Data Mining with Parallel Processing Technique for Complexity Reduction and Characterization of Big Data

Author's Name: J.Josepha Menandas and J.Jakkulin Joshi
Subject Area: Science and Engineering
Subject Computer Science
Section Research Paper


Big data, complexity, data mining, heterogeneity, autonomous sources, volume, velocity, variety, variability.


Big data concern extremely large data sets with multiple that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions including physical, biological and biomedical sciences. Big data describes the exponential growth and availability of data both structured and unstructured, includes large-volume, complex, autonomous sources. With the fast development of networking, data storage and the capacity of data collection, Big data are now rapidly expanding in all science and engineering domain. This paper presents a PARALLEL PROCESSING TECHNIQUE (PPT) that characterizes the features of big data revolution, reduces complexity, and proposes a big data processing model from the data mining perspective. This model involves data accessing and computing, data privacy and domain knowledge, and big data mining algorithms, and also the big data revolution.

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