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Top1. Introduction
Data mining is a technology that is utilized for extracting effective, inherent, theoretic valuable, and understandable data from the massive dataset using emergent computing technologies (Karim, et al., 2018; Ramírez-Gallego, et al., 2018). Extreme learning machine (ELM) is adopted when the dataset increases and becomes complex. Moreover, ELM recognizes the capability to be quick and operative (Duan, et al., 2018). Big data is essential for triumphant data mining to have measurable and effective solutions that should be easily reached to various types of skilled experts (Koliopoulos, et al., 2015). Data mining is a technique for dealing with huge data to improve decision making (Ramírez-Gallego, et al., 2017). Using conventional methods, it is inconvenient to collect datasets with more accuracy and processability. Thus, big data is extensively used for processing massive datasets which provides an easier understanding that makes it possible for organizations to acquire insights from previous data. Various journals have been launched by the academic community on big data. The journals, namely Nature” and “Science” addressed the big data obstacles which involve “Big data” and “Deal with data” that helped to solve various obstacles. Internet expertise, economics, supercomputing, medicines (Sailee Bhambere, 2017; Sailee D Bhambere 2017), etc are some of the issues in big data technology (Lin, et al., 2017). An exceptionally large quantity of processed data is accumulated by current systems so research on big data has become widespread, as an enormous amount of data is needed for performing research activities (Ramírez-Gallego, et al., 2017). Processing data is simple with small data, but if there is a rise in data size performance may go down. The database software is incapable to process data that differ in size, quantity, variety, accuracy. Moreover, typical database software cannot incarcerate and pile. Such software does not have the power to influence or to form an idea (Ramírez-Gallego, et al., 2018).
Parallel programming models have become famous nowadays which invokes interest amongst researchers to devise novel machine learning algorithms. Usually, big data deals with examining a large quantity of data from the geographically distributed region using Machine learning algorithms (Lv, et al., 2018). The machine-learning algorithm has become a prominent examining tool for multinational companies and governments. Machine learning techniques interpret difficult and complex data sets to make effective decisions after detailed and scholarly analysis to attain high performance. Anticipated performances have a direct connection with the model features on basis of a parallel programming framework (Sheshasaayee, & Lakshmi, 2017). In the conventional technique, data is collected from all over the world to central data center location which is then processed by data-driven parallel application (Hernández, et al., 2018). Thus, a better tool is still needed for processing a huge amount of data. Apache Spark is one of the big data examining tools for security analysis (Lighari, & Hussain, 2017). MapReduce and Spark are the major platforms that are largely used and support Genetic algorithms and particle swarm optimization (Elsebakhi, et al., 2015; Ditzler, et al., 2017; Ekanayake, et al., 2016). MapReduce is a parallel programming model that is used to achieve maximum productivity in the form of processing big datasets. The new models of MapReduce are highly measurable. For parallel processing, MapReduce utilizes Hadoop distributed file system. However, the increase in development and technology led to various new platforms like Kafka, Spark, and Flume, etc. Due to the shortcoming of MapReduce, many new technologies took place of it (Sheshasaayee, & Lakshmi, 2017). Apache Spark is one of the fast cluster computing engines that give accurate measurability and fault detecting features as compared to MapReduce (Hadgu, et al., 2015).