A survey of open source tools for machine learning with big data in the Hadoop ecosystem

Landset, Sara; Khoshgoftaar, Taghi M.; Richter, Aaron; Hasanin, Tawfiq

doi:10.1186/s40537-015-0032-1

Cited by 371 publications

(197 citation statements)

References 66 publications

(61 reference statements)

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“…Velocity can be considered as the speed at which data are transmitted and analyzed [83]. The data is now continuously streaming into the servers in real-time and makes the original batch process break down [84].…”

Section: Properties Of Mobile Big Datamentioning

confidence: 99%

A Survey on Machine Learning-Based Mobile Big Data Analysis: Challenges and Applications

Xie

Song

et al. 2018

Wireless Communications and Mobile Computing

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This paper attempts to identify the requirement and the development of machine learning-based mobile big data (MBD) analysis through discussing the insights of challenges in the mobile big data. Furthermore, it reviews the state-of-the-art applications of data analysis in the area of MBD. Firstly, we introduce the development of MBD. Secondly, the frequently applied data analysis methods are reviewed. Three typical applications of MBD analysis, namely, wireless channel modeling, human online and offline behavior analysis, and speech recognition in the Internet of Vehicles, are introduced, respectively. Finally, we summarize the main challenges and future development directions of mobile big data analysis.

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Section: Properties Of Mobile Big Datamentioning

confidence: 99%

A Survey on Machine Learning-Based Mobile Big Data Analysis: Challenges and Applications

Xie

Song

et al. 2018

Wireless Communications and Mobile Computing

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“…Hue [49] is a web application for interacting with Hadoop and its ecosystem. Hue [50] is friendly with any edition of Hadoop and is existing in all of the most important Hadoop distributions. …”

Section: Data Analyticsmentioning

confidence: 99%

Parallel processing on Big Data in the context of Machine Learning and Hadoop Ecosystem: A Survey

Brahmane¹,

R²

2018

IJET

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Emergent Big Data applications have become gradually more essential. In reality, a lot of institutes, businesses and in general entire society from diverse segments depend more and more on information take out from enormous quantity of raw information, statistics and numbers. On the other hand, in Big Data perspective, customary information methods and policies are not as much of capable. They prove a time-consuming receptiveness and are short of quantifiability, measurability, presentation and accurateness. To solve the composite Big Data constraints and difficulties, a large amount effort has been carried out. As an effect, different categories of packages, distributions and technologies have been developed. In this paper an evaluation is done, this studies recent technologies developed for Big Data. It aims to assist to choose and adopt the exact combination of diverse Big Data technologies according to their technological, scientific needs and particular applications requirements. It provides not only a worldwide sight of most important Big Data technologies but also relationship according to special organizational, classifications levels

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“…GDP, Average base fair, Population, and Distance. [8] has straightforward hyper-parameter tuning and is also fast, having Java back-end and option to run on some number of clusters k where k represents the number of cores on the processor [9]. After performing hyperparameter optimization using grid search [10], [11], the best neural network topology is tuned as Algorithm 2. fold-Assignment = 'Module' 8: l1 = 5.6e-05 9: l2 = 7.4e-05 10:…”

Section: A Dataset and Parameter Settingmentioning

confidence: 99%

Optimizing a Deep Learning Model in Order to Have a Robust Neural Network Topology

Khan¹,

Kumar²

2018

IJMO

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Abstract-In this study, a method based on different feature engineering / feature extraction / feature derivation is proposed for improving air passenger forecasting by machine learning existing libraries. In this kind of formulation, we kept focus on creating different kinds of datasets that differ one from another by methodology so we extracted new features and compared new feature space with original feature space in terms of variable importance. We conducted experiments to improve the variance by aggregating all the features in final feature space. Finally, we optimized a deep learning model to have a Robust Neural Network Topology.Index Terms-Deep learning, logistic regression, feature extraction, neural network.

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A survey of open source tools for machine learning with big data in the Hadoop ecosystem

Cited by 371 publications

References 66 publications

A Survey on Machine Learning-Based Mobile Big Data Analysis: Challenges and Applications

A Survey on Machine Learning-Based Mobile Big Data Analysis: Challenges and Applications

Parallel processing on Big Data in the context of Machine Learning and Hadoop Ecosystem: A Survey

Optimizing a Deep Learning Model in Order to Have a Robust Neural Network Topology

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