An Accurate and Efficient Scheduler for Hadoop MapReduce Framework

Vinutha, D C; Raju, G. T.

doi:10.11591/ijeecs.v12.i3.pp1132-1142

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…Hadoop keeps multiple copies of the same data as it tries to overcome the hardware failure, and the data is never lost. Hadoop performs the processes in a fault-tolerant manner and it is very much reliable [8,9].…”

Section: Hadoopmentioning

confidence: 99%

Twitter data analysis using hadoop ecosystems and apache zeppelin

Wilson

Sivakumar

2019

IJEECS

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The day-to-day life of the people doesn't depend only on what they think, but it is affected and influenced by what others think. The advertisements and campaigns of the favourite celebrities and mesmerizing personalities influence the way people think and see the world. People get the news and information at lightning speed than ever before. The growth of textual data on the internet is very fast. People express themselves in various ways on the web every minute. They make use of various platforms to share their views and opinions. A huge amount of data is being generated at every moment on this process. Being one of the most important and well-known social media of the present time, millions of tweets are posted on Twitter every day. These tweets are a source of very important information and it can be made use for business, small industries, creating government policies, and various studies can be performed by using it. This paper focuses on the location from where the tweets are posted and the language in which the tweets are written. These details can be effectively analysed by using Hadoop. Hadoop is a tool that is used to analyze distributed big data, streaming data, timestamp data and text data. With the help of Apache Flume, the tweets can be collected from Twitter and then sink in the HDFS (Hadoop Distributed File System). These raw data then analyzed using Apache Pig and the information available can be made use for social and commercial purposes. The result will be visualized using Apache Zeppelin.

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Section: Hadoopmentioning

confidence: 99%

Twitter data analysis using hadoop ecosystems and apache zeppelin

Wilson

Sivakumar

2019

IJEECS

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“…There are many different types of studies in the area of big data processing models. For example, data flow models such as MapReduce, which facilitate data processing utilizing a variety of operators while sharing stable storage systems, are one type of study [68]- [74]. Resilient distributed datasets (RDDs) are a more efficient data sharing abstraction from stable storage since they do not require data copying, which saves money.…”

Section: Introductionmentioning

confidence: 99%

Large scale data analysis using MLlib

et al. 2021

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Recent advancements in the internet, social media, and internet of things (IoT) devices have significantly increased the amount of data generated in a variety of formats. The data must be converted into formats that is easily handled by the data analysis techniques. It is mathematically and physically expensive to apply machine learning algorithms to big and complicated data sets. It is a resource-intensive process that necessitates a huge amount of logical and physical resources. Machine learning is a sophisticated data analytics technology that has gained in importance as a result of the massive amount of data generated daily that needs to be examined. Apache Spark machine learning library (MLlib) is one of the big data analysis platforms that provides a variety of outstanding functions for various machine learning tasks, spanning from classification to regression and dimension reduction. From a computational standpoint, this research investigated Apache Spark MLlib 2.0 as an open source, autonomous, scalable, and distributed learning library. Several real-world machine learning experiments are carried out in order to evaluate the properties of the platform on a qualitative and quantitative level. Some of the fundamental concepts and approaches for developing a scalable data model in a distributed environment are also discussed.

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“…The most common method for mobile application development using image processing machine learning technique is Mobile Cloud Computing (MCC). MCC is combination of mobiles and cloud computing which can simply processing from the user loca l devices to the data centre facilities over the internet [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. The major service providers of MCC are including Google, Amazon, Microsoft and Yahoo.…”

Section: Introductionmentioning

confidence: 99%

Development of vocabulary learning application by using machine learning technique

Brahin¹,

Nasir²,

Jidin³

et al. 2020

Bulletin EEI

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Nowadays an educational mobile application has been widely accepted and opened new windows of opportunity to explore. With its flexibility and practicality, the mobile application can promote learning through playing with an interactive environment especially to the children. This paper describes the development of mobile learning to help children above 4 years old in learning English and Arabic language in a playful and fun way. The application is developed with a combination of Android Studio and the machine learning technique, TensorFlow object detection API in order to predict the output result. Developed application namely “LearnWithIman” has successfully been implemented and the results show the prediction of application is accurate based on the captured image with the list item. The inclusion of the user database for lesson tracking and new lesson will be added for improvement in the future.

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An Accurate and Efficient Scheduler for Hadoop MapReduce Framework

Cited by 6 publications

References 15 publications

Twitter data analysis using hadoop ecosystems and apache zeppelin

Twitter data analysis using hadoop ecosystems and apache zeppelin

Large scale data analysis using MLlib

Development of vocabulary learning application by using machine learning technique

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