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LocationSpark: In-memory Distributed Spatial Query Processing and Optimization.

Mingjie Tang1, Yongyang Yu2, Ahmed R Mahmood3

  • 1Chinese Academy of Science, Beijing, China.

Frontiers in Big Data
|March 11, 2021
PubMed
Summary
This summary is machine-generated.

LocationSpark enhances distributed spatial query processing using novel techniques to minimize communication costs and handle query skew. This system improves performance by up to tenfold compared to existing spatial data solutions.

Keywords:
in-memory computationparallel computingquery optimizationquery processingspatial data

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Area of Science:

  • Computer Science
  • Data Science
  • Geographic Information Systems

Background:

  • Spatial data applications generate vast amounts of data, necessitating scalable query processing.
  • Existing systems struggle with query skew and high communication costs in distributed environments.

Purpose of the Study:

  • To develop new techniques for scalable spatial query processing and optimization in distributed systems.
  • To address challenges of query skew and minimize communication overhead.

Main Methods:

  • Proposed a distributed query scheduler with a new cost model to minimize spatial query processing costs.
  • Introduced spatial indexing techniques using bitmap filters for efficient query forwarding.
  • Developed local query optimization strategies based on node-specific indexes and query characteristics.
  • Prototyped the system, named LocationSpark, within the Apache Spark distributed computing framework.

Main Results:

  • LocationSpark effectively handles query skew, a common issue in spatial data processing.
  • The system significantly minimizes communication costs in distributed spatial query execution.
  • Experimental studies show LocationSpark enhances distributed spatial query processing performance by up to an order of magnitude.

Conclusions:

  • LocationSpark offers a significant advancement in distributed spatial query processing scalability and efficiency.
  • The proposed techniques provide a robust solution for handling large-scale spatial datasets in distributed environments.