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Raum­‐zeitliches Data Mining – Ein Werkzeug zur Analyse von Fischbewegungen im Murray River


Bleisch, Susanne; Laube, Patrick; Duckham, Matt (2013). Raum­‐zeitliches Data Mining – Ein Werkzeug zur Analyse von Fischbewegungen im Murray River. Geomatik Schweiz, 111(5):247-249.

Abstract

Sensornetzwerke ermöglichen Umweltmonitoring in höchster räumlicher und zeitlicher Auflösung. Raum-zeitliches Data Mining bietet ein geeignetes Analysewerkzeug, um die in derartigen Messnetzen entstehenden grossen Datenmengen hinsichtlich interessanter Zusammenhänge und Muster zu untersuchen. Dieser Artikel berichtet über den Einsatz von Sequenzanalyse und Assoziationsregelsuche in einem Umweltmonitoring-­Programm zum Gesundheitszustand von Fischhabitaten im Südosten Australiens. Erste Resultate liefern vielversprechende Hinweise auf kausale Zusammenhänge zwischen Umweltvariablen und beobachteten Fischbewegungen.

Geosensor networks allow for environmental monitoring at previously unseen spatial and temporal granularities. Spatio-temporal data mining offers a toolset for the analysis of the rich data sources produced by such systems. This article reports on a study using sequence and association rule mining on a data source emerging from a river health study in the Murray River, southeastern Australia. First results indicate that discovered sequence patterns and association rules might serve as proxies for causal relationship between environmental variables and monitored fish movements.

Abstract

Sensornetzwerke ermöglichen Umweltmonitoring in höchster räumlicher und zeitlicher Auflösung. Raum-zeitliches Data Mining bietet ein geeignetes Analysewerkzeug, um die in derartigen Messnetzen entstehenden grossen Datenmengen hinsichtlich interessanter Zusammenhänge und Muster zu untersuchen. Dieser Artikel berichtet über den Einsatz von Sequenzanalyse und Assoziationsregelsuche in einem Umweltmonitoring-­Programm zum Gesundheitszustand von Fischhabitaten im Südosten Australiens. Erste Resultate liefern vielversprechende Hinweise auf kausale Zusammenhänge zwischen Umweltvariablen und beobachteten Fischbewegungen.

Geosensor networks allow for environmental monitoring at previously unseen spatial and temporal granularities. Spatio-temporal data mining offers a toolset for the analysis of the rich data sources produced by such systems. This article reports on a study using sequence and association rule mining on a data source emerging from a river health study in the Murray River, southeastern Australia. First results indicate that discovered sequence patterns and association rules might serve as proxies for causal relationship between environmental variables and monitored fish movements.

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Additional indexing

Item Type:Journal Article, not refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:German
Date:2013
Deposited On:23 Jan 2014 07:50
Last Modified:05 Apr 2016 17:23
Publisher:Sigimedia
ISSN:1660-4458
Free access at:Related URL. An embargo period may apply.
Related URLs:http://retro.seals.ch/digbib/vollist?UID=geo-007

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