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Statements

Subject Item
n2:RIV%2F61989100%3A27740%2F14%3A86091970%21RIV15-MSM-27740___
rdf:type
skos:Concept n21:Vysledek
dcterms:description
Success of many models and artificial intelligence methods strongly depends on ability to quickly and precisely search input data collection. Despite the existence of many algorithms for faster earching, the most of them fail while processing distorted input. Unfortunately, the distortion is natural for many types of data collections, especially for measurements of natural phenomena such as recipitations, river discharge volume etc. In this type of collections, there are no exact levels for generated values. This paper discusses possibilities of indexing and searching such distorted inputs and also proposes an alternative approach for their indexing. The proposed approach utilizes the Voting Experts algorithm for splitting the input regarding statistical indicators, the Dynamic Time Warping for dealing with distorted inaccuracies and hierarchical clustering for grouping similar sequences. Finally, the sample result of proposed algorithm applied on data collections consisting of measured river discharge volumes is shown. Success of many models and artificial intelligence methods strongly depends on ability to quickly and precisely search input data collection. Despite the existence of many algorithms for faster earching, the most of them fail while processing distorted input. Unfortunately, the distortion is natural for many types of data collections, especially for measurements of natural phenomena such as recipitations, river discharge volume etc. In this type of collections, there are no exact levels for generated values. This paper discusses possibilities of indexing and searching such distorted inputs and also proposes an alternative approach for their indexing. The proposed approach utilizes the Voting Experts algorithm for splitting the input regarding statistical indicators, the Dynamic Time Warping for dealing with distorted inaccuracies and hierarchical clustering for grouping similar sequences. Finally, the sample result of proposed algorithm applied on data collections consisting of measured river discharge volumes is shown.
dcterms:title
SEARCHING AND INDEXING DISTORTED DATA COLLECTIONS SEARCHING AND INDEXING DISTORTED DATA COLLECTIONS
skos:prefLabel
SEARCHING AND INDEXING DISTORTED DATA COLLECTIONS SEARCHING AND INDEXING DISTORTED DATA COLLECTIONS
skos:notation
RIV/61989100:27740/14:86091970!RIV15-MSM-27740___
n3:aktivita
n18:P
n3:aktivity
P(ED1.1.00/02.0070), P(EE2.3.30.0055)
n3:dodaniDat
n17:2015
n3:domaciTvurceVysledku
n10:9491562 n10:1224492 Podhorányi, Michal
n3:druhVysledku
n12:D
n3:duvernostUdaju
n20:S
n3:entitaPredkladatele
n15:predkladatel
n3:idSjednocenehoVysledku
44094
n3:idVysledku
RIV/61989100:27740/14:86091970
n3:jazykVysledku
n13:eng
n3:klicovaSlova
time series, indexing, dynamic time warping, voting experts, symbolic approximation
n3:klicoveSlovo
n7:indexing n7:time%20series n7:symbolic%20approximation n7:dynamic%20time%20warping n7:voting%20experts
n3:kontrolniKodProRIV
[064F49419692]
n3:mistoKonaniAkce
Bordeaux
n3:mistoVydani
Genova
n3:nazevZdroje
26th European Modeling and Simulation Symposium, EMSS 2014
n3:obor
n4:IN
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n16:ED1.1.00%2F02.0070 n16:EE2.3.30.0055
n3:rokUplatneniVysledku
n17:2014
n3:tvurceVysledku
Podhorányi, Michal Kocyan, Tomáš Martinovič, Jan
n3:typAkce
n14:WRD
n3:zahajeniAkce
2014-09-10+02:00
s:numberOfPages
6
n11:hasPublisher
Dime University of Genoa
n19:isbn
978-88-97999-32-4
n6:organizacniJednotka
27740