Using Advanced Data Mining and Integration in Environmental Prediction Scenarios

Habala, Ondrej; Hluchý, Ladislav; Tran, Viet; Krammer, Peter H.; Seleng, Martin

doi:10.7494/csci.2012.13.1.5

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…information about its occurrence is important as an input to various hydrological models of river flows and flooded areas, for meteorologists, agriculture, or as an input necessary for any local flood warning system. Data mining is emerging as a suitable method for extracting patterns from extensive sets of heterogeneous data related to prediction of meteorological phenomena [5], [6].…”

Section: Significant and Hazardous Meteorological Phenomenamentioning

confidence: 99%

Data mining and integration for predicting significant meteorological phenomena

Bartok¹,

Habala²,

Bednár³

et al. 2010

Procedia Computer Science

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a MicroStep-MIS spol. s r.o., Čavojského 1, 84108 Bratislava, Slovenská republika b Ústav informatiky SAV, Dúbravská cesta 9, 84507 Bratislava, Slovenská republika c Fakulta elektrotechniky a informatiky TUKE, AbstractThis paper describes the planned contribution of the project Data Mining Meteo (DMM) to the research of parametrized models and methods for detection and prediction of significant meteorological phenomena, especially fog and low cloud cover. The project is expected to cover methods for integration of distributed meteorological data necessary for running the prediction models, training models and then mining the data in order to be able to efficiently and quickly predict even randomly occurring phenomena. We present the methods and technologies we will use for integration of the input data, distributed on different vendors' servers. The meteorological detection and prediction methods are based on statistical and climatological methods combined with knowledge discovery -data mining of meteorological data (SYNOP, METAR messages, weather radar imagery, "raw" meteorological data from stations, satellite imagery and results of common meteorological prediction models).

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Section: Significant and Hazardous Meteorological Phenomenamentioning

confidence: 99%

Data mining and integration for predicting significant meteorological phenomena

Bartok¹,

Habala²,

Bednár³

et al. 2010

Procedia Computer Science

Self Cite

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Implementation Data Mining with the Naive Bayes Classifier Algorithm in Determining the Type of Stroke

Ningrum,

Yosrita,

Djunaidi

et al. 2023

2023 International Conference on Networking, Electrical Engineering, Computer Science, and Technology (IConNECT)

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Distributed Web-Scale Infrastructure for Crawling, Indexing and Search With Semantic Support

Dlugolinský

Seleng

Laclavik

et al. 2012

csci

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In this paper, we describe our work in progress in the scope of web-scale informationextraction and information retrieval utilizing distributed computing. Wepresent a distributed architecture built on top of the MapReduce paradigm forinformation retrieval, information processing and intelligent search supportedby spatial capabilities. Proposed architecture is focused on crawling documentsin several different formats, information extraction, lightweight semantic annotationof the extracted information, indexing of extracted information andfinally on indexing of documents based on the geo-spatial information foundin a document. We demonstrate the architecture on two use cases, where thefirst is search in job offers retrieved from the LinkedIn portal and the second issearch in BBC news feeds and discuss several problems we had to face duringthe implementation. We also discuss spatial search applications for both casesbecause both LinkedIn job offer pages and BBC news feeds contain a lot of spatialinformation to extract and process

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Using Advanced Data Mining and Integration in Environmental Prediction Scenarios

Cited by 3 publications

References 8 publications

Data mining and integration for predicting significant meteorological phenomena

Data mining and integration for predicting significant meteorological phenomena

Implementation Data Mining with the Naive Bayes Classifier Algorithm in Determining the Type of Stroke

Distributed Web-Scale Infrastructure for Crawling, Indexing and Search With Semantic Support

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