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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

ISBN: 978-989-758-187-8

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. (More)

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Paper citation in several formats:
Oliveira P., Fraideinberze A., Laverde N., Gualdron H., Gonzaga A., Ferreira L., Oliveira W., Rodrigues-Jr. J., Cordeiro R., Traina Jr. C., Traina A. and Sousa E. (2016). On the Support of a Similarity-enabled Relational Database Management System in Civilian Crisis Situations.In Proceedings of the 18th International Conference on Enterprise Information SystemsISBN 978-989-758-187-8, pages 119-126. DOI: 10.5220/0005816701190126

@conference{iceis16,
author={Paulo H. Oliveira and Antonio C. Fraideinberze and Natan A. Laverde and Hugo Gualdron and Andre S. Gonzaga and Lucas D. Ferreira and Willian D. Oliveira and Jose F. Rodrigues-Jr. and Robson L. F. Cordeiro and Caetano Traina Jr. and Agma J. M. Traina and Elaine P. M. Sousa},
title={On the Support of a Similarity-enabled Relational Database Management System in Civilian Crisis Situations},
booktitle={Proceedings of the 18th International Conference on Enterprise Information Systems},
year={2016},
pages={119-126},
doi={10.5220/0005816701190126},
isbn={978-989-758-187-8},
}

TY - CONF

JO - Proceedings of the 18th International Conference on Enterprise Information Systems
TI - On the Support of a Similarity-enabled Relational Database Management System in Civilian Crisis Situations
SN - 978-989-758-187-8
AU - Oliveira P.
AU - Fraideinberze A.
AU - Laverde N.
AU - Gualdron H.
AU - Gonzaga A.
AU - Ferreira L.
AU - Oliveira W.
AU - Rodrigues-Jr. J.
AU - Cordeiro R.
AU - Traina Jr. C.
AU - Traina A.
AU - Sousa E.
PY - 2016
SP - 119
EP - 126
DO - 10.5220/0005816701190126

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