Is On-Line Data Analysis Safety? Pitfalls Steaming from Automated Processing of Heterogeneous Environmental Data and Possible Solutions

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Is On-Line Data Analysis Safety? Pitfalls Steaming from Automated Processing of Heterogeneous Environmental Data and Possible Solutions

Popis výsledku v původním jazyce

The current situation in environmental monitoring is characterized by increasing amount of data from monitoring networks together with increasing requirements on joining of these data from various sources in comprehensive databases and their usage for decision support in environmental protection and management. The automated analysis of such a heterogeneous datasets is a complicated process, rich in statistical pitfalls. There is a number of methods for multivariate classification of objects, e.g. logistic regression, discriminant analysis or neural networks; however, most of commonly used classification techniques have prerequisites about distribution of data, are computationally demanding or their model can be considered as ?black box?. Keeping thesefacts in mind, we attempted to develop a robust multivariate method suitable for classification of unknown cases with minimum sensitivity to data distribution problems; and thus, suitable for routine use in practice.

Název v anglickém jazyce

Is On-Line Data Analysis Safety? Pitfalls Steaming from Automated Processing of Heterogeneous Environmental Data and Possible Solutions

Popis výsledku anglicky

The current situation in environmental monitoring is characterized by increasing amount of data from monitoring networks together with increasing requirements on joining of these data from various sources in comprehensive databases and their usage for decision support in environmental protection and management. The automated analysis of such a heterogeneous datasets is a complicated process, rich in statistical pitfalls. There is a number of methods for multivariate classification of objects, e.g. logistic regression, discriminant analysis or neural networks; however, most of commonly used classification techniques have prerequisites about distribution of data, are computationally demanding or their model can be considered as ?black box?. Keeping thesefacts in mind, we attempted to develop a robust multivariate method suitable for classification of unknown cases with minimum sensitivity to data distribution problems; and thus, suitable for routine use in practice.

Druh

D - Stať ve sborníku

CEP obor

FP - Ostatní lékařské obory

OECD FORD obor

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Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2011

Kód důvěrnosti údajů

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

Název statě ve sborníku

Environmental Software Systems. Frameworks of eEnvironment. 9th IFIP WG 5.11 International Symposium, ISESS 2011, Brno, Czech Republic, June 27-29, 2011. Proceedings

ISBN

978-3-642-22284-9

ISSN

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e-ISSN

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Počet stran výsledku

5

Strana od-do

486-490

Název nakladatele

Springer

Místo vydání

Neuveden

Místo konání akce

Brno

Datum konání akce

1. 1. 2011

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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