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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F17%3A00094702" target="_blank" >RIV/00216224:14330/17:00094702 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/s11042-016-3415-1" target="_blank" >http://dx.doi.org/10.1007/s11042-016-3415-1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11042-016-3415-1" target="_blank" >10.1007/s11042-016-3415-1</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Centrum pro multi-modální interpretaci dat velkého rozsahu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Multimedia Tools and Applications

ISSN

1380-7501

e-ISSN

—

Svazek periodika

76

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

21

Strana od-do

7497-7517

Kód UT WoS článku

000397278400062

EID výsledku v databázi Scopus

2-s2.0-84960356866