Download Related Concepts Database Systems Relational Database Models

Related Concepts
Data Mining
Goal: Examine some areas which are related to
data mining.
CS 341, Spring 2007
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Lecture 2: Related Concepts (I)
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Database Systems
Decision Support System
Data Warehousing
Fuzzy Sets and Logic
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Database Systems
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Relational Database Models
Database
Based on the relational model developed by
E.F. Codd in 1970
Data and relationship between them are
organized in tables. Properties of Relational
Tables:
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– A collection of related records
– The conceptual view: table with rows and columns
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Schema
– A structural description of the type of facts held in a
database
– E.g. (EmployeeID
(EmployeeID,, Name, Address, Salary, JobNo)
JobNo)
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– Your own example?
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Database models : modeling database
structure
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ProdID
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Relation: A rectangular table
– Attribute: A column in the table
– Tuple:
Tuple: A row in the table
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A Relation Containing product
information
Relational Database Model
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Values Are Atomic
Each Row is Unique
Column Values Are of the Same Kind
The Sequence of Columns is Insignificant
The Sequence of Rows is Insignificant
Each Column Has a Unique Name
Dominant in commercial data processing
systems
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LocID
Dallas
Houston
Dallas
Dallas
Fort
Worth
Chicago
Seattle
Rochester
Bradenton
Chicago
Date
022900
020100
031500
031500
021000
Quantity
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1
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UnitPrice
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20
100
95
80
012000
030100
021500
022000
012000
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A relation containing
redundancy
A relation containing
employee information
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An employee database
consisting of three relations
Relational Operations
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The SELECT operation
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Select: Choose rows
Project: Choose columns
Join: Assemble information from two or
more relations
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The PROJECT operation
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Another example of the
JOIN operation
The JOIN operation
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An application of the JOIN
operation
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Structured Query Language
(SQL)
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Operations to manipulate tuples
– insert
– update
– delete
– select
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SQL Examples
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SQL Examples (continued)
select EmplId,
EmplId, Dept
from ASSIGNMENT, JOB
where ASSIGNMENT.JobId = JOB.JobId
and ASSIGNMENT.TermData = “*”
insert into EMPLOYEE
values (‘
(‘43212’
43212’, ‘Sue A. Burt’
Burt’,
’33 Fair St.’
St.’, ‘444661111’
444661111’)
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delete from EMPLOYEE
where Name = ‘G. Jerry Smith’
Smith’
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update EMPLOYEE
set Address = ‘1812 Napoleon Ave.’
Ave.’
where Name = ‘Joe E. Baker’
Baker’
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Maintaining Database
Integrity
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Maintaining database
integrity (continued)
Transaction: A sequence of operations that must
all happen together
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– Incorrect summary problem
– Lost update problem
– Example: transferring money between bank accounts
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Transaction log: A nonnon-volatile record of each
transaction’
transaction’s activities, built before the
transaction is allowed to execute
– Commit point: The point at which a transaction has
been recorded in the log
– RollRoll-back: The process of undoing a transaction
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Simultaneous access problems
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Locking = preventing others from
accessing data being used by a transaction
– Shared lock: used when reading data
– Exclusive lock: used when altering data
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Other Database Models
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Database Systems
Hierarchical model
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Network model
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» More than 1 parent per child, m : m mapping
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» Add database functionality to object
programming language.
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The conceptual layers of a
database implementation
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Database applications
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ObjectObject-oriented model
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A relational database management system
– A software package used to create a database
(Oracle, Microsoft SQL sever, MYSQL)
» a tree structure, parentparent-child relationships, 1:m
mapping
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Human resource management system
Sales management system
Inventory management system
Decision support system
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A database vs. a file
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Decision Support System
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What is a Data Warehouse
Computer systems and related tools that
assist managers in making decisions and
solving problems.
Build upon database systems,
systems, provide
specific information needed by management
More ad hoc and customized information
DSS may use data mining tools on a data
warehouse
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DM: May access data in
warehouse.
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SubjectSubject-oriented
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– Data related to the same event or object are linked
together
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timetime-variant
– Changes to the data in database are tracked and
recorded
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Nonvolatile
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Integrated
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– Data in ware house never be deleted or changed
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Operational Data
Data Warehouse
OLTP
Precise Queries
Snapshot
Dynamic
Application
Operational Values
Gigabits
Detailed
Often
Few Seconds
Relational
OLAP
Ad Hoc
Historical
Static
Business
Integrated
Terabits
Summarized
Less Often
Minutes
Star/Snowflake
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Data mining tools often access data
warehouses rather than operational data.
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Operational vs. Informational
Application
Use
Temporal
Modification
Orientation
Data
Size
Level
Access
Response
Data Schema
Operational Data: Data used in day to day
needs of company.
Informational Data: Supports other functions
such as planning and forecasting.
DM: May access data in
warehouse.
– Contains data from all applications for an
organization.
– Keep consistent
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Operational Data vs.
Informational Data
What is a Data Warehouse
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The main repository of the organization's
historical data
Contains the raw material for
management's decision support system
The data warehouse is optimized for
reporting and analysis
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OLAP
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Online Analytic Processing (OLAP): provides more
complex queries than OLTP.
OnLine Transaction Processing (OLTP): traditional
database/transaction processing.
Dimensional data; cube view
Visualization of operations:
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Slice: examine subsub-cube.
Dice: rotate cube to look at another dimension.
Roll Up/Drill Down
DM: May use OLAP queries.
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Fuzzy Sets and Logic
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Fuzzy Sets
Fuzzy Set: Set membership function is a real valued
function with output in the range [0,1].
f(x): Probability x is in F.
1-f(x): Probability x is not in F.
EX:
– T = {x | x is a person and x is tall}
– Let f(x) be the probability that x is tall
– Here f is the membership function
DM: Prediction and classification are fuzzy.
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Classification/Prediction is
Fuzzy
Next Lecture:
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Loan
Reject
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Information Retrieval, Question
Answering, Web Search
Reading assignments: Chapter 2
Reject
Amnt
Accept
Accept
Simple
Fuzzy
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