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Parallelized Heat Map Algorithm Using Multiple Cores

Publication Type : Conference Paper

Publisher : ICDSMLA

Source : International Conference on Data Science, Machine Learning & Applications (ICDSMLA 2019), CMR Institute of Technology, Hyderabad, India (2020)

Url : https://link.springer.com/chapter/10.1007/978-981-15-1420-3_64

Campus : Bengaluru

School : Department of Computer Science and Engineering, School of Engineering

Department : Computer Science

Year : 2020

Abstract : MapReduce simplifies the programming for large-scale data-parallel applications and greatly reduces the development effort by sparing the programmer from complex issues such as parallel execution, fault tolerance, data management, task scheduling, etc. A heat map is generally used to provide the visual summary of information using a two-dimensional representation of data in which values are represented by colours. More elaborate heat maps allow the viewer to understand complex data sets in an easily viewable form. This paper discusses the advantages and uses of parallelized heat map algorithm which is mainly used in eye tracking data. Parallelized version of heat map has also been proved to be efficient when compared with the serial version.

Cite this Research Publication : L. Kolasani and Dr. Supriya M., “Parallelized Heat Map Algorithm Using Multiple Cores”, in International Conference on Data Science, Machine Learning & Applications (ICDSMLA 2019), CMR Institute of Technology, Hyderabad, India , 2020.

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