<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mezzoudj, Saliha</style></author><author><style face="normal" font="default" size="100%">Behloul, Ali</style></author><author><style face="normal" font="default" size="100%">Seghir, Rachid</style></author><author><style face="normal" font="default" size="100%">Saadna, Yassmina</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A parallel content-based image retrieval system using spark and tachyon frameworks</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of King Saud University - Computer and Information SciencesJournal of King Saud University - Computer and Information Sciences</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">With the huge increase of large-scale multimedia over Internet, especially images, building Content-Based Image Retrieval (CBIR) systems for large-scale images has become a big challenge. One of the drawbacks associated with CBIR is the very long execution time. In this article, we propose a fast Content-Based Image Retrieval system using Spark (CBIR-S) targeting large-scale images. Our system is composed of two steps.&amp;nbsp;(i) image indexation step, in which we use&amp;nbsp;MapReduce&amp;nbsp;distributed model on Spark in order to speed up the indexation process. We also use a memory-centric&amp;nbsp;distributed storage system, called Tachyon, to enhance the write operation&amp;nbsp;(ii) image retrieving step&amp;nbsp;which we speed up by using a parallel k-Nearest Neighbors (k-NN) search method based on&amp;nbsp;MapReduce model&amp;nbsp;implemented under&amp;nbsp;Apache Spark, in addition to exploiting the cache method of spark framework. We have showed, through a wide set of experiments, the effectiveness of our approach in terms of processing time.</style></abstract></record></records></xml>