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Analyzing and Securing Social Networks

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

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Analyzing and Securing Social Networks

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  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) Security/ Integrity Manager Query 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 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

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

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

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

  12. Data Distribution S I T E 1 E M P 1 D E P T 1 D # S S # N a m e S a l a r y D # D n a m e M G R 1 0 1 J o h n 2 0 1 0 C . S c i . J a n e 2 0 2 P a u l 3 0 2 0 3 J a m e s 4 0 3 0 E n g l i s h D a v i d 2 0 4 J i l l 5 0 4 0 F r e n c h P e t e r 1 0 6 0 5 M a r y 2 0 6 J a n e 7 0 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 n a m e D # M G R 9 M a t h e w 7 0 5 0 5 0 J o h n M a t h 7 D a v i d 8 0 3 0 P h y s i c s P a u l 2 0 8 P e t e r 9 0 4 0

  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 Network Model Object- Oriented Model Relational Model Hierarchical 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 Data Warehouse: Data correlating Employees With Medical Benefits and Projects Users Query the Warehouse Could be any DBMS; Usually based on the relational data model Oracle DBMS for Employees Sybase DBMS for Projects Informix DBMS for Medical

  20. Information Harvesting Knowledge Mining Data Mining Knowledge Discovery in Databases Data Dredging Data Archaeology Data Pattern Processing Database Mining Knowledge Extraction 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) Data Mining

  21. Video and Metadata Multimedia Information Management Broadcast News Editor (BNE) Video Source Broadcast News Navigator (BNN) Correlation Scene Change Detection Story GIST Theme Broadcast Detection Frame Classifier Key Frame Selection Commercial Detection Imagery Silence Detection Story Segmentation Multimedia Database Management System Audio Speaker Change Detection Closed Caption Text Token Detection Named Entity Tagging Closed Caption Preprocess 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

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

  24. Knowledge Management Components Knowledge Components of Management: Components, Cycle and Technologies Cycle: Technologies: Components: Knowledge, Creation Expert systems Strategies Sharing, Measurement Collaboration Processes And Improvement Training Metrics Web

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