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 a Pavel ZEZULA

Základní údaje

Originální název

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

Autoři

MERA PÉREZ, David (724 Španělsko), Michal BATKO (203 Česká republika, garant, domácí) a Pavel ZEZULA (203 Česká republika, domácí)

Vydání

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

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 1.541

Kód RIV

RIV/00216224:14330/17:00094702

Organizační jednotka

Fakulta informatiky

DOI

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

UT WoS

000397278400062

Klíčová slova anglicky

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

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 27. 4. 2018 10:32, RNDr. Pavel Šmerk, Ph.D.

Anotace

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.

Návaznosti

GBP103/12/G084, projekt VaV

Název: Centrum pro multi-modální interpretaci dat velkého rozsahu

Investor: Grantová agentura ČR, Centrum pro multi-modální interpretaci dat velkého rozsahu

Citovat

MERA PÉREZ, David, Michal BATKO a Pavel ZEZULA. Speeding up the multimedia feature extraction: a comparative study on the big data approach. Multimedia Tools and Applications. Springer, 2017, roč. 76, č. 5, s. 7497-7517. ISSN 1380-7501. Dostupné z: 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 a Pavel ZEZULA. Speeding up the multimedia feature extraction: a comparative study on the big data approach. \textit{Multimedia Tools and Applications}. Springer, 2017, roč.~76, č.~5, s.~7497-7517. ISSN~1380-7501. Dostupné z: https://dx.doi.org/10.1007/s11042-016-3415-1.

Podrobný výpis o publikaci