Speeding up the multimedia feature extraction: a comparative
study on the big data approach

J 2017

Speeding up the multimedia feature extraction: a comparative study on the big data approach

MERA PÉREZ, David, Michal BATKO and Pavel ZEZULA

Basic information

Original name

Speeding up the multimedia feature extraction: a comparative study on the big data approach

Authors

MERA PÉREZ, David (724 Spain), Michal BATKO (203 Czech Republic, guarantor, belonging to the institution) and Pavel ZEZULA (203 Czech Republic, belonging to the institution)

Edition

Multimedia Tools and Applications, Springer, 2017, 1380-7501

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 1.541

RIV identification code

RIV/00216224:14330/17:00094702

Organization unit

Faculty of Informatics

DOI

http://dx.doi.org/10.1007/s11042-016-3415-1

UT WoS

000397278400062

Keywords in English

Big data;Image feature extraction;Map Reduce;Apache Storm;Apache Spark;Grid computing

Abstract

V originále

The current explosion of multimedia data is significantly increasing the amount of potential knowledge. However, to get to the actual information requires to apply novel content-based techniques which in turn require time consuming extraction of indexable features from the raw data. In order to deal with large datasets, this task needs to be parallelized. However, there are multiple approaches to choose from, each with its own benefits and drawbacks. There are also several parameters that must be taken into consideration, for example the amount of available resources, the size of the data and their availability. In this paper, we empirically evaluate and compare approaches based on Apache Hadoop, Apache Storm, Apache Spark, and Grid computing, employed to distribute the extraction task over an outsourced and distributed infrastructure.

Links

GBP103/12/G084, research and development project

Name: Centrum pro multi-modální interpretaci dat velkého rozsahu

Investor: Czech Science Foundation

Citovat

MERA PÉREZ, David, Michal BATKO and Pavel ZEZULA. Speeding up the multimedia feature extraction: a comparative study on the big data approach. Multimedia Tools and Applications. Springer, 2017, vol. 76, No 5, p. 7497-7517. ISSN 1380-7501. Available from: https://dx.doi.org/10.1007/s11042-016-3415-1.

@article{1377502,
   author = {Mera Pérez, David and Batko, Michal and Zezula, Pavel},
   article_number = {5},
   doi = {http://dx.doi.org/10.1007/s11042-016-3415-1},
   keywords = {Big data;Image feature extraction;Map Reduce;Apache Storm;Apache Spark;Grid computing},
   language = {eng},
   issn = {1380-7501},
   journal = {Multimedia Tools and Applications},
   title = {Speeding up the multimedia feature extraction: a comparative study on the big data approach},
   url = {http://dx.doi.org/10.1007/s11042-016-3415-1},
   volume = {76},
   year = {2017}
}

TY  - JOUR
ID  - 1377502
AU  - Mera Pérez, David - Batko, Michal - Zezula, Pavel
PY  - 2017
TI  - Speeding up the multimedia feature extraction: a comparative study on the big data approach
JF  - Multimedia Tools and Applications
VL  - 76
IS  - 5
SP  - 7497-7517
EP  - 7497-7517
PB  - Springer
SN  - 13807501
KW  - Big data;Image feature extraction;Map Reduce;Apache Storm;Apache Spark;Grid computing
UR  - http://dx.doi.org/10.1007/s11042-016-3415-1
N2  - The current explosion of multimedia data is significantly increasing the amount of potential knowledge. However, to get to the actual information requires to apply novel content-based techniques which in turn require time consuming extraction of indexable features from the raw data. In order to deal with large datasets, this task needs to be parallelized. However, there are multiple approaches to choose from, each with its own benefits and drawbacks. There are also several parameters that must be taken into consideration, for example the amount of available resources, the size of the data and their availability. In this paper, we empirically evaluate and compare approaches based on Apache Hadoop, Apache Storm, Apache Spark, and Grid computing, employed to distribute the extraction task over an outsourced and distributed infrastructure.
ER  -

MERA PÉREZ, David, Michal BATKO and Pavel ZEZULA. Speeding up the multimedia feature extraction: a comparative study on the big data approach. \textit{Multimedia Tools and Applications}. Springer, 2017, vol.~76, No~5, p.~7497-7517. ISSN~1380-7501. Available from: https://dx.doi.org/10.1007/s11042-016-3415-1.

Detailed Information on Publication Record