1
|
- Peter P. Chen
- Foster Distinguished Chair Professor
- Computer Science Dept.
- Louisiana State University
- Baton Rouge, LA 70803, USA
- pchen@lsu.edu
- http://www.csc.lsu.edu/~chen
|
2
|
- Examples: 9-11, Airport Security, D.C. snipers, Louisiana serial ki=
ller,
Ohio sniper, etc.
- Current Problems:
- Isolated Data
- Questionable data
- Little Mathematical Analysis
- Algorithms (if any) are independent of (or incompatible with) data
models
|
3
|
- 9-11
- D.C. snipers
- serial killers in Louisiana, California, etc.
- Ohio sniper, etc.
- Airport Security
|
4
|
|
5
|
- Black hair?
- Beard/moustache?
- Nationality: xxxx?
- Has traveled to Country X three times?
|
6
|
- black hair
- beard/moustache
|
7
|
- black hair
- beard/moustache
|
8
|
- black hair
- beard/moustache
|
9
|
|
10
|
|
11
|
|
12
|
|
13
|
- Current Problems:
- Isolated Data
- Questionable data
- Little Mathematical Analysis
- “Unscientific/Unproven” Methods
- Algorithms (if any) are independent of (or incompatible with) data
models
- Solution:
- Data “links” (“relationships”)
- Info validity and conflict resolution
- Optimization model & algorithms
- Integration of data model and algorithms
|
14
|
- Discovering „Links/Relationships“ from Data in Various
Sources (such as DARPA‘s EELD Program)
- „Auto“-construction of „Relationships“
- „Dynamically adjusting“ the weights of relationships
- Validity/Credibility Analysis of Data
- A Paper was published in InfoFusion 2001, Montreal
- Algorithm was developed
- Prototype developed
- Also, developed machine learning algorithm
|
15
|
- We Model the „profiling“ problem as a „generalize=
d set
covering problem“
- Start with the conventional definition of a „set covering pr=
oblem
(SCP)“
- Then, define a „weighted set covering problem“
- Finally, define a „generalized set covering problem“=
li>
- We have developed several efficient algorithms for solving this typ=
e of
problems. Some of the=
m are
modified versions of the „greedy algorithm“
- Based on our tests, these new algorithms perform better than other
algorithms in the SCP case
- We have also obtained and proved some computational complexity boun=
ds
|
16
|
|
17
|
|
18
|
|
19
|
|
20
|
|
21
|
- Each Si Î S
is associated with a weighted set Wi ÎW, where W =3D {W1,
W2, … , Wn} and Wi Í
G, 1 ≤ i =
804; n,
where G is a finite set.
Each element b Î B is
weighted.
A combination of weighted elements of B with an additional factor <=
font
face=3DSymbol>l enables a relaxation of t=
he
covering requirement.
|
22
|
|
23
|
|
24
|
|
25
|
|
26
|
|
27
|
|
28
|
|
29
|
|
30
|
|
31
|
|
32
|
|
33
|
|
34
|
|
35
|
|
36
|
|
37
|
|
38
|
|
39
|
|
40
|
|
41
|
|
42
|
- Take advantage of LSU’s NCSRT, one of the largest traini=
ng
centers of emergency and anti-terrorism workers
- Test the Models and algorithms with law enforcement agencies and o=
ther
agencies
- Test the data-model/math-model integration problems with real and
quasi-real data sets
|
43
|
- Integration of conceptual models (ER model, etc.) with databases, m=
ath
models
- New Machine Learning Techniques
- Trustworthiness of Data and Conflict Resolutions
- (High and low-level) System Architecture and Cyber Security
- Cost/Effective Assessments of Security Techniques -- Making real
impacts!
|
44
|
|