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Author (up) 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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Author (up) Frédérick Bénaben; Chihab Hanachi; Matthieu Lauras; Pierre Couget; Vincent Chapurlat
Title A metamodel and its ontology to guide crisis characterization and its collaborative management Type Conference Article
Year 2008 Publication Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2008
Volume Issue Pages 189-196
Keywords Characterization; Hardware; Ontology; Automated reasoning; Collaborative management; Collaborative process; Crisis; Crisis management; Metamodeling; Reference modeling; System of systems; Information systems
Abstract This paper presents a research in progress about the French ISyCri project that aims at providing partners involved in crisis management with an agile Mediation Information System (MIS). Not only this MIS shoul support the interoperability of the partners' information systems but it is also dedicated to coordinate their activities through a collaborative process. One of the first and main steps towards such a MIS, is to elaborate a common and sharable reference model built to characterize crisis situations. Such a model is also an input for automated reasoning to elaborate and adapt a crisis solving collaborative process. This article presents the objective of the project, our approach and our first results: a UML metamodel of crisis situation and its corresponding OWL ontology on top of which deductions are possible.
Address Université de Toulouse, Mines ALBI, DR/GI, France; Université de Toulouse 1, France; Prefecture du Tarn, France; Ecole des Mines D'Alès, LGI2P, France
Corporate Author Thesis
Publisher Information Systems for Crisis Response and Management, ISCRAM Place of Publication Washington, DC Editor F. Fiedrich, B. Van de Walle
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 9780615206974 Medium
Track Ontologies for Crisis Management Expedition Conference 5th International ISCRAM Conference on Information Systems for Crisis Response and Management
Notes Approved no
Call Number Serial 301
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