Performance comparison of a parallel recommender algorithm across three Hadoop -based frameworks
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One of the challenges our society faces is the ever increasing amount of data. Among existing platforms that address the system requirements, Hadoop is a framework widely used to store and analyze big data . On the human side, one of the aids to finding the things

Fast, Parallel Stream Clustering using Hadoop Online
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Cluster Analysis suggests how groups of units are determined such that units within groups are similar in some respect and unlike those from other groups. Units in computer science would be any kind of multi-dimensional points. So, clustering is very useful in a large variety

Managing Skew in Hadoop .
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Abstract Challenges in Big Data analytics stem not only from volume, but also variety: extreme diversity in both data types (eg, text, images, and graphs) and in operations beyond relational algebra (eg, machine learning, natural language processing, image processing

Making Hadoop MapReduce Byzantine Fault-Tolerant
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MapReduce is a programming model and a runtime environment designed by Google for processing large data sets in its warehouse-scale machines (WSM) with hundreds to thousands of servers [2, 4]. MapReduce is becoming increasingly popular with the

Solr, lucene and Hadoop : Towards a complete solution to improve research in big data environment (Case of the UAE)
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In this article we present a complete solution to improve the process of information search by the UAEs student in Big Data environment. This, with the intention to evaluate the reliability and the value of the returned information to identify opportunities for improvement and