Highly scalable algorithm for computation of recurrence quantitative analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F19%3A10242321" target="_blank" >RIV/61989100:27740/19:10242321 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/s11227-018-2350-5" target="_blank" >https://doi.org/10.1007/s11227-018-2350-5</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11227-018-2350-5" target="_blank" >10.1007/s11227-018-2350-5</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Highly scalable algorithm for computation of recurrence quantitative analysis

Popis výsledku v původním jazyce

Recurrence plot analysis is a well-established method to analyse time seriesin numerous areas of research. However, it has exponential computational and spa-tial complexity. As the main result of this paper, a technique for the computation ofrecurrence quantitative analysis (RQA) is outlined. This method significantly reducesspatial complexity of computation by computing RQA directly from the time series,optimizing memory accesses and reducing computational time. Additionally, parallelimplementation of this technique is tested on the Salomon cluster and is proved to beextremely fast and scalable. This means that recurrence quantitative analysis may beapplied to longer time series or in applications with the need of real-time analysis

Název v anglickém jazyce

Highly scalable algorithm for computation of recurrence quantitative analysis

Popis výsledku anglicky

Recurrence plot analysis is a well-established method to analyse time seriesin numerous areas of research. However, it has exponential computational and spa-tial complexity. As the main result of this paper, a technique for the computation ofrecurrence quantitative analysis (RQA) is outlined. This method significantly reducesspatial complexity of computation by computing RQA directly from the time series,optimizing memory accesses and reducing computational time. Additionally, parallelimplementation of this technique is tested on the Salomon cluster and is proved to beextremely fast and scalable. This means that recurrence quantitative analysis may beapplied to longer time series or in applications with the need of real-time analysis

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

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

Journal of Supercomputing

ISSN

0920-8542

e-ISSN

—

Svazek periodika

75

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

12

Strana od-do

1175-1186

Kód UT WoS článku

000463635700015

EID výsledku v databázi Scopus

2-s2.0-85044730270