TELKOMNIKA Telecommunication Computing Electronics and Control
Big data classification based on improved parallel k-nearest neighbor

TELKOMNIKA Telecommunication Computing Electronics and Control
Big data classification based on improved parallel k-nearest neighbor

Big data
K-nearest neighbor
Machine learning
Parallel processing
Radoop
Spark

In response to the rapid growth of many sorts of information, highway data
has continued to evolve in the direction of big data in terms of scale, type,
and structure, exhibiting characteristics of multi-source heterogeneous data.
The k-nearest neighbor (KNN) join has received a lot of interest in recent years
due to its wide range of applications. Processing KNN joins is time-consuming
and inefficient due to the quadratic structure of the join method. As the number
of applications dealing with vast amounts of data develops, KNN joins get
more sophisticated. The authors seek to save money on computer resources
by leveraging a large number of threads and multiprocessors. Six popular
datasets are used to apply the method and evaluate the sequential and
parallel performance of the KNN technique. These datasets are used to
compare the sequential and parallel performance of the KNN method. When
compared to a matching multi-core solution, the final implementation saves
computing resources. It has been optimized to utilize as little RAM as
possible, allowing it to manage high-resolution photo data without
sacrificing efficiency. The authors will use the technique they presented
using Spark Radoop. Our performance research validates the supplied
method’s efficacy and scalability.

Ahmed Hussein Ali, Mostafa Abduhgafoor Mohammed, Raed Abdulkareem Hasan, Maan Nawaf Abbod , Mohammed Sh. Ahmed , Tole Sutikno

http://telkomnika.uad.ac.id

Nov 05, 2022

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