential query evaluation methods lead to inefficient, repeated distance calculations.

Purpose of the Study:

  • To develop an efficient algorithm for processing spatial proximity queries in dynamic spatial networks.
  • To reduce computational overhead by minimizing redundant distance calculations for spatially clustered queries.
  • To enhance the performance and scalability of location-based services.

Main Methods:

  • Proposes a Unified Batch Algorithm (UBA) designed for dynamic spatial networks.
  • Defines spatial proximity query distance based on shortest path travel time, accounting for traffic variations.
  • Clusters nearby SP queries to enable shared distance computations within query clusters.

Main Results:

  • Demonstrates significant performance improvements of UBA over state-of-the-art sequential solutions.
  • Exhibits superior scalability of UBA when evaluated on real-world road network datasets.
  • Confirms the effectiveness of UBA in avoiding unnecessary distance calculations for clustered queries.

Conclusions:

  • The proposed UBA offers a more efficient and scalable approach to processing spatial proximity queries in dynamic environments.
  • UBA's clustering strategy effectively reduces computational costs by exploiting shared calculations.
  • This algorithm presents a valuable advancement for location-based services requiring real-time spatial query processing.