A Hadoop solution for ballistic image analysis and recognition
Hakan Kocakulak, Tuğba Taşkaya Temizel · 2011
The importance of ballistic applications has been recently recognized due to the increasing crime and terrorism threats and incidents around the world. Ballistic image analysis is one of the application areas which requires immediate response with high precision from large databases. Here, the microscopic markings on cartridge case of a bullet obtained in a crime scene are compared with that of images on ballistic databases for similarity in order to find out whether it is fired from any of the firearms within the database. In this paper, we have implemented a MapReduce solution using Hadoop for ballistic image comparison which is a high data and computation intensive task. MapReduce, a programming model developed by Google, provides a scalable, flexible and QoS guaranteed IT infrastructure particularly for embarrassingly parallel data oriented computational tasks. Our results have shown that we can effectively utilize the computing resources and gain significant increases in performance. Furthermore, we will share our experiences in programming and tuning a Hadoop cluster in the paper.