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Statements

Subject Item
n2:RIV%2F00216224%3A14330%2F10%3A00044990%21RIV11-GA0-14330___
rdf:type
n12:Vysledek skos:Concept
dcterms:description
Similarity is a central notion throughout human lives and it will soon become the prevalent strategy for dealing with digital content also in computer systems. But the exponential growth of data makes the scalability and performance issues serious matters of concern. Contemporary decentralized media of mass communication allowing cooperative and collaborative practices enable users autonomously contribute to production of global media, whose elements are in fact related by numerous multi-facet links of similarity. As an example, consider the sites like Flickr, YouTube, or Facebook that host user-contributed heterogeneous content for a variety of events. Accordingly, the core ability of future data processing systems is the similarity management of large and ever growing volumes of data. In a simplified way, the real-life performance can be constrained from two points of view: (1) the query response time, and (2) the query execution throughput, i.e. the number of queries processed per a unit of time. Similarity is a central notion throughout human lives and it will soon become the prevalent strategy for dealing with digital content also in computer systems. But the exponential growth of data makes the scalability and performance issues serious matters of concern. Contemporary decentralized media of mass communication allowing cooperative and collaborative practices enable users autonomously contribute to production of global media, whose elements are in fact related by numerous multi-facet links of similarity. As an example, consider the sites like Flickr, YouTube, or Facebook that host user-contributed heterogeneous content for a variety of events. Accordingly, the core ability of future data processing systems is the similarity management of large and ever growing volumes of data. In a simplified way, the real-life performance can be constrained from two points of view: (1) the query response time, and (2) the query execution throughput, i.e. the number of queries processed per a unit of time.
dcterms:title
Real-life performance of metric searching Real-life performance of metric searching
skos:prefLabel
Real-life performance of metric searching Real-life performance of metric searching
skos:notation
RIV/00216224:14330/10:00044990!RIV11-GA0-14330___
n3:aktivita
n15:S n15:P
n3:aktivity
P(GA201/09/0683), S
n3:cisloPeriodika
2
n3:dodaniDat
n6:2011
n3:domaciTvurceVysledku
n10:3165647 n10:3540324
n3:druhVysledku
n5:J
n3:duvernostUdaju
n13:S
n3:entitaPredkladatele
n16:predkladatel
n3:idSjednocenehoVysledku
284073
n3:idVysledku
RIV/00216224:14330/10:00044990
n3:jazykVysledku
n18:eng
n3:klicovaSlova
similarity searching; real-life performance; metric space
n3:klicoveSlovo
n7:metric%20space n7:similarity%20searching n7:real-life%20performance
n3:kodStatuVydavatele
US - Spojené státy americké
n3:kontrolniKodProRIV
[5F3AEF754DF9]
n3:nazevZdroje
SIGSPATIAL Special
n3:obor
n11:IN
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n17:GA201%2F09%2F0683
n3:rokUplatneniVysledku
n6:2010
n3:svazekPeriodika
2
n3:tvurceVysledku
Dohnal, Vlastislav Zezula, Pavel
s:issn
1946-7729
s:numberOfPages
4
n8:organizacniJednotka
14330