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Hamzah Noori Fejer,Mohanaed Ajmi Falih
In this paper, a new method has been proposed to eliminate the weaknesses in the previous algorithms. The proposed method for data density clustering is reduced in the mapping programming model. Our analysis result shows that misleading data was presente...
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Yong Wang, Wenlong Ke and Xiaoling Tao
Currently, with the rapid increasing of data scales in network traffic classifications, how to select traffic features efficiently is becoming a big challenge. Although a number of traditional feature selection methods using the Hadoop-MapReduce framewor...
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P.V. Kumaraguru, Vidyavathi Kamalakkannan, Gururaj H L, Francesco Flammini, Badria Sulaiman Alfurhood and Rajesh Natarajan
Terabytes of data are now being handled by an increasing number of apps, and rapid user decision-making is hampered by data analysis. At the same time, there is a rise in interest in big data analysis for social networks at the moment. Thus, adopting dis...
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Dauren Ayazbayev, Andrey Bogdanchikov, Kamila Orynbekova and Iraklis Varlamis
This work focuses on determining semantically close words and using semantic similarity in general in order to improve performance in information retrieval tasks. The semantic similarity of words is an important task with many applications from informati...
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Andreas Kanavos, Stavros Anastasios Iakovou, Spyros Sioutas and Vassilis Tampakas
In this manuscript, we present a prediction model based on the behaviour of each customer using data mining techniques. The proposed model utilizes a supermarket database and an additional database from Amazon, both containing information about customers...
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Andreas Kanavos, Stavros Anastasios Iakovou, Spyros Sioutas and Vassilis Tampakas
In this manuscript, we present a prediction model based on the behaviour of each customer using data mining techniques. The proposed model utilizes a supermarket database and an additional database from Amazon, both containing information about customers...
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Kevin Aydin, MohammadHossein Bateni and Vahab Mirrokni
Balanced partitioning is often a crucial first step in solving large-scale graph optimization problems, for example, in some cases, a big graph can be chopped into pieces that fit on one machine to be processed independently before stitching the results ...
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Matheus H. M. Pericini, Lucas G. M. Leite, Francisco H. De Carvalho-Junior, Javam C. Machado and Cenez A. Rezende
MapReduce is a parallel computing model in which a large dataset is split into smaller parts and executed on multiple machines. Due to its simplicity, MapReduce has been widely used in various applications domains. MapReduce can significantly reduce the ...
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Matheus H. M. Pericini, Lucas G. M. Leite, Francisco H. De Carvalho-Junior, Javam C. Machado and Cenez A. Rezende
MapReduce is a parallel computing model in which a large dataset is split into smaller parts and executed on multiple machines. Due to its simplicity, MapReduce has been widely used in various applications domains. MapReduce can significantly reduce the ...
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Md. Anisuzzaman Siddique, Hao Tian, Mahboob Qaosar and Yasuhiko Morimoto
The skyline query and its variant queries are useful functions in the early stages of a knowledge-discovery processes. The skyline query and its variant queries select a set of important objects, which are better than other common objects in the dataset....
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Christian Sturm, Myriel Fichtner and Stefan Schönig
Declarative process management has emerged as an alternative solution for describing flexible workflows. In turn, the modelling opportunities with languages such as Declare are less intuitive and hard to implement. The area of process discovery covers th...
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