Performances Evaluation of a Novel Hadoop and Spark Based System of Image Retrieval for Huge Collections

Joint Authors

Costantini, Luca
Nicolussi, Raffaele

Source

Advances in Multimedia

Issue

Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-7, 7 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2015-12-16

Country of Publication

Egypt

No. of Pages

7

Main Subjects

Information Technology and Computer Science

Abstract EN

A novel system of image retrieval, based on Hadoop and Spark, is presented.

Managing and extracting information from Big Data is a challenging and fundamental task.

For these reasons, the system is scalable and it is designed to be able to manage small collections of images as well as huge collections of images.

Hadoop and Spark are based on the MapReduce framework, but they have different characteristics.

The proposed system is designed to take advantage of these two technologies.

The performances of the proposed system are evaluated and analysed in terms of computational cost in order to understand in which context it could be successfully used.

The experimental results show that the proposed system is efficient for both small and huge collections.

American Psychological Association (APA)

Costantini, Luca& Nicolussi, Raffaele. 2015. Performances Evaluation of a Novel Hadoop and Spark Based System of Image Retrieval for Huge Collections. Advances in Multimedia،Vol. 2015, no. 2015, pp.1-7.
https://search.emarefa.net/detail/BIM-1052645

Modern Language Association (MLA)

Costantini, Luca& Nicolussi, Raffaele. Performances Evaluation of a Novel Hadoop and Spark Based System of Image Retrieval for Huge Collections. Advances in Multimedia No. 2015 (2015), pp.1-7.
https://search.emarefa.net/detail/BIM-1052645

American Medical Association (AMA)

Costantini, Luca& Nicolussi, Raffaele. Performances Evaluation of a Novel Hadoop and Spark Based System of Image Retrieval for Huge Collections. Advances in Multimedia. 2015. Vol. 2015, no. 2015, pp.1-7.
https://search.emarefa.net/detail/BIM-1052645

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1052645