Download Information Fusion in Asymmetric Warfare

Fusion Technology Development for
Urban/Asymmetric Warfare:
Deductive and Inductive Approaches
Rakesh Nagi
Department of Industrial Engineering and
Center for Multisource Information Fusion (CMIF)
University at Buffalo, State University of New York at Buffalo
[email protected]
November 30, 2005
Center for Mutisource Information Fusion
Introduction: Problem Space Overview
Slide 2
• Representative modern-day asymmetric
problems:
• Urban warfare/Critical Infrastructure Attacks
• Improvised Explosion Devices (IEDs)
• Dirty bomb (Radiological weapon)
http://www.defendamerica.mil/
*http://icasualties.org/oif/IED.aspx
Unclassified: CUBRC/UB Proprietary
Introduction: Problem Space Overview
Slide 3
• An unexploded IED sits on the hood of a car as American
soldiers investigate at the site of an April attack on a U.S. convoy
north of Baghdad. (By Khalid Mohammed -- Associated Press)
Unclassified: CUBRC/UB Proprietary
Basic Questions
Slide 4
• How do we understand and classify these asymmetric threat?
• How do we develop Knowledge/Information Fusion Technology
to assist the analyst?
• How do we test the efficacy of the Fusion Technology to thwart
intended attacks?
Unclassified: CUBRC/UB Proprietary
Outline
Slide 5
• Introduction: Problem Space Overview
• Fusion Research Approach
• Problem and Domain Understanding
• Hybrid Deductive + Inductive
1. Problem and Domain Research
• Part 1.A: Scenario/Use Case
• Part 1.B: Operational Net Assessment
• Part 1.C: Ontology
2. Deductive or Model-based research
• Part 2.A: Information Fusion Engine for Real-time Decision Making (INFERD)
• Part 2.B: Graph Matching
3. Inductive or Data Mining and Knowledge Discovery research
• Part 3.A: Semantic Networks (SNePS)
• Part 3.B: Graph Data Mining
4. Integrated Software Architecture
• Discussion
Unclassified: CUBRC/UB Proprietary
Taxonomy of Asymmetric Warfare Problems
Slide 6
Asymmetric
Warfare
Physical
Improvised
Explosive
Devices
Urban
Warfare
Surgical
Mental
Unexploded
Ordinance
Precision
Suicide
Bombs
VehicleBorne
Weapons of
Mass
Destruction
Package
Type
Booby
Traps
Biologic
Political
Information
Reliability
Philosophic
Information
Availability
Couple
Single
Direct
Trigger
Electric
Chemical
Recover
Daisy Chain
Boosting
Multiple
Remote
Trigger
Pressure
Nuclear
Rescure
Rolling
Combat
Ref: “Asymmetric Warfare: A Conventional Classification Approach to
Understanding the Unconventional” CUBRC Report July 2005.
Policy
Information
Positional
AT
Raids
Technological
CyberThreat
Media
Dirty Bombs
Radio
Communicat
ion
Television
Intelligence
Electronic
Hacking
Print
Unclassified: CUBRC/UB Proprietary
Introduction: Problem Space Overview
Slide 7
• General Characteristics:
• Knowledge/Model-based approach viable for parts but
not all aspects of these problems
High Uncertainty
Deductive + Inductive
• Observability spotty, ambiguous
High Dimensionality
• Extensive data base requirements
High Risk
• High collateral damage environments
Unclassified: CUBRC/UB Proprietary
Fusion Research Approach
Realistic and
problem-oriented
approach
Scalable Scenario
Evidential Framework
For ONA
Interoperable and
formally designed
world views
Hybrid deductive
and inductive
approach due to
high uncertainty in
these environments
Research Approach
Problem and Domain
Understanding
Ontology
Slide 8
Deductive SA/IA
Model/Algorithmic
Inductive
SA/IA
Information Fusion
Engine for Real-time
Decision Making
(INFERD)
Graph Matching
Multi-perspective and
insightful “gaming”
approach
Integrated Software System
SNePS
KRR System
Graph
Data Mining
Adaptive: Hybrid Inferencing
Insightful: Multi-perspective
Interoperable: Onto-grounded
Unclassified: CUBRC/UB Proprietary
Deductive Approach
Slide 9
Domain Study
Historical Cases
Ontology
Development
SA/IA FUSION Technology
Software Environment
Scenario
Development
Integrated Simulation
SMEs
User
Forensic Methods:
e.g., Graph Matching
Unclassified: CUBRC/UB Proprietary
Inductive Approach
Slide 10
Historical Cases
Scenario
Development
Ontology
Development
SA/IA FUSION Technology
Integrated Simulation
SMEs
Software Environment
Knowledge Reasoning
and Representation
System
Domain Study
Knowledge
Ontology
Reasoning
translation to
KRR system
Text Mining
for Lexicon
Generation
Data
Mining
User
Forensic Methods:
e.g., Graph Matching
Integrated
Software System
Unclassified: CUBRC/UB Proprietary
Fusion Research Approach
Slide 11
Research Approach
Problem and Domain
Understanding
Scalable Scenario
Evidential Framework
For ONA
Deductive SA/IA
Model/Algorithmic
Information Fusion
Engine for Real-time
Decision Making
(INFERD)
Graph Matching
Ontology
Integrated Software System
Inductive
SA/IA
SNePS
KRR System
Graph
Data Mining
Unclassified: CUBRC/UB Proprietary
Scenario/Use Case
Slide 12
Motivation for Scenario Development
• To satisfy Use Case Requirements:
• Use Case Requirements
• Representative of modern-day military and/or security threat
• Scaleable to other “genre’s” of the Scenario
• To motivate fusion technology development
• Sufficiently complex to:
• Further test and develop existing fusion capability
• Motivate new, innovative fusion capabilities
• To provide basis for demonstration of ONA process
• Enough observational basis to allow multi-perspective fusion-based
inferencing
*Credits: Dr. James Llinas and Justin Yates, CUBRC/CMIF
Unclassified: CUBRC/UB Proprietary
Scenario/Use Case
Slide 13
Research approach
• Survey classes of “typical” modern-day threats
• Select a genre that is representative of a sufficiently wide class of
•
•
problems
Explore authoritative operational literature so Use Case script is
defendable/plausible
Frame script representation for both operational understanding
and use by fusion processes
• Cannot overemphasize the role of SMEs
Unclassified: CUBRC/UB Proprietary
Scenario/Use Case: Example
Slide 14
• Genre: “Coordinated small unit attack on Critical Infrastructure point target”
• Meta-genre: “Opportunity-constrained Threat”
• Phases:
• Reconnaissance
• Subtle, covert data gathering by Red
• Intell reconn by Blue
• Pre-Mission
• Solidification of Red plans to point of initial positioning
• Mission
•
•
•
•
•
•
Execution of point attack
Cyberattack included
Movement to contact
Execute diversion
Attack task execution
Immediate post-attack actions
• Pursuit
• Red dispersal, movement to escape
• Blue coordinated pursuit
Unclassified: CUBRC/UB Proprietary
The Nature of Critical Infrastructure Entities*
Slide 15
* GAO Report to Congress, “CRITICAL INFRASTRUCTURE PROTECTION”, GAO-05-434,
May 2005Proprietary
Unclassified: CUBRC/UB
Addtl Extensibility
Slide 16
• Analogous also to Base Defense
• US bases on foreign soil—Bases in Theater
• See Joint Pub 3-10.1
• Joint Tactics, Techniques, and Procedures for Base
Defense
Unclassified: CUBRC/UB Proprietary
Slide 17
Base Defense Threats*
Small Unit
Ops
* Joint Pub 3-10.1 Joint Tactics, Techniques, and Procedures for Base Defense
Unclassified: CUBRC/UB Proprietary
Scenario/Use Case
Slide 18
• Specific Case: Coordinated Insurgent attack on an Infrastructure
Facility to extract Fissile Materials for Use in WMD
• Urban location
• Typical of Critical Infrastructure Facilities
• Secure Research Institute (Typical of other Infrastructureembassies, Govt offices, etc)
• Coordinated, Multi-jurisdictional Defense and Pursuit
• Facility security staff (private, contractor-type)—Local
Police—Natl Intell
Unclassified: CUBRC/UB Proprietary
Cases of Fissile Material Diversions*
Slide 19
*from: “International Terrorist Threat to Nuclear Facilities”, Braun, C., et al, Amer. Nuclear Soc Winter 2002 Mtg,
Washington DC, Nov 2002
Unclassified: CUBRC/UB Proprietary
Link to Scenario
Operation Kharkiv Defense
TIME
Location
0.00
HQ
(25 miles from
El McKenna)
RED
True Activity
Commander and Platoon
leaders plan extraction
Center for Mutisource Information Fusion
True Activity
ISR
BLUE
Observed Activity
COMINT
IMINT
Observed Attributes
~interception of sporadic Red communic.
(observation + error)
Fusion Research Approach
Slide 21
Research Approach
Problem and Domain
Understanding
Scalable Scenario
Evidential Framework
For ONA
Deductive SA/IA
Model/Algorithmic
Information Fusion
Engine for Real-time
Decision Making
(INFERD)
Graph Matching
Ontology
Integrated Software System
Inductive
SA/IA
SNePS
KRR System
Graph
Data Mining
Unclassified: CUBRC/UB Proprietary
The Operational Net Assessment Concept*
Slide 22
Adaptive ISR Sensor
Management
MULTI-PERSPECTIVE
BLUE FUSION PROCESS
Blue View
of Blue
Cmdr's
Guidance
ISR
ASSETS
Blue Self-awareness
and Vulnerabilities
Blue View
of Red
Estimated
Red
COA
Inherent
Threat
Blue View
Perceived Threat
of (Red View of Blue)
by Red
Blue View
of (Red View of Red )
Embedded
Gaming
and
Route
Estimation
Red's Selfawareness
Nominated BLUE
Action
or
Resource Utilization
Effects
Analysis
Contextual
Data and
Information
* Biggie, J., Operational Net Assessment” brfg, JFCOM J9, Nov 2003,
http://www.mors.org/meetings/decision_aids/da_pres/Biggie.pdf
Unclassified: CUBRC/UB Proprietary
Slide 23
Biggie, J., Operational Net Assessment” brfg, JFCOM J9, Nov 2003,
http://www.mors.org/meetings/decision_aids/da_pres/Biggie.pdf
Unclassified: CUBRC/UB Proprietary
ONA—Evidential Growth Requirement
“PMESII” Evidence Space
•Political
•Military
•Economic
•Social
•Information
•Infrastructure
Slide 24
PMESII System Behavior Models
Economic/
Infrastructure
Information
Social/
Culture
Political/
Religious
Model/Activity
Interaction
Regular
Military
• Much more Holistic View
• Much better Adversarial Insight
• Technology Challenges:
-- Increased Combinatorics, hypothesis mgmt
-- Development and integration of large a priori info: Data base mgmt
-- Testing and Validation
Unclassified: CUBRC/UB Proprietary
Fusion Research Approach
Slide 25
Research Approach
Problem and Domain
Understanding
Scalable Scenario
Evidential Framework
For ONA
Deductive SA/IA
Model/Algorithmic
Information Fusion
Engine for Real-time
Decision Making
(INFERD)
Graph Matching
Ontology
Integrated Software System
Inductive
SA/IA
SNePS
KRR System
Graph
Data Mining
Unclassified: CUBRC/UB Proprietary
What Is Ontology?
Slide 26
Ontology
Philosophy
Theory-based:
Information Sciences
Application-based:
• Formal Ontology
• Logical Theory
• Doctrine of Hylomorphism
• Mind-Body Problem […]
Ontology
Epistemology
Both Are Needed
•Objs (X, Y,…)
•Attributes (p, q,…)
•Relations
•Events
FORMAL ONTOLOGY PROVIDES:
1.
Epistemic states:
X’s belief in Y (KR)
• SUO
• Species of OWL
• IDEF5
• Ontolingua
• Protégé 2000 […]
A Shared LEXICON of relevant terms.
2. A FORMAL STRUCTURE capable of capturing (i.e., representing)
all types of RELATIONS between terms within the lexicon.
3. A methodology for providing a CONSISTENT as well as a
COMPREHENSIVE representation of both physical and
non-physical items within a given domain.
Unclassified: CUBRC/UB Proprietary
Ontology Development Methodology
0. Utilize Text-Mining
Slide 27
1. DEVELOP A DOMAIN LEXICON
Software for Term
Extraction
The lexicon should contain a sufficiently large sample of terms which
represent those items found within a given domain.
Mine useful terms from
electronic documents for use in
constructing the initial domain
lexicon.
2. DEVELOP UPPER-ONTOLOGY CATEGORIES
4. Merge Ontology With
Domain-specific categories contain those (L1/L2 Fusion) from within a
specific spatio-temporal domain.
Cognitive Work Analysis
Upper-level categories contain highly abstract metaphysical items.
3. INTEGRATE DOMAIN-SPECIFIC CATEGORIES
CWA’s provide user-centric
and functionalistic domain info.
5. FORMALIZE ONTOLOGICAL RELATIONS
6. Integrate Ontology
7. CODE INTO COMPUTATIONAL LANGUAGE
with KR Tool for
Reasoning Over
Ontological Relations
Develop a computational language which captures necessary relations.
Reason over items and relations
within the ontology to aid in
improved discovery of relations.
Test ontology to assure its consistency and completeness. This process
assures the ontology remains relevant for a variety of applications.
Map relations between upper- and lower-level ontological items.
8. DEVELOP METHOD FOR EVALUATION
*Eric Little, CUBRC/CMIF
Unclassified: CUBRC/UB Proprietary
Methodology (cont.)
STEP
0
Slide 28
Lexical Terms
Of
Interest
E-Documents
DATA/TEXT
MINING
LEXICON
CONSTRUCTION
• nouns
• verbs
• Etc
Manual Checking
Evaluation
Procedure
STEP
7
STEP
1
Domain
Lexicon
(alphabetized
& organized)
Define Formal
Relations
STEP
4
Knowledge
Representation
Tool
STEP
2
Define Formal
Relations
SNAP
Ontology
(Spatial Items)
SPAN
Ontology
(Temporal Items)
Upper-Ontology
Reasoning over
Ontology
STEPS
5&6
STEP
3
Domain Ontology
(Organized Domain Lexicon)
Unclassified: CUBRC/UB Proprietary
Exemplary Skeletal Ontology Model From Domain
Terminology
Situational Item
(Attack on Nuclear Facility
including cyber attack)
Temporal Items (SPAN)
Temporal Region
Scattered
Disconnected Times
of Reports
System Updates
Connected
Instance
Attack event at
a given time
Interval
Attack event
over time
Processual Entity
Facility Infiltration
Reconnaissance
Mission
Pursuit
Cyber Attack
Recon
Privilege
Escalation
Intrusion
Time of Day
Day of Week
Time of Year
Fluid
Temporal
Boundary
Enemy clustering
Growing
Shrinking
Dependent Item
Substance
Civil Infrastructure
Facilities
Affected
Nuclear Facility
Cyber System
Quality
Damage
Agent
Plan
Civilian
Unaffected
Building Properties
Non-combatants.
Affected
Security
Unaffected
Police Facility
Setting
Spatial Items (SNAP)
Independent Item
Process
Slide 29
Combatants
Friendly Forces
Blue Base
Size, Materials, etc
Capacity
Facilities
Police
Cyber
Transportation
Systems
Road, Bridge, etc.
Road, Bridge, etc.
Situation = Spatial + Temporal
Components: Must Be Modeled
Independently
Unclassified: CUBRC/UB Proprietary
2. Deductive Research
Center for Mutisource Information Fusion
Fusion Research Approach
Slide 31
Research Approach
Problem and Domain
Understanding
Scalable Scenario
Evidential Framework
For ONA
Deductive SA/IA
Model/Algorithmic
Information Fusion
Engine for Real-time
Decision Making
(INFERD)
Graph Matching
Ontology
Integrated Software System
Inductive
SA/IA
SNePS
KRR System
Graph
Data Mining
Unclassified: CUBRC/UB Proprietary
INFERD:Technology Approach
Slide 32
Minimize Apriori Knowledge
Initial Knowledge of
Domain and Objectives
Target Graph Generation
Level 4
Sensor Location and Settings
Target
Graphs
Database
Physical/Virtual Domain of Interest
Sensors
Type 1
Sensors
Type 2
Real-Time
Level 0/1
Cleansing, Filtering and
Homogenizing Data
SA/IA Visualization
INFERD
Sensors
Type n
sensed data
SIGINT, COMINT,
HUMINT
SME
Automatic
Efficient Deployment
Adaptive
Learning
Data Graph
Generator
Level 2/3
Graph Matching
(Batch)
Multiple Formats
*Credits: Dr. Moises Sudit, CUBRC/CMIF
Decision Maker
Completeness
Unclassified: CUBRC/UB Proprietary
INFERD: Why a new approach?
Methodology
Slide 33
Advantages
Disadvantages
Parametric
- Portability
- Generality
- Need for a priori training process
- Accuracy variance
Rules Based
- Expressiveness
- Accuracy variance
- Rules sets become unwieldy
Probabilistic
- Known Distributions
- Generalizations
- Distribution Assumptions
- Inflexible
Graph Matching
-Robust
-Accurate
-Computational performance
-Suitable only for post facto analysis
INFERD
INformation Fusion Engine for
Real-time Decision-making
- Flexible
-Real-time
- Hierarchical
- No use of future information
- Dependent on good templates
Time Occurrences
Event
Rule Based Probabilistic
INFERD
Parametric
Real-Time
Graph Matching
Forensics
Unclassified:
CUBRC/UB Proprietary
System Architecture: INFERD + Graph Matching
Slide 34
INformation Fusion
Engine for Real-time
Decision-making
(INFERD)
Truncated Graph
Matching Heuristic
Unclassified: CUBRC/UB Proprietary
General Hierarchical Fusion Framework (INFERD)
Slide 35
• Graphical representation of
•
•
the elements that make up a
Template Graph (Attack
Track)
Each Template Node is
composed of a Feature Tree
Each Feature Node is
asserted via an L0/1 fusion
method on sensory data.
Unclassified: CUBRC/UB Proprietary
INFERD: L2 Computational Technology
Slide 36
• Depth of Template Measurement –
measures longest path
Ditj  (rhmax  {CFki (t , k ) : vk  Shmax }) /( H  1) i, j, t
ijt
• Breadth of Attack Measurement measures how much of the entire
possible scope of the template has
already taken place,
• Reliability of Attack Measurement measures how sure we are that this
particular template is actually happening
(Information Theory – Shannon)
(Generalized Entropy – Tsillas)
ijt
hijtmax
Btmax   Max { Sh  Vitj }
t
i 1,..., m jt
h 1
j 1,..., n
Bijt 

CFkij (t ,  k )
vk Vitj
i, j , t
max
t
B
Relative Entropy
H  H ( x)
 max
H max  H min
|N |

log(| N | 1)  log(| N |  CFijk  1)
k 1
log(| N | 1)
Unclassified: CUBRC/UB Proprietary
INFERD: Success in Asymmetric Domains
• IED Detection (CACI)
• Urban Warfare
(MIT LL, LMCO)
Slide 37
• BDA (Army)
• Chem/Bio (DTRA)
Unclassified: CUBRC/UB Proprietary
INFERD Capability in UW
Slide 38
Video shows a U.S. strike on Taliban forces in
Afghanistan.
Video taken from Predator UAV – used for
reconnaissance to provide real-time images for
attacks.
•Shows people and vehicles moving around
•Shows series of buildings including a mosque
•Can hear discussions between aircrews and ground controllers concerning
targeting
•They take special care as to protect the mosque
INFERD is placed over the video using a grid-like overlay to show its ability
to represent the situation at hand.
Unclassified: CUBRC/UB Proprietary
Demo: INFERD in UW Situational Assessment
Slide 39
Unclassified: CUBRC/UB Proprietary
INFERD and Predator’s Video
People
1
Manual Weapons
0
Power Mean
Collateral Concern
M j 1
Grid-box #27, Time interval #141
1/0p
1 n p
p
Target Structure
M j (ak : k Auto1,Weapons
2,..., n)    ak 
 n k 1 
Civilians
Vehicles
M jp
Slide 40
0
0
1
1
CF = 0.625
Function
Desctiption0.625
Credibility Factor
n
Harmonic Mean
n
1

k 1 ak
WA
0.75
0.25
1/ n
0
Mj
M 1j
M
2
j
M j
M j
 n

  ak 
 k 1 
1 n
 ak
n k 1
1 n 2
 ak
n k 1
max
min
0.5
WA
Root Mean Square
Minimum
Max ak
Maximum
k 1,..., n
1.0
Arithmetic Mean
Min ak
k 1,..., n
0.5
Geometric Mean
0.5
0.25
0.25
people
man.
weap.
vehicle
1.0
0
0
target
structure
auto.
weap.
civilian
0
0
1.0
collateral
1.0
Unclassified: CUBRC/UB Proprietary
Fusion Research Approach
Slide 41
Research Approach
Problem and Domain
Understanding
Scalable Scenario
Evidential Framework
For ONA
Deductive SA/IA
Model/Algorithmic
Information Fusion
Engine for Real-time
Decision Making
(INFERD)
Graph Matching
Ontology
Integrated Software System
Inductive
SA/IA
SNePS
KRR System
Graph
Data Mining
Unclassified: CUBRC/UB Proprietary
Graph Matching: Objectives
• Find
Slide 42
the “best” match to a template in the Data graph
Attributed Graph Structure G = (V, E, Av, AE)
Where V - the set of nodes;
Av - the set of node attributes;
E - the set of arcs;
Av - the set of arc attributes.
Unclassified: CUBRC/UB Proprietary
Graph Matching: 1-Hop Neighbor Matching
Slide 43
Y1,f1
Y2,f7
B3
B4
Y2
e1
Y3
B1,e3
Y3,f2
Y4
B2,e4
Y4,f3
Y5
B3,e1
Y5,f4
B2,e4
Y6,f5
e2
f7
Y1
f2
f1
A
f3
X
e3
B1
f4
f8
e4
Y8
f6
f5
B2
Y7
Y6
Y7,f6
Y8,f8
•
Algorithm
─ Step 1: Compute a node score, denoted as Cij , for each node in
the template graph to each node in the data graph.
─ Step 2: Compute the scores, denoted as Wij , for the 1-Hop
neighbors of each root node pair.
─ The score is given by  Cij + (1-) Wij
“” is the Score vs.
Topology Parameter.
Unclassified: CUBRC/UB Proprietary
Graph Matching: Truncated Greedy Algorithm
Slide 44
k0 = 3
ki = 3
i = 7
 =4
Unclassified: CUBRC/UB Proprietary
3. Inductive Research
Center for Mutisource Information Fusion
Fusion Research Approach
Slide 46
Research Approach
Problem and Domain
Understanding
Scalable Scenario
Evidential Framework
For ONA
Deductive SA/IA
Model/Algorithmic
Information Fusion
Engine for Real-time
Decision Making
(INFERD)
Graph Matching
Ontology
Integrated Software System
Inductive
SA/IA
SNePS
KRR System
Graph
Data Mining
Unclassified: CUBRC/UB Proprietary
What is SNePS?
Slide 47
• A Logic- and Network-based
• As
expressive
as higher-order
Efficient,
path-based
reasoning.logic
Knowledge Representation,
designed
for commonsense reasoning.
• but
Useful
for ontological
set of
rules of inference
reasoning
andlogical
graph-matching.
Reasoning, • Broad
but incomplete to improve tractability.
• Inconsistency detection and Belief Revision
and Acting system.
for Multisource Information Fusion.
• In existence/development
for 30 years,
• To make use of non-logic-based computations
such as efficient mathematical calculations.
with participation• by
more than 64 people.
Integrated reasoning/acting for intelligent agents.
• Clean syntax/semantics
• (Academic, not commercial,
software.)
•for reasoning rules,
•acting rules,
•and policies, that connect them.
*Credits: Dr. Stuart Shapiro, CSE
Unclassified: CUBRC/UB Proprietary
SNePS: Sample Applications (Prototypes)
Slide 48
• Neurological diagnosis (1984-86)
• An expert system for fault diagnosis (1986-88)
• CUBRICON: A multi-modal intelligent user interface to a tactical
•
•
•
•
Air Force mission planner (1988-94)
Foveal Extravehicular Activity Helper-Retriever robot(1992-96)
An unexploded ordinance recovery robot (1996-2001)
Truth Maintenance in Data Fusion for Situation Assessment
(1998-2003)
Intelligent agents in a Virtual Reality drama (2003-present)
Unclassified: CUBRC/UB Proprietary
Inductive Research Part A: SNePS
Slide 49
Research Objective
• To deploy SNePS as a KRR (Knowledge
Representation and Reasoning) tool
• For combined representing and reasoning about
• The Ontology
• The Data Graphs and Template Graphs
• To make the SNePS representations available
• For data mining
• Induction and testing of new templates
Unclassified: CUBRC/UB Proprietary
Fusion Research Approach
Slide 50
Research Approach
Problem and Domain
Understanding
Scalable Scenario
Evidential Framework
For ONA
Deductive SA/IA
Model/Algorithmic
Information Fusion
Engine for Real-time
Decision Making
(INFERD)
Graph Matching
Ontology
Integrated Software System
Inductive
SA/IA
SNePS
KRR System
Graph
Data Mining
Unclassified: CUBRC/UB Proprietary
Inductive Research Part B: Graph Mining
Slide 51
Research Objective
• Leverage data mining's capabilities
• Unsupervised mining (knowledge discovery)
• Supervised mining (classification)
• Integrate with data fusion at all levels
• Single graph or database of graphs
• Single graph algorithms work on databases
• Not vice versa
• Edge disjoint or overlapping?
• Overlapping increases size of search space
Edge disjoint subgraphs
Subgraph overlap
Unclassified: CUBRC/UB Proprietary
Taxonomy of Graph Mining Algorithms
Slide 52
Graph mining algorithms
Single graph
Frequent subgraphs
Complete
SEuS
Kuramochi et al
Database of graphs
Clustering
Heuristic
SUBDUE
DB-SUBDUE
Complete
Apriori-like
SUBDUE
Wu et al.
GBI
GREW
*Credits: Dr. Carol Romanowski
Frequent subgraphs
SVM
Non-Apriori
Inokuchi
ANF
Heuristic
gSpan
Vanetik et al.
CloseGraph
FSG
DSPM
ADI-MIne
Huan et al
LCGMiner
GraphMIne
Gaston
SPIN
Unclassified: CUBRC/UB Proprietary
Inductive Research Part B: Graph Mining
Slide 53
Research Approach
• Choosing/developing an algorithm is a data-driven activity
• Choose goal first
• Find frequent patterns?
• Find rare patterns?
• Find clusters?
• Exploit characteristics of input data
• Mitigate algorithmic challenges
• Suggest new avenues for solution
• Define and develop integrated data mining/data fusion
architecture
Unclassified: CUBRC/UB Proprietary
4. Integrated Software Architecture
Center for Mutisource Information Fusion
Integrated Software Architecture
Slide 55
Objective
• Implement the various modules that embody the
methods/technology
• Develop an integrated architecture to tie them together and for
seamless execution
• Demonstration of the software prototype
• Design with Transition Engineering principles
• Training
• Operational Use
Unclassified: CUBRC/UB Proprietary
Integrated Software Architecture
Slide 56
Approach
Scalable Scenario Authoring Tool
• Create a standard format to describe scenarios and scenario
data (scenario authoring tool for fusion technology
development).
Simulation Tool to “play out” scenario
TIME
Location
0.00
HQ
(25 miles from
El McKenna)
RED
True Activity
Commander and Platoon
leaders plan extraction
True Activity
ISR
BLUE
Observed Activity
COMINT
IMINT
Observed Attributes
~interception of sporadic Red communic.
(observation + error)
Unclassified: CUBRC/UB Proprietary
Integrated Software Architecture
Slide 57
User Interface for Scenario Simulation Tool
Unclassified: CUBRC/UB Proprietary
Integrated Software Architecture
Slide 58
Other Fuctions
Ontology and KRR
• Text mining for Ontology Lexicon development
• Enhance ontology representation and analysis by creating a
SNePS plugin for Protégé
INFERD and Graph Matching
• Implement software and integrate with deductive/inductive
fusion engines
• SNePS and Graph data mining integration
Unclassified: CUBRC/UB Proprietary
Demo: Ontology to KR System Translation
Slide 59
Unclassified: CUBRC/UB Proprietary
Integrated Software Architecture
Slide 60
Text mining support for Ontology building
Example of unsupervised text mining output
Chapter 3 of Joint Urban Operation Manual
Unclassified: CUBRC/UB Proprietary
Integrated Software Architecture
Text mining support for
Ontology building
E-Docs
Editing
dictionary
• Methodology:
• Unsupervised text mining
to find initial lexicon
terms
• Supervised text mining to
find specific concepts
• Iterative, semi-automatic,
collaborative approach
• Final result used as
framework for ontology
Slide 61
Unsupervised
text mining
Initial list
of terms
Classification
Refinement of terms
Further
refinement
Supervised
text mining
Final list
of terms
Consensus
Validated final list
of terms
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DISCUSSION
Center for Mutisource Information Fusion