Experiments with computing similarity coefficient over big data

Author

Cosulschi, M. ; Gabroveanu, M. ; Slabu, F. ; Sbircea, A.

Author_Institution

Dept. of Comput. Sci., Univ. of Craiova, Craiova, Romania

fYear

2014

fDate

7-9 July 2014

Firstpage

112

Lastpage

117

Abstract

Big data is a hot topic nowadays due to the huge amount of data resulted from various commercial processes and also due to every day data handled by social networks. The MapReduce programming model focuses on processing and generating large data sets. Using the values obtained by computing the Jaccard similarity coefficients for two very large graphs, we have analysed the connections and influences that some nodes have over the other nodes. Furthermore, we have shown how Apache Hadoop framework and MapReduce programming model can be used for high volume computations. All tests were performed on a distributed cluster in order to obtain the results described in the paper.

Keywords

Big Data; data analysis; graph theory; pattern clustering; programming; Apache Hadoop framework; Jaccard similarity coefficients; MapReduce programming model; big data; commercial processes; distributed cluster; graphs; high volume computations; large data sets; social networks; Big data; Cloud computing; Computational modeling; Facebook; Indexes; Programming;

fLanguage

English

Publisher

ieee

Conference_Titel

Information, Intelligence, Systems and Applications, IISA 2014, The 5th International Conference on

Conference_Location

Chania

Type

conf

DOI

10.1109/IISA.2014.6878734

Filename

6878734