This HTML5 document contains 46 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
n8http://linked.opendata.cz/ontology/domain/vavai/riv/typAkce/
dctermshttp://purl.org/dc/terms/
n13http://localhost/temp/predkladatel/
n10http://purl.org/net/nknouf/ns/bibtex#
n17http://linked.opendata.cz/resource/domain/vavai/projekt/
n6http://linked.opendata.cz/resource/domain/vavai/riv/tvurce/
n15http://linked.opendata.cz/ontology/domain/vavai/
n11https://schema.org/
shttp://schema.org/
skoshttp://www.w3.org/2004/02/skos/core#
n3http://linked.opendata.cz/ontology/domain/vavai/riv/
n2http://linked.opendata.cz/resource/domain/vavai/vysledek/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n21http://linked.opendata.cz/resource/domain/vavai/vysledek/RIV%2F00216224%3A14330%2F14%3A00074391%21RIV15-GA0-14330___/
n5http://linked.opendata.cz/ontology/domain/vavai/riv/klicoveSlovo/
n9http://linked.opendata.cz/ontology/domain/vavai/riv/duvernostUdaju/
xsdhhttp://www.w3.org/2001/XMLSchema#
n19http://linked.opendata.cz/ontology/domain/vavai/riv/jazykVysledku/
n14http://linked.opendata.cz/ontology/domain/vavai/riv/aktivita/
n20http://linked.opendata.cz/ontology/domain/vavai/riv/druhVysledku/
n18http://linked.opendata.cz/ontology/domain/vavai/riv/obor/
n4http://reference.data.gov.uk/id/gregorian-year/

Statements

Subject Item
n2:RIV%2F00216224%3A14330%2F14%3A00074391%21RIV15-GA0-14330___
rdf:type
n15:Vysledek skos:Concept
dcterms:description
The current explosion of data accelerated evolution of various content-based indexing techniques that allow to efficiently search in multimedia data such as images. However, indexable features must be first extracted from the raw images before the indexing. This necessary step can be very time consuming for large datasets thus parallelization is desirable to speed the process up. In this paper, we experimentally compare two approaches to distribute the task among multiple machines: the Apache Hadoop and the Apache Storm projects. The current explosion of data accelerated evolution of various content-based indexing techniques that allow to efficiently search in multimedia data such as images. However, indexable features must be first extracted from the raw images before the indexing. This necessary step can be very time consuming for large datasets thus parallelization is desirable to speed the process up. In this paper, we experimentally compare two approaches to distribute the task among multiple machines: the Apache Hadoop and the Apache Storm projects.
dcterms:title
Towards Fast Multimedia Feature Extraction: Hadoop or Storm Towards Fast Multimedia Feature Extraction: Hadoop or Storm
skos:prefLabel
Towards Fast Multimedia Feature Extraction: Hadoop or Storm Towards Fast Multimedia Feature Extraction: Hadoop or Storm
skos:notation
RIV/00216224:14330/14:00074391!RIV15-GA0-14330___
n3:aktivita
n14:P
n3:aktivity
P(GBP103/12/G084)
n3:dodaniDat
n4:2015
n3:domaciTvurceVysledku
n6:8876398 Mera Pérez, David n6:3165647
n3:druhVysledku
n20:D
n3:duvernostUdaju
n9:S
n3:entitaPredkladatele
n21:predkladatel
n3:idSjednocenehoVysledku
50823
n3:idVysledku
RIV/00216224:14330/14:00074391
n3:jazykVysledku
n19:eng
n3:klicovaSlova
Multimedia; Big Data; Feature Extraction; Map Reduce; Apache Storm; Apache Hadoop
n3:klicoveSlovo
n5:Apache%20Storm n5:Multimedia n5:Feature%20Extraction n5:Apache%20Hadoop n5:Map%20Reduce n5:Big%20Data
n3:kontrolniKodProRIV
[A92BAFF8557B]
n3:mistoKonaniAkce
Taichung, Taiwan
n3:mistoVydani
Washington, DC
n3:nazevZdroje
Proceedings of 2014 IEEE International Symposium on Multimedia (ISM)
n3:obor
n18:IN
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n17:GBP103%2F12%2FG084
n3:rokUplatneniVysledku
n4:2014
n3:tvurceVysledku
Batko, Michal Mera Pérez, David Zezula, Pavel
n3:typAkce
n8:WRD
n3:zahajeniAkce
2014-12-10+01:00
s:numberOfPages
4
n10:hasPublisher
IEEE Computer Society Publications
n11:isbn
9781479943111
n13:organizacniJednotka
14330