Enhancing Map-Reduce Mechanism for Big Data with Density-Based Clustering

Provided by: The International Journal of Innovative Research in Computer and Communication Engineering
Topic: Data Management
Format: PDF
Map-Reduce is software framework that allows certain type of parallelizable or distributable problems involving bulky data sets to be solve using computing clusters. This paper presents a hybrid Map-Reduce framework that gathers computations resources from different clusters and runs Map-Reduce jobs across them. The mechanism is realized using DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithms among Map-Reduce, parallel processing framework over clusters. However, the instant accomplishment of algorithms undergoes from efficiency problem for higher execution time as well as inadequate memory.

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