About: Recognizing characteristic patterns in distorted data collections

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Recognizing characteristic patterns in distorted data collections Goto Sponge Distinct Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	Many models and artificial intelligence methods work with the inputs in the form of time series. Generally, success of many of them strongly depends on ability to successfully manage input data, which often contains repeating similar episodes (patterns). If these patterns are recognized, they can be used for instance for indexing, prediction or compression. These operations can also be very useful for improving the already existing model performance and accuracy. Our effort is to provide a robust mechanism for retrieving these characteristic patterns from the collections that are subject of various distortions. The whole process of our pattern recognition consists of receiving the episodes, their clustering into the groups of similar episodes and deriving the representatives of each cluster. These representatives will be used for further indexing collections. This paper is focused on the last step of this process - receiving the representatives of concrete clusters using Dynamic Time Warping method. Many models and artificial intelligence methods work with the inputs in the form of time series. Generally, success of many of them strongly depends on ability to successfully manage input data, which often contains repeating similar episodes (patterns). If these patterns are recognized, they can be used for instance for indexing, prediction or compression. These operations can also be very useful for improving the already existing model performance and accuracy. Our effort is to provide a robust mechanism for retrieving these characteristic patterns from the collections that are subject of various distortions. The whole process of our pattern recognition consists of receiving the episodes, their clustering into the groups of similar episodes and deriving the representatives of each cluster. These representatives will be used for further indexing collections. This paper is focused on the last step of this process - receiving the representatives of concrete clusters using Dynamic Time Warping method. (en)
Title	Recognizing characteristic patterns in distorted data collections Recognizing characteristic patterns in distorted data collections (en)
skos:prefLabel	Recognizing characteristic patterns in distorted data collections Recognizing characteristic patterns in distorted data collections (en)
skos:notation	RIV/61989100:27740/13:86088268!RIV14-TA0-27740___
http://linked.open...avai/predkladatel	IT4Innovations
http://linked.open...avai/riv/aktivita	P
http://linked.open...avai/riv/aktivity	P(ED1.1.00/02.0070), P(TA01021374)
http://linked.open...vai/riv/dodaniDat	2014
http://linked.open...aciTvurceVysledku	Kocyan, Tomáš Martinovič, Jan
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Vysoká škola báňská - Technická univerzita Ostrava / IT4Innovations
http://linked.open...dnocenehoVysledku	101752
http://linked.open...ai/riv/idVysledku	RIV/61989100:27740/13:86088268
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Time series; Pattern recognition; Dynamic time warping; Clustering (en)
http://linked.open.../riv/klicoveSlovo	Pattern recognition Clustering Time series Dynamic time warping
http://linked.open...ontrolniKodProRIV	[DF7A82B82EC9]
http://linked.open...v/mistoKonaniAkce	Athény
http://linked.open...i/riv/mistoVydani	Genova
http://linked.open...i/riv/nazevZdroje	25th European Modeling and Simulation Symposium, EMSS 2013
http://linked.open...in/vavai/riv/obor	IN
http://linked.open...ichTvurcuVysledku	2 (xsd:int)
http://linked.open...cetTvurcuVysledku	4 (xsd:int)
http://linked.open...vavai/riv/projekt	IT4Innovations Centre of Excellence New Technologies for Environmental Protection against the Negative Consequences of Natural Mass Movement
http://linked.open...UplatneniVysledku	2013
http://linked.open...iv/tvurceVysledku	Dráždilová, Pavla Kocyan, Tomáš Martinovič, Jan Slaninová, Kateřina
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open.../riv/zahajeniAkce	2013-09-25 (xsd:date)
number of pages	6 (xsd:int)
http://purl.org/ne...btex#hasPublisher	DIME Università Di Genova
https://schema.org/isbn	978-88-97999-22-5
http://localhost/t...ganizacniJednotka	27740

Faceted Search & Find service v1.16.118 as of Jun 21 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 112 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software