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 provides a methodology for employing a similarity-enabled RDBMS in disaster-relief tasks. To fill this gap, we introduce a similarity-enabled methodology together with a supporting architecture named Data-Centric Crisis Management (DCCM), which employs our methods over a 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 the proposed methodology over DCCM is feasible for real-time applications. In addition to high performance, accurate results were obtained with a proper combination of techniques for each task. At last, given its accuracy and efficiency, we expect our work to provide a framework for further developments on crisis management solutions.

1. Luiz Olmes (speaker)
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, Elaine P. M. Sousa
pholiveira@usp.br
On the Support of a Similarity-Enabled
Relational Database Management System
in Civilian Crisis Situations

2. Outline
 Introduction
 Background
 The DCCM architecture
 Case Study
 Quality Results
 Overall Performance
 Conclusions
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3. Introduction – RESCUER project
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 The RESCUER project, a partnership between the
European Union and Brazil, aims at developing
solutions to improve decision-making in crises
 Further details: http://www.rescuer-project.org/

4. 4
Introduction
 Multimedia data
 Support decision-making during crises
 Automatic analysis on multimedia data
 Concepts related to similarity search
 Gap
 No well-defined methodology for applying
similarity search on crisis situations

5. Introduction
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 Contributions
1. Methodology for employing a similarity-
enabled RDBMS in disaster-relief tasks
2. Data-Centric Crisis Management (DCCM)
architecture, which employs our methodology
over a similarity-enabled RDBMS

6. Introduction
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 Evaluation of the contributions
 Task 1. Objects Classification
 Task 2. Redundant Objects Filtering
 Task 3. Retrieval of Historical Data

7. Outline
 Introduction
 Background
 The DCCM architecture
 Case Study
 Quality Results
 Overall Performance
 Conclusions
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8. Background
 Content-Based Retrieval
 Types of Similarity Queries
 Instance-Based Learning
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9. Background – Content-Based Retrieval
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10. Background – Similarity Queries
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k-NN query Range query

11. Background – Instance-Based Learning
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k-NN Classifier
k = 3

12. Outline
 Introduction
 Background
 The DCCM architecture
 Case Study
 Quality Results
 Overall Performance
 Conclusions
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13. Scenario
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Range
query

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k-NN query
Range query

30. Outline
 Introduction
 Background
 The DCCM archtecture
 Case Study
 Quality Results
 Overall Performance
 Conclusions
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31. Case Study – Dataset
 Flickr-Fire (Bedo et. al, 2015)
1000 images containing fire
1000 images not containing fire
80 images for the Filtering task
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32. Case Study
 Similarity support on PostgreSQL
Our own implementation: Kiara
 Recalling the methodology tasks
1. Objects Classification
2. Redundant Objects Filtering
3. Retrieval of Historical Data
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33. Outline
 Introduction
 Background
 The DCCM archtecture
 Case Study
 Quality Results
 Overall Performance
 Conclusions
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34. Results – Objects Classification
 Color Structure Descriptor and
Manhattan Distance (Bedo et. al, 2015)
 k-NN Classifier (k = 10)
 10-fold cross validation
 Accuracy: 86%
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35. Results – Redundant Objects Filtering
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36. Results – Redundant Objects Filtering
 Buffer: 80 images
 43 of one class and 37 of another class
 Range queries
 Range = 10
 Feature Extractor: Perceptual Hash
 http://www.phash.org/
 Evaluation Function: Hamming Distance
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37. Results – Redundant Objects Filtering
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38. Results – Retrieval of Historical Data
 Color Structure Descriptor and
Manhattan Distance (Bedo et. al, 2015)
 Range queries
 Range = 7.2
 K-NN queries
 k = 1000
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39. Results – Retrieval of Historical Data
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40. Outline
 Introduction
 Background
 The DCCM archtecture
 Case Study
 Quality Results
 Overall Performance
 Conclusions
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41. Results – Performance (Tasks)
 Classification
k-NN Classifier (k = 10)
 Filtering
Range query (range = 10)
 Retrieval of Historical Data
Range query (range = 2.8)
k-NN query (k = 50)
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42. Results – Performance (Tasks)
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Task Average Time (s)
1) Objects Classification 0,851
2) Redundant Objects Filtering 0,057
3a) Retrieval of Historical Data – Range 1,147
3b) Retrieval of Historical Data – kNN 0,849

43. Results – Performance (Scalability)
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44. Outline
 Introduction
 Background
 The DCCM archtecture
 Case Study
 Quality Results
 Overall Performance
 Conclusions
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45. Conclusions
 Methodology for employing similarity-enabled
RDBMS on crisis management
 The Data-Centric Crisis Management (DCCM)
architecture, based on such methodology
 Our methodology follows 3 tasks
 Objects Classification
 Redundant Objects Filtering
 Retrieval of Historical Data
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46. Conclusions
 By employing proper similarity techniques
(e.g. Feature Extractors, Evaluation Functions)
to the crisis context
 Accurate response
 Efficient response
 The DCCM architecture enables the use of
well-known cutting-edge methods and
technologies to aid in a critical scenario
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47. On the Support of a Similarity-Enabled
Relational Database Management System
in Civilian Crisis Situations
Thank you for your attention!
Luiz Olmes (speaker)
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, Elaine P. M. Sousa
pholiveira@usp.br