Language Technologies for Geomatics: From Intelligence to Agility

1. Language
Technologies for
Geomatics: From
Intelligence to Agility
Vision Géomatique - 2014-11-12
Stéphane Gagnon, Ph.D.
Professeur, DSA, UQO

2. Outline
1. Business Intelligence
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2. Language Technologies
3. Geomatics Applications
4. Big Data and Geo-Agility

3. Abstract
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 Language Technologies are used for automated text
analytics, and rely on a blend of Linguistics, Artificial
Intelligence (AI), and Decision Sciences.
 They include such applications as content
management, document indexing and search, text
classification, automated translation, geographic and
contextual localization, semantic web, real-time text
stream processing, event patterns analysis, and others.
 We present a brief discussion of how Language
Technologies may be integrated with geomatics
applications, not simply to enhance business and
decisional intelligence, but with the aim of making
organizations more agile and resilient in the face of risk
and uncertainty.

4. Sources
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 Baccalauréat en administration - Systèmes d'information de gestion
 SIG1003 - Systèmes d'information pour gestionnaires
 Efraim Turban, Linda Volonino, Gregory Wood, et Janice Sipior,
(2013), Information technology for management: Advancing
sustainable, profitable business growth, 9th edition, New York,
Wiley, 480 pages, ISBN: 9781118547861
 SIG1043 - Intelligence d’affaires
 Ramesh Sharda, Dursun Delen, Efraim Turban, (2013), Business
Intelligence: A Managerial Perspective on Analytics, CourseSmart
eTextbook, 3rd edition, New York, Pearson Higher Education, 330
pages, ISBN: 9780133051070

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Stéphane Gagnon
1. Business Intelligence
Stéphane Gagnon

6. Goals of BI
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7. BI Evolution
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Modern BI Dashboard

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BI Project Lifecycle

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Typical BI Architecture
Data
Data Warehouse
Environment
ü Organizing Warehouse
BPM strategy
ü Summarizing
ü Standardizing
Technical staff
Data
Sources
Business Analytics
Environment
Performance and
Strategy
Business users Managers / executives
Built the data warehouse Access
Manipulation
Results
Future component
intelligent systems
User Interface
- browser
- portal
- dashboard

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BI Data Management

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2. Language Technologies
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Data Mining (DM)
Statistics
Artificial Intelligence
Pattern
Recognition
Machine
Learning
DATA
MINING
Databases
Mathematical
Modeling
Management Science &
Information Systems

14. DM Tasks
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Data Mining
Prediction
Classification
Regression
Association
Link analysis
Sequence analysis
Clustering
Learning Method Popular Algorithms
Supervised
Supervised
Supervised
Unsupervised
Unsupervised
Unsupervised
Unsupervised
Classification and Regression Trees,
ANN, SVM, Genetic Algorithms
Decision trees, ANN/MLP, SVM, Rough
sets, Genetic Algorithms
Linear/Nonlinear Regression, Regression
trees, ANN/MLP, SVM
Apriory, OneR, ZeroR, Eclat
Expectation Maximization, Apriory
Algorithm, Graph-based Matching
Apriory Algorithm, FP-Growth technique
K-means, ANN/SOM
Outlier analysis Unsupervised K-means, Expectation Maximization (EM)

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Language Technologies
Statistical Methods
 Analyze documents as bags of
words
Semantic Methods
 Analyze documents using tags from
ontologies describing relationships

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Statistical Methods
Information retrieval/search
Topic/keyword tracking
Geo-language recognition
Categorization/classification
Clustering/recommendation
Concept linking/association rules

17. Semantic Methods
Natural Language Processing (NLP)
 Part-of-speech tagging
 Text segmentation
Word sense disambiguation
 Syntax ambiguity
 Imperfect or irregular input
 Speech acts
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18. NLP Tasks
 Information extraction
 Named-entity recognition
 Question answering
 Automatic summarization
 Natural language generation & understanding
 Machine translation
 Foreign language reading & writing
 Speech recognition
 Text proofing, optical character recognition
 Sentiment analysis
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Text Mining (TM) Process
Task 1 Task 2 Task 3
Establish the Corpus:
Collect & Organize the
Domain Specific
Unstructured Data
Create the Term-
Document Matrix:
Introduce Structure
to the Corpus
Extract Knowledge:
Discover Novel
Patterns from the
T-D Matrix
The inputs to the process
includes a variety of relevant
unstructured (and semi-structured)
data sources such
as text, XML, HTML, etc.
The output of the Task 1 is a
collection of documents in
some digitized format for
computer processing
The output of the Task 2 is a
flat file called term-document
matrix where the cells are
populated with the term
frequencies
The output of Task 3 is a
number of problem specific
classification, association,
clustering models and
visualizations
Feedback Feedback

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Enterprise Index/Search

21. Web Mining
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Web
Analytics
Voice of
Customer
Customer Experience
Management
Customer Interaction
on theWeb
Analysis of Interactions Knowledge about the Holistic
View of the Customer

22. IBM Watson QA
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Trained
models
Question
analysis
Answer
sources
Hypothesis
generation
Query
decomposition
Soft
filtering
Evidence
sources
Hypothesis and
evidence scoring
Synthesis
Final merging
and ranking
Answer and
confidence
Hypothesis
generation
Soft
filtering
Hypothesis and
evidence scoring
... ... ...
Primary
search
Candidate
answer
generation
Support
evidence
retrieval
Deep
evidence
Question scoring
1
2
3
4
5

23. TM for Lies
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Statements
Transcribed for
Processing
Text Processing
Software Identified
Cues in Statements
Statements Labeled as
Truthful or Deceptive
By Law Enforcement
Text Processing
Software Generated
Quantified Cues
Classification Models
Trained and Tested on
Quantified Cues
Cues Extracted &
Selected

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3. Geomatics Applications
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25. Geo-Analytics
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Geo-Textual Contextualization
Geo-Information Sensors
Software/hardware limitations
Privacy issues
Linguistic limitations
Extract
knowledge
from available
data sources
A0
Unstructured data (text)
Structured data (databases)
Context-specific knowledge
Geo-Localized Contents
Domain expertise
Tools and techniques
Geographic Information
Geo-Intelligence Models

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Geo-Social Network Analysis

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Geo-Analytics of Voter Talk
INPUT: Data Sources
§ Census data
Population specifics, age,
race, sex, income, etc.
§ Election Databases
Party affiliations, previous
election outcomes, trends
and distributions
§ Market research
Polls, recent trends and
movements
§ Social media
Facebook, Twitter, LinkedIn,
Newsgroups, Blogs, etc.
§ Web (in general)
Web pages, posts and
replies, search trends, etc.
· Other data sources
OUTPUT: Goals
§ Raise money contributions
§ Increase number of
volunteers
§ Organize movements
§ Mobilize voters to get out
and vote
§ Other goals and objectives
§ ...
Big Data & Analytics
(Data Mining, Web Mining, Text
Mining, Multi-media Mining)
§ Predicting outcomes and
trends
§ Identifying associations
between events and
outcomes
§ Assessing and measuring
the sentiments
§ Profiling (clustering) groups
with similar behavioral
patterns
§ Other knowledge nuggets

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Geo-Contextualized
Text and Voice
Messages

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Geo-Analytics of Test Reports

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4. Big Data and Geo-Agility
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Competitive Advantage

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Pressures for Agility

34. BI and Agility
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 Process efficiency and cost reduction
 Brand management
 Revenue maximization, cross-selling/up-selling
 Enhanced customer experience
 Churn identification, customer recruiting
 Improved customer service
 Identifying new products and market opportunities
 Risk management
 Regulatory compliance
 Enhanced security capabilities

35. Big Data - Definition
 Big Data means different things to people
with different backgrounds and interests
 Traditionally, “Big Data” = Giga and Tera
 E.g., volume of data at CERN, NASA, Google, …
 The Vs that define Big Data
 Volume
 Variety
 Velocity
 Veracity
 Variability
 Value
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Stéphane Gagnon
Big Data Examples
Data Sources
 Web text documents
 Multimedia annotations
 Web logs
 RFID
 GPS systems
 Sensor networks
 Social networks
 Internet search indexes
 Detail call records
Application Domains
 Financial markets
 Broadcasting
 Biology and life sciences
 Healthcare informatics
 Transportation
 Security and defense
 Atmospheric science
 Genomics and research
 Energy and SCADA

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Big Data Architecture
Marketing
Applications
Business
Intelligence
Data
Mining
Math
and Stats
Languages
ANALYTIC
Customers
Partners
Business
Analysts
Data
Scientists
TOOLS & APPS USERS
UNIFIED DATA ARCHITECTURE
MOVE MANAGE ACCESS
INTEGRATED
DATA WAREHOUSE
DISCOVERY PLATFORM
DATA
PLATFORM
System Conceptual View
Marketing
Executives
Operational
Systems
Frontline
Workers
Engineers
EVENT
PROCESSING
ERP
ERP
SCM
CRM
Images
Audio
and Video
Machine
Logs
Text
Web and
Social
BIG DATA
SOURCES

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Big Data Requirements
A Clear
business need
Keys to Success
with Big Data
Analytics
Strong,
committed
sponsorship
Alignment
between the
business and IT
strategy
A fact-based
decision-making
culture
Personnel with
advanced
analytical skills
A strong data
infrastructure
The right
analytics tools

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Conclusion: Toward Geo-Agility
 People-Centric: Track geo-information from key
individuals and assets across/around the enterprise
 Contextualize: Add geo-info to unstructured contents,
use DM and TM with geo-analytics
 Exploration: Link contextualized geo-info to real-time
decision requirements
 Open: Leverage open and mobile sources
 Big Data: Make real-time streaming capabilities
 Event-Driven: Develop organization agility and resilience,
capability to automate adaptation
 Emergent Strategies: Adapt business strategy along with
evidence-based decision-making

40. Merci!
Stéphane Gagnon, Ph.D.
Professeur agrégé
Département des sciences administratives
Université du Québec en Outaouais
Pavillon Lucien-Brault
101, rue St-Jean-Bosco, Local A2228
C.P. 1250, succursale Hull
Gatineau (Québec) J8X 3X7
Téléphone: 819-595-3900, poste 1942
Télécopieur: 819-773-1747
Courriel: stephane.gagnon@uqo.ca
Web: http://www.gagnontech.org
Skype: stephanegagnon
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Crédits des photos: SJ: http://www.lgt.ws, AT et LB: http://www.flickr.com/photos/uqo/