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Author
Alessandro Farasin
;
Paolo Garza
Title
PERCEIVE: Precipitation Data Characterization by means on Frequent Spatio-Temporal Sequences
Type
Conference Article
Year
2018
Publication
ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management
Abbreviated Journal
Iscram 2018
Volume
Issue
Pages
1081-1088
Keywords
Spatio-temporal sequence mining, Data characterization
Abstract
Nowadays large amounts of climatology data, including daily precipitation data, are collected by means of sensors located in different locations of the world. The data driven analysis of these large data sets by means of scalable machine learning and data mining techniques allows extracting interesting knowledge from data, inferring interesting patterns and correlations among sets of spatio-temporal events and characterizing them. In this paper, we describe the PERCEIVE framework. PERCEIVE is a data-driven framework based on frequent spatio-temporal sequences and aims at extracting frequent correlations among spatio-temporal precipitation events. It is implemented by using R and Apache Spark, for scalability reasons, and provides also a visualization module that can be used to intuitively show the extracted patterns. A preliminary set of experiments show the efficiency and the effectiveness of PERCEIVE.
Address
Corporate Author
Thesis
Publisher
Rochester Institute of Technology
Place of Publication
Rochester, NY (USA)
Editor
Kees Boersma; Brian Tomaszeski
Language
English
Summary Language
English
Original Title
Series Editor
Series Title
Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
2411-3387
ISBN
978-0-692-12760-5
Medium
Track
1st International Workshop on Intelligent Crisis Management Technologies for Climate Events (ICMT)
Expedition
Conference
ISCRAM 2018 Conference Proceedings - 15th International Conference on Information Systems for Crisis Response and Management
Notes
Approved
no
Call Number
Serial
2180
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