Using Apache Spark To Speed Analysis Of Ads B Aircraft Tracking Data Techniques

Abstract

The U.S. Navy is exploring the feasibility of using a big-data platform and machine-learning algorithms to analyze combat-identification data. Combat identification involves a large number of remote sensors that report back data for aggregation and analysis. In this thesis, we used a sample of ADS-B aircraft-tracking data to test big-data methods for machine-learning methods developed previously. We showed large speed improvements in the analysis setup over the previous single-processor methods, and a lesser speed improvement for machine-learning based anomaly analysis.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2018
Accession Number
AD1060123

Entities

People

  • Jim Z. Zhou

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • Engineered Resilient Systems
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Aircrafts
  • Big Data
  • California
  • Change Detection
  • Computer Programming
  • Computer Science
  • Computers
  • Data Mining
  • Data Science
  • Databases
  • Detectors
  • Domain Specific Programming Languages
  • High Performance Computing
  • Identification
  • Identification Systems
  • Information Science
  • Machine Learning
  • Network Science
  • Operating Systems
  • Programming Languages
  • Python Programming Language
  • Sense And Avoid Systems

Fields of Study

  • Computer science

Readers

  • Aviation Science / Aeronautics.
  • Computational Modeling and Simulation
  • Distributed Systems and Data Platform Development

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks