Comparative Survey on Big data Security Applications, A Blink on Interactive Security Mechanism in Apache Ozone

Wagan, Shiraz Ali; Junaid, Muhammad; Qureshi, Nawab Muhammad Faseeh; Shin, Dong Ryeol; Choi, Kee‐Hyun

doi:10.1109/gcwot49901.2020.9391610

Cited by 1 publication

(1 citation statement)

References 31 publications

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“…Rapid Miner is mostly used to diagnose diabetes, according to their analysis [45]. used Rapid Miner data mining technology will be used to classify massive data and analyze people' attitudes and opinions on the Facebook social networking site [47]. tested decision trees, regression, SVM, k-NN and Naive Bayes on breast cancer detection using Rapid Miner.…”

Section: Rapid Minermentioning

confidence: 99%

Performance Evaluation of Data-driven Intelligent Algorithms for Big data Ecosystem

Junaid

Ali

Siddiqui

et al. 2022

Wireless Pers Commun

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Artificial intelligence, specifically machine learning, has been applied in a variety of methods by the research group to transform several data sources into valuable facts and understanding, allowing for superior pattern identification skills. Machine learning algorithms on huge and complicated data sets, computationally expensive on the other hand, processing requires hardware and logical resources, such as space, CPU, and memory. As the amount of data created daily reaches quintillion bytes, A complex big data infrastructure becomes more and more relevant. Apache Spark Machine learning library (ML-lib) is a famous platform used for big data analysis, it includes several useful features for machine learning applications, involving regression, classification, and dimension reduction, as well as clustering and features extraction. In this contribution, we consider Apache Spark ML-lib as a computationally independent machine learning library, which is open-source, distributed, scalable, and platform. We have evaluated and compared several ML algorithms to analyze the platform’s qualities, compared Apache Spark ML-lib against Rapid Miner and Sklearn, which are two additional Big data and machine learning processing platforms. Logistic Classifier (LC), Decision Tree Classifier (DTc), Random Forest Classifier (RFC), and Gradient Boosted Tree Classifier (GBTC) are four machine learning algorithms that are compared across platforms. In addition, we have tested general regression methods such as Linear Regressor (LR), Decision Tree Regressor (DTR), Random Forest Regressor (RFR), and Gradient Boosted Tree Regressor (GBTR) on SUSY and Higgs datasets. Moreover, We have evaluated the unsupervised learning methods like K-means and Gaussian Mixer Models on the data set SUSY and Hepmass to determine the robustness of PySpark, in comparison with the classification and regression models. We used ”SUSY,” ”HIGGS,” ”BANK,” and ”HEPMASS” dataset from the UCI data repository. We also talk about recent developments in the research into Big Data machines and provide future research directions.

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Section: Rapid Minermentioning

confidence: 99%