| Date post: | 14-Feb-2017 |
| Category: |
Data & Analytics |
| Upload: | jose-f-rodrigues-jr |
| View: | 18 times |
| Download: | 2 times |
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
On the Support of a Similarity-EnabledRelational Database Management System
in Civilian Crisis Situations
2
Outline
Introduction Background The DCCM architecture Case Study
Quality ResultsOverall Performance
Conclusions
3
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/
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
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
6
Introduction
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 ResultsOverall Performance
Conclusions
8
Background
Content-Based Retrieval Types of Similarity Queries Instance-Based Learning
9
Background – Content-Based Retrieval
10
Background – Similarity Queries
k-NN query Range query
11
Background – Instance-Based Learning
k-NN Classifier
k = 3
12
Outline
Introduction Background The DCCM architecture Case Study
Quality ResultsOverall Performance
Conclusions
13
Scenario
14
15
16
17
18
19
20
Range
query
21
22
23
24
25
26
27
28
29
k-NN queryRange query
30
Outline
Introduction Background The DCCM archtecture Case Study
Quality ResultsOverall Performance
Conclusions
31
Case Study – Dataset Flickr-Fire (Bedo et. al, 2015)
1000 images containing fire1000 images not containing fire80 images for the Filtering task
32
Case Study Similarity support on PostgreSQL
Our own implementation: Kiara
Recalling the methodology tasks1. Objects Classification2. Redundant Objects Filtering3. Retrieval of Historical Data
33
Outline
Introduction Background The DCCM archtecture Case Study
Quality ResultsOverall Performance
Conclusions
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%
35
Results – Redundant Objects Filtering
36
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
37
Results – Redundant Objects Filtering
38
Results – Retrieval of Historical Data
Color Structure Descriptor and Manhattan Distance (Bedo et. al, 2015)
Range queriesRange = 7.2
K-NN queriesk = 1000
39
Results – Retrieval of Historical Data
40
Outline
Introduction Background The DCCM archtecture Case Study
Quality ResultsOverall Performance
Conclusions
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)
42
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
43
Results – Performance (Scalability)
44
Outline
Introduction Background The DCCM archtecture Case Study
Quality ResultsOverall Performance
Conclusions
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 tasksObjects ClassificationRedundant Objects FilteringRetrieval of Historical Data
46
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
