Review of Parallel Processing Methods for Big Image Data Applications

  • K. Vigneshwari
  • K. Kalaiselvi
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 637)


The coexistence of technologies, like big data application, cloud computing, and the numerous images in the Web has paved the need for new image processing algorithms that exploit the processed image for diverse applications. There arises a need for new image processing algorithms to utilize the processed image for diverse applications though many techniques with variations exist. Ultimately, the enduring issue is to enhance the effectiveness of huge image processing and to maintain the combination of the same with recent works. This paper presents a review of the newest progress in researches on parallel processing methods for the processing of big data. Initially, the reviews about the parallel processing methods were carried out by highlighting some promising parallel processing methods in recent studies, such as the representation of MapReduce (MR) framework, distributed, parallel methods, and Hadoop framework. Subsequently, focus on analysis and deliberations about the challenges and promising solutions of parallel computing methods on big data in various applications and on image processing applications were made and concluded with a summary of number of open problems and research areas.


Big data Image processing MapReduce (MR) framework Hadoop Parallel processing methods Distributed system and applications 


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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • K. Vigneshwari
    • 1
  • K. Kalaiselvi
    • 2
  1. 1.Department of Computer ScienceVELS Institute of Science, Technology and Advanced StudiesChennaiIndia
  2. 2.Department of Computer Science, School of Computing SciencesVels Institute of Science Technology and Advanced Studies (VISTAS), Formerly Vels UniversityChennaiIndia

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