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Introduction to Data, Information and Knowledge
Management
Dr. Bhavani Thuraisingham
The University of Texas at Dallas
Data, Information and Knowledge Management
January 2014
Data Management
Concepts in database systems Types of database systems Distributed Data Management Heterogeneous database integration Federated data management
An Example Database System
Database
Database Management SystemApplicationPrograms
Users
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
User Interface Manager
QueryManager
Transaction Manager
Schema(Data Dictionary)Manager (metadata)
Security/IntegrityManager
FileManager
DiskManager
Data Management
Storage Management
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 CITYS1 Smith 20 LondonS2 Jones 10 ParisS3 Blake 30 ParisS4 Clark 20 LondonS5 Adams 30 Athens
Relation P:
P# PNAME COLOR WEIGHT CITYP1 Nut Red 12 LondonP2 Bolt Green 17 ParisP3 Screw Blue 17 RomeP4 Screw Red 14 LondonP5 Cam Blue 12 ParisP6 Cog Red 19 London
Relation SP:
S# P# QTYS1 P1 300S1 P2 200S1 P3 400S1 P4 200S1 P5 100S1 P6 100S2 P1 300S2 P2 400S3 P2 200S4 P2 200S4 P4 300S4 P5 400
Example Class Hierarchy
DocumentClass
D1 D2
Book Subclass
B1# of Chapters Volume #
Print-doc-att(ID)
Method1:
JournalSubclass
J1
Print-doc(ID)
Method2:
ID Name
Author
Publisher
Example Composite Object
CompositeDocument
Object
Section 1Object
Section 2Object
Paragraph 1Object
Paragraph 2Object
Distributed Database System
Communication NetworkDistributed Processor 1
DBMS 1
Data-base 1 Data-
base 3
Data-base 2 DBMS 2
DBMS 3
Distributed Processor 2
Distributed Processor 3
Site 1
Site 2
Site 3
Data Distribution
EMP1
SS# Name Salary
1 John 20 2 Paul 303 James 404 Jill 50
605 Mary6 Jane 70
D#
102020 201020
DnameD# MGR
10 30 40
Jane David Peter
DEPT1
SITE 1
SITE 2EMP2
SS# Name Salary9 Mathew 70
D#50
DnameD# MGR
50 Math John
Physics
DEPT2
David 80 30
Peter 90 40
7
8
C. Sci. English French
20 Paul
Interoperability of Heterogeneous Database Systems
Database System A Database System B
Network
Database System C(Legacy)
Transparent accessto heterogeneousdatabases - both usersand application programs;Query, Transactionprocessing
(Relational) (Object-Oriented)
Different Data Models
Node A Node B
Database Database
RelationalModel
NetworkModel
Node C
Database
Object-Oriented Model
Network
Node D
Database
HierarchicalModel
Developments: Tools for interoperability; commercial productsChallenges: Global data model
Federated Database Management
Database System A Database System B
Database System C
Cooperating databasesystems yet maintainingsome degree ofautonomy
Federation F1
Federation F2
Federated Data and Policy Management
ExportData/Policy
ComponentData/Policy for
Agency A
Data/Policy for Federation
ExportData/Policy
ComponentData/Policy for
Agency C
ComponentData/Policy for
Agency B
ExportData/Policy
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
OracleDBMS forEmployees
SybaseDBMS forProjects
InformixDBMS forMedical
Data Warehouse:Data correlatingEmployees WithMedical Benefitsand Projects
Could beany DBMS; Usually based on the relational data model
UsersQuerythe Warehouse
Data Mining
Data MiningKnowledge Mining
Knowledge Discoveryin Databases
Data Archaeology
Data Dredging
Database MiningKnowledge 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
VideoSource Scene
ChangeDetection
SpeakerChange
Detection
SilenceDetection
CommercialDetection
Key FrameSelection
StorySegmentation
NamedEntityTagging
Broadcast News Editor (BNE) Broadcast NewsNavigator (BNN)
Video and
Metadata
MultimediaDatabase
ManagementSystem
Web-based Search/Browse by Program, Person, Location, ...
Imagery
Audio
ClosedCaptionText
Segregate VideoStreams
Analyze and Store Video and Metadata
StoryGIST Theme
FrameClassifier
ClosedCaption
Preprocess
Correlation
Token Detection
BroadcastDetection
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
Semantic Web
0 Some Challenges: Security and Privacy cut across all layers
XML, XML Schemas
Rules/Query
Logic, Proof and TrustTRUST
OtherServicesRDF, Ontologies
URI, UNICODE
PRIVACY
0Adapted from Tim Berners Lee’s description of the Semantic Web
Knowledge Management Components
Components:StrategiesProcessesMetrics
Cycle:Knowledge, CreationSharing, Measurement And Improvement
Technologies:Expert systemsCollaborationTrainingWeb
Components ofKnowledge Management: Components,Cycle and Technologies
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