2553143
doi
10.5281/zenodo.2553143
oai:zenodo.org:2553143
user-eu
Garza, Paolo
Politecnico di Torino
PERCEIVE: Precipitation Data Characterization by means on Frequent Spatio-Temporal Sequences
Farasin, Alessandro
Istituto Superiore Mario Boella and Politecnico di Torino
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Spatio-temporal sequence mining
Data characterization
<p>Nowadays large amounts of climatology data, including daily precipitation data, are collected by means of sensors<br>
located in different locations of the world. The data driven analysis of these large data sets by means of scalable<br>
machine learning and data mining techniques allows extracting interesting knowledge from data, inferring interesting<br>
patterns and correlations among sets of spatio-temporal events and characterizing them. In this paper, we describe<br>
the PERCEIVE framework. PERCEIVE is a data-driven framework based on frequent spatio-temporal sequences<br>
and aims at extracting frequent correlations among spatio-temporal precipitation events. It is implemented by<br>
using R and Apache Spark, for scalability reasons, and provides also a visualization module that can be used to<br>
intuitively show the extracted patterns. A preliminary set of experiments show the efficiency and the effectiveness<br>
of PERCEIVE.</p>
Zenodo
2018-05-20
info:eu-repo/semantics/conferencePaper
2553142
user-eu
award_title=Improving Resilience to Emergencies through Advanced Cyber Technologies; award_number=700256; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/700256; funder_id=00k4n6c32; funder_name=European Commission;
1579540770.070488
888745
md5:0621bac89e6133211ceee18ed77832ac
https://zenodo.org/records/2553143/files/1628_AlessandroFarasin+PaoloGarza2018.pdf
public
10.5281/zenodo.2553142
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doi