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Big Data Processing and Performance Improvement for Ship Trajectory using MapReduce Technique

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2019, 24(10), pp.65-70
  • DOI : 10.9708/jksci.2019.24.10.065
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : August 21, 2019
  • Accepted : September 22, 2019
  • Published : October 31, 2019

Kim Kwang-il 1 Joo-sung, Kim 2

1제주대학교
2목포해양대학교

Accredited

ABSTRACT

In recently, ship trajectory data consisting of ship position, speed, course, and so on can be obtained from the Automatic Identification System device with which all ships should be equipped. These data are gathered more than 2GB every day at a crowed sea port and used for analysis of ship traffic statistic and patterns. In this study, we propose a method to process ship trajectory data efficiently with distributed computing resources using MapReduce algorithm. In data preprocessing phase, ship dynamic and static data are integrated into target dataset and filtered out ship trajectory that is not of interest. In mapping phase, we convert ship's position to Geohash code, and assign Geohash and ship MMSI to key and value. In reducing phase, key-value pairs are sorted according to the same key value and counted the ship traffic number in a grid cell. To evaluate the proposed method, we implemented it and compared it with IALA waterway risk assessment program(IWRAP) in their performance. The data processing performance improve 1 to 4 times that of the existing ship trajectory analysis program.

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