Analyzing and Securing Social Networks Dr. Bhavani Thuraisingham The University of Texas at Dallas Lecture #4 Data, Information and Knowledge Management January 25, 2013
Data Management Concepts in database systems Types of database systems Distributed Data Management Heterogeneous database integration Federated data management
An Example Database System Adapted from C. J. Date, Addison Wesley, 1990
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
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
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
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.
Relational Database: Example Relation S: S# SNAME STATUS CITY S1 Smith 20 London S2 Jones 10 Paris S3 Blake 30 Paris S4 Clark 20 London S5 Adams 30 Athens Relation P: P# PNAME COLOR WEIGHT CITY P1 Nut Red 12 London P2 Bolt Green 17 Paris P3 Screw Blue 17 Rome P4 Screw Red 14 London P5 Cam Blue 12 Paris P6 Cog Red 19 London Relation SP: S# P# QTY S1 P1 300 S1 P2 200 S1 P3 400 S1 P4 200 S1 P5 100 S1 P6 100 S2 P1 300 S2 P2 400 S3 P2 200 S4 P2 200 S4 P4 300 S4 P5 400
Example Class Hierarchy ID 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
Example Composite Object Document Object Section 2 Object Section 1 Object Paragraph 1 Object Paragraph 2 Object
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
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 r y 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
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)
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
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
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
Outline of Part I: Information Management Information Management Framework Information Management Overview Some Information Management Technologies Knowledge Management
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
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
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)
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, ...
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.
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
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