1. Analyzing and Securing Social Networks
Dr. Bhavani Thuraisingham
The University of Texas at Dallas
Lecture #4
Data, Information and Knowledge Management
January 25, 2013

2. Data Management Concepts in database systems Types of database systems
Distributed Data Management
Heterogeneous database integration
Federated data management

3. An Example Database System
Adapted from C. J. Date, Addison Wesley, 1990

4. Metadata Metadata describes the data in the database
Example: Database D consists of a relation EMP with attributes SS#, Name, and Salary
Metadatabase stores the metadata
Could be physically stored with the database
Metadatabase may also store constraints and administrative information
Metadata is also referred to as the schema or data dictionary

5. Functional Architecture
Data Management
User Interface Manager
Schema
(Data Dictionary)
Manager
(metadata)
Query
Manager
Security/
Integrity
Manager
Transaction Manager
Storage Management
File
Manager
Disk
Manager

6. DBMS Design Issues Query Processing Optimization techniques
Transaction Management
Techniques for concurrency control and recovery
Metadata Management
Techniques for querying and updating the metadatabase
Security/Integrity Maintenance
Techniques for processing integrity constraints and enforcing access control rules
Storage management
Access methods and index strategies for efficient access to the database

7. Types of Database Systems
Relational Database Systems
Object Database Systems
Deductive Database Systems
Other
Real-time, Secure, Parallel, Scientific, Temporal, Wireless, Functional, Entity-Relationship, Sensor/Stream Database Systems, etc.

8. Relational Database: Example
Relation S:
S# SNAME STATUS CITY
S1 Smith London
S2 Jones Paris
S3 Blake Paris
S4 Clark London
S5 Adams Athens
Relation P:
P# PNAME COLOR WEIGHT CITY
P1 Nut Red London
P2 Bolt Green Paris
P3 Screw Blue Rome
P4 Screw Red London
P5 Cam Blue Paris
P6 Cog Red London
Relation SP:
S# P# QTY
S1 P
S1 P
S1 P
S1 P
S1 P
S1 P
S2 P
S2 P
S3 P
S4 P
S4 P
S4 P

9. Example Class HierarchyID
Name
Author
Publisher
Document
Class D1 D2
Method1:
Method2:
Print-doc-att(ID)
Print-doc(ID)
Journal
Subclass
Book Subclass
# of Chapters
Volume # B1 J1

10. Example Composite Object
Document
Object
Section 2
Object
Section 1
Object
Paragraph 1
Object
Paragraph 2
Object

11. Distributed Database System
Communication Network
Distributed
Processor 1
DBMS 1
Data-
base 1
base 3
base 2
DBMS 2
DBMS 3
Processor 2
Processor 3
Site 1
Site 2
Site 3

12. Data Distribution S I T E 1 E M P 1 D E P T 1 S S # N a m e S a l a ry D # D # D n a m e M G R 1 J o h n 2 1 1 C . S c i . J a n e 2 P a u l 3 2 3 J a m e s 4 2 3 E n g l i s h D a v i d 4 J i l l 5 2 5 M a r y 6 1 4 F r e n c h P e t e r 6 J a n e 7 2 S I T E 2 E M P 2 D E P T 2 S S # N a m e S a l a r y D # D # D n a m e M G R 9 M a t h e w 7 5 5 M a t h J o h n 7 D a v i d 8 3 P h y s i c s P a u l 8 P e t e r 9 4 2

13. Interoperability of Heterogeneous Database Systems
Database System A
Database System B
(Relational)
(Object-
Oriented)
Network
Transparent access
to heterogeneous
databases -
both users
and application
programs;
Query, Transaction
processing
Database System C
(Legacy)

14. Different Data Models Network Node A Node B Node C Node D
Database
Database
Database
Database
Relational
Model
Network
Model
Hierarchical
Model
Object-
Oriented Model
Developments: Tools for interoperability; commercial products
Challenges: Global data model

15. Federated Database Management
Database System A
Database System B
Federation F1
Cooperating database
systems yet maintaining
some degree of
autonomy
Federation F2
Database System C

16. Federated Data and Policy Management
Data/Policy for Federation
Export
Export
Data/Policy
Data/Policy
Export
Data/Policy
Component
Component
Data/Policy for
Data/Policy for
Agency A
Agency C
Component
Data/Policy for
Agency B

17. Outline of Part I: Information Management
Information Management Framework
Information Management Overview
Some Information Management Technologies
Knowledge Management

18. What is Information Management?
Information management essentially analyzes the data and makes sense out of the data
Several technologies have to work together for effective information management
Data Warehousing: Extracting relevant data and putting this data into a repository for analysis
Data Mining: Extracting information from the data previously unknown
Multimedia: managing different media including text, images, video and audio
Web: managing the databases and libraries on the web

19. Data Warehouse Users Query the Warehouse Data Warehouse:
Data correlating
Employees With
Medical Benefits
and Projects
Could be
any DBMS;
Usually based on
the relational
data model
Oracle
DBMS for
Employees
Sybase
DBMS for
Projects
Informix
DBMS for
Medical

20. Data Mining Information Harvesting Knowledge Mining Data Mining
Knowledge Discovery
in Databases
Data Archaeology
Data Dredging
Database Mining
Knowledge Extraction
Data Pattern Processing
Information Harvesting
Siftware
The process of discovering meaningful new correlations, patterns, and trends by sifting through large amounts of data, often previously unknown, using pattern recognition technologies and statistical and mathematical techniques
(Thuraisingham 1998)

21. Multimedia Information Management
Broadcast News Editor (BNE)
Video
Source
Broadcast News
Navigator (BNN)
Scene
Change
Detection
Correlation
Story
GIST Theme
Broadcast
Detection
Frame
Classifier
Commercial
Detection
Key Frame
Selection
Imagery
Silence
Detection
Story
Segmentation
Multimedia
Database
Management
System
Audio
Speaker
Change
Detection
Closed
Caption
Text
Token
Detection
Video
and
Metadata
Closed
Caption
Preprocess
Named
Entity
Tagging
Segregate
Video
Streams
Analyze and Store Video and Metadata
Web-based Search/Browse by
Program, Person, Location, ...

22. Image Processing: Example: Change Detection:
Trained Neural Network to predict “new” pixel from “old” pixel
Neural Networks good for multidimensional continuous data
Multiple nets gives range of “expected values”
Identified pixels where actual value substantially outside range of expected values
Anomaly if three or more bands (of seven) out of range
Identified groups of anomalous pixels
Started with two known anomalies (ship, circle) -- third (parking lot) detected by algorithm.
Neural net takes 7 bands of old as input, 7 bands of new as output.
Multiple nets trained, gives range of predictions. Width of range gives measure of how “confident” the prediction is.
Measure ratio of actual-predicted to range, if high is anomaly
High defined as 4 standard deviations above average ratio for that band
Anomalous pixel if 3 or more anomalous bands
Anomalous region if 6 or more bad pixels in 3x3 block.

23. Semantic Web
Adapted from Tim Berners Lee’s description of the Semantic Web
XML, XML Schemas
Rules/Query
Logic, Proof and Trust
TRUST
Other
Services
RDF, Ontologies
URI, UNICODE P R I V A C Y
Some Challenges: Security and Privacy cut across all layers

24. Knowledge Management Components
Components of
Management:
Components,
Cycle and
Technologies
Cycle:
Technologies:
Components:
Knowledge, Creation
Expert systems
Strategies
Sharing, Measurement
Collaboration
Processes
And Improvement
Training
None of these things were endorsed by military acquisitions, but all have gradually started happening out of necessity and user requirements.
Metrics
Web