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-EnabledRelational Database Management System

in Civilian Crisis Situations

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Outline

Introduction Background The DCCM architecture Case Study

Quality ResultsOverall Performance

Conclusions

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Introduction – RESCUER project

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/

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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

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Introduction

Contributions1. Methodology for employing a similarity-

enabled RDBMS in disaster-relief tasks2. Data-Centric Crisis Management

(DCCM) architecture, which employs our methodology over a similarity-enabled RDBMS

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Introduction

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

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Outline

Introduction Background The DCCM architecture Case Study

Quality ResultsOverall Performance

Conclusions

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Background

Content-Based Retrieval Types of Similarity Queries Instance-Based Learning

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Background – Content-Based Retrieval

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Background – Similarity Queries

k-NN query Range query

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Background – Instance-Based Learning

k-NN Classifier

k = 3

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Outline

Introduction Background The DCCM architecture Case Study

Quality ResultsOverall Performance

Conclusions

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Scenario

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Range

query

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k-NN queryRange query

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Outline

Introduction Background The DCCM archtecture Case Study

Quality ResultsOverall Performance

Conclusions

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Case Study – Dataset Flickr-Fire (Bedo et. al, 2015)

1000 images containing fire1000 images not containing fire80 images for the Filtering task

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Case Study Similarity support on PostgreSQL

Our own implementation: Kiara

Recalling the methodology tasks1. Objects Classification2. Redundant Objects Filtering3. Retrieval of Historical Data

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Outline

Introduction Background The DCCM archtecture Case Study

Quality ResultsOverall Performance

Conclusions

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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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Results – Redundant Objects Filtering

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Results – Redundant Objects Filtering

Buffer: 80 images43 of one class and 37 of another class

Range queriesRange = 10

Feature Extractor: Perceptual Hash http://www.phash.org/

Evaluation Function: Hamming Distance

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Results – Redundant Objects Filtering

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Results – Retrieval of Historical Data

Color Structure Descriptor and Manhattan Distance (Bedo et. al, 2015)

Range queriesRange = 7.2

K-NN queriesk = 1000

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Results – Retrieval of Historical Data

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Outline

Introduction Background The DCCM archtecture Case Study

Quality ResultsOverall Performance

Conclusions

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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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Results – Performance (Tasks)

Task Average Time (s)1) Objects Classification 0,8512) Redundant Objects Filtering 0,0573a) Retrieval of Historical Data – Range 1,147 3b) Retrieval of Historical Data – kNN 0,849

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Results – Performance (Scalability)

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Outline

Introduction Background The DCCM archtecture Case Study

Quality ResultsOverall Performance

Conclusions

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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 tasksObjects ClassificationRedundant Objects FilteringRetrieval of Historical Data

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Conclusions By employing proper similarity

techniques (e.g. Feature Extractors, Evaluation Functions) to the crisis contextAccurate responseEfficient response

The DCCM architecture enables the use of well-known cutting-edge methods and technologies to aid in a critical scenario

On the Support of a Similarity-EnabledRelational 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