Multicast-based replication for Hadoop HDFS

Wu, Jiadong; Hong, Bin Na

doi:10.1109/snpd.2015.7176191

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…With the rapid advancement of the Apache Hadoop project, HDFS now serves a thriving ecosystem of open-source big data technologies, such as Giraph, Spark, Pig, Hive, and HBase. The performance of HDFS is crucial to all these components built on top of it (Wu & Hong, 2015).…”

Section: Hadoop Distributed File System Hdfsmentioning

confidence: 99%

Finding Relevant Documents in a Search Engine Using N-Grams Model and Reinforcement Learning

Hadi

Madani

Ayachi

et al. 2022

Journal of Information Technology Research

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The field of information retrieval (IR) is an important area in computer science, this domain helps us to find information that we are interested in from an important volume of information. A search engine is the best example of the application of information retrieval to get the most relevant results. In this paper, we propose a new recommendation approach for recommending relevant documents to a search engine’s users. In this work, we proposed a new approach for calculating the similarity between a user query and a list of documents in a search engine. The proposed method uses a new reinforcement learning algorithm based on n-grams model (i.e., a sub-sequence of n constructed elements from a given sequence) and a similarity measure. Results show that our method outperforms some methods from the literature with a high value of accuracy.

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Section: Hadoop Distributed File System Hdfsmentioning

confidence: 99%

Finding Relevant Documents in a Search Engine Using N-Grams Model and Reinforcement Learning

Hadi

Madani

Ayachi

et al. 2022

Journal of Information Technology Research

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“…The various features of the GFS also available in Hadoop as automatic failure management, flexible horizontal scalability, check sum correction and file redundancy (Wu and Hong, 2015). HDFS provides the highest level of fault tolerance over the low cost computer clusters.…”

Section: Hdfsmentioning

confidence: 99%

Processing Large Volume of Biometric Data in the Hadoop Single Cluster Node Environment

Vaithiyashankar¹,

Sayeed²,

Amin³

2017

American Journal of Applied Sciences

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Abstract:In big data evolution, the analysis of large scale data and scrutinizing the required vital information becomes very demanding task. The emerging cloud platform promises and gives hope in handling the enormous volume of data. Hence, a new kind of methodology is required to tap the full potential of leveraging the big data analytics over the biometric data. In this work, we are going to deal with the integration of Hadoop, a map reduce framework with the infamous powerful computer vision library tool, Opencv. The proposed setup will comparatively analyze the large set of biometric data; such as face over the pseudo distributed environment. We test the capacity of our methodology with a different data set and analyze various computational parameters. The results show the proposed method is applicable for dealing in the real distributed environment.

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