Authors:
Paulo H. Oliveira
;
Antonio C. Fraideinberze
;
Natan A. Laverde
;
Hugo Gualdron
;
Andre S. Gonzaga
;
Lucas D. Ferreira
;
Willian D. Oliveira
;
Jose F. Rodrigues-Jr.
;
Robson L. F. Cordeiro
;
Caetano Traina Jr.
;
Agma J. M. Traina
and
Elaine P. M. Sousa
Affiliation:
University of Sao Paulo, Brazil
Keyword(s):
Crisis Situation, Crisis Management, Relational Database Management System, Similarity Query.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Group Decision Support Systems
;
Human-Computer Interaction
;
Multimedia Systems
;
Query Languages and Query Processing
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Strategic Decision Support Systems
Abstract:
Crowdsourcing solutions can be helpful to extract information from disaster-related data during crisis management. However, certain information can only be obtained through similarity operations. Some of them also depend on additional data stored in a Relational Database Management System (RDBMS). In this context, several works focus on crisis management supported by data. Nevertheless, none of them provide a methodology for employing a similarity-enabled RDBMS in disaster-relief tasks. To fill this gap, we introduce a methodology together with the Data-Centric Crisis Management (DCCM) architecture, which employs our methods over a similarity-enabled RDBMS. We evaluate our proposal through three tasks: classification of incoming data regarding current events, identifying relevant information to guide rescue teams; filtering of incoming data, enhancing the decision support by removing near-duplicate data; and similarity retrieval of historical data, supporting analytical comprehension
of the crisis context. To make it possible, similarity-based operations were implemented within one popular, open-source RDBMS. Results using real data from Flickr show that our proposal is feasible for real-time applications. In addition to high performance, accurate results were obtained with a proper combination of techniques for each task. Hence, we expect our work to provide a framework for further developments on crisis management solutions.
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