An overview on cloud computing platform spark for Human Genome mining

Ding, Dongliang; Wu, Di

doi:10.1109/icma.2016.7558977

Cited by 8 publications

(3 citation statements)

References 20 publications

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“…Adopting products and best practices that emerge from an ever-growing Information and Communication Technologies (ICT) industry has proven to be a cost and performance effective path to serve the community. Large-scale science projects used to be a driver of IT infrastructure developments [419][420][421][422][423][424]. A future particle collider research facility can again be a case and a driving force for advanced ICT developments in areas that go beyond the hardware and software domains, which dominated the particle accelerator projects of the 1980s and 90s.…”

Section: Communications Computing and Data Servicesmentioning

confidence: 99%

FCC-ee: The Lepton Collider

Abada¹,

Abbrescia²,

AbdusSalam³

et al. 2019

Eur. Phys. J. Spec. Top.

650

511

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Section: Communications Computing and Data Servicesmentioning

confidence: 99%

FCC-ee: The Lepton Collider

Abada¹,

Abbrescia²,

AbdusSalam³

et al. 2019

Eur. Phys. J. Spec. Top.

650

511

View full text Add to dashboard Cite

show abstract

“…This improves the advancement of in-memory programming approaches and top-level data processing frameworks [13,14]. PySpart frameworks have demonstrated successfully in stochastic modeling [15], healthcare informative [16], text mining [17], genomics data analysis [18,19], etc.…”

Section: Introductionmentioning

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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“…It has been worked on plenty of variation on this methodology including correlation and vector-based similarity calculations, user and item based recommendation approaches, and selective down-sampling of user interactions. In the end, it has taken an extensive research on comparison of these methods on the Movie Lens dataset consisting of 10 million movie ratings [18,19].…”

Section: Related Workmentioning

confidence: 99%

An Improved Hybrid Distributed Collaborative Filtering Model for Recommender Engine using Apache Spark

Lenka¹,

Barik²,

Panigrahi³

et al. 2018

IJISA

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The present scenario there is a serious need of scalability for efficient analytics of big data. In order to achieve this, technology like MapReduce, Pig and HIVE came into action but when the question comes to scalability; Apache Spark maintains a great position far ahead. In this research paper, it has designed and developed an improved hybrid distributed collaborative model for filtering recommender engine. Execution time, scalability and robustness of the engine are the three evaluation parameters; has been considered for this present study. The present work keeps an eye on recommender system built with help of Apache Spark. Apart from this, it has been proposed and implemented the bisecting KMeans clustering algorithms. It has discussed about the comparative analysis between KMeans and Bisecting KMeans clustering algorithms on Apache Spark environment.

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An overview on cloud computing platform spark for Human Genome mining

Cited by 8 publications

References 20 publications

FCC-ee: The Lepton Collider

FCC-ee: The Lepton Collider

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

An Improved Hybrid Distributed Collaborative Filtering Model for Recommender Engine using Apache Spark

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