Application of selected classifcation and clustering methods on the database query categorization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F15%3A86093670" target="_blank" >RIV/61989100:27510/15:86093670 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Application of selected classifcation and clustering methods on the database query categorization

Popis výsledku v původním jazyce

In this paper, we deal with the analysis of performance aspects that should influence query execution results when using anchor data modeling based database schema. The anchor data modeling technique is intended to implicitly support agile oriented datamodel design and data warehouse management processes. The querying process is carried out in the simulated environment of a sample data mart with 290 star queries executed to gather query execution data. The results then show that we were able to predictclass of a query execution time difference from 4 specified query aspects with a certain degree of precision. The classification results (using the k-Nearest Neighbors method) indicate that both empirical and predicted classes were statistically insignificantly different (on the 5% level of significance). The k-means clustering method's application results, on the other hand, showed that we are able to obtain distinguishable clusters from specified query aspects.

Název v anglickém jazyce

Application of selected classifcation and clustering methods on the database query categorization

Popis výsledku anglicky

In this paper, we deal with the analysis of performance aspects that should influence query execution results when using anchor data modeling based database schema. The anchor data modeling technique is intended to implicitly support agile oriented datamodel design and data warehouse management processes. The querying process is carried out in the simulated environment of a sample data mart with 290 star queries executed to gather query execution data. The results then show that we were able to predictclass of a query execution time difference from 4 specified query aspects with a certain degree of precision. The classification results (using the k-Nearest Neighbors method) indicate that both empirical and predicted classes were statistically insignificantly different (on the 5% level of significance). The k-means clustering method's application results, on the other hand, showed that we are able to obtain distinguishable clusters from specified query aspects.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/EE2.3.20.0296" target="_blank" >EE2.3.20.0296: Výzkumný tým pro modelování ekonomických a finančních procesů na VŠB-TU Ostrava</a><br>

Návaznosti

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

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

Strategic Management and its Support by Information Systems 2015 : proceedings of the 11th international conference : May 21st-22nd, Uherské Hradiště, Czech Republic

ISBN

978-80-248-3741-3

ISSN

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e-ISSN

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Počet stran výsledku

8

Strana od-do

439-446

Název nakladatele

VŠB - Technical University of Ostrava

Místo vydání

Ostrava

Místo konání akce

Uherské Hradiště

Datum konání akce

21. 5. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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