Transcript Handout_Database_Part2

Database – Part 2
Dr. V.T. Raja
Oregon State University
Outline
 Database Models
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Relational Model
 Some trends
 Additional
Terminology
 Why learn about databases?
Two Database Models
 From E-R diagram to Relational Model
 Normalization
 Process of minimizing data redundancy while
developing the database
 Object Oriented Model
 Object is like an entity with additional features;
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Encapsulation, Inheritance
Helpful in multimedia environments
 Design blueprints, photo images of parts, acoustics,
quality-control data etc.
Data Dictionary
Explains details about an attribute, such as the name of the
field, whether or not it is a primary key or part of a primary key,
the data type, and valid values for each field
The data dictionary could also explain (business rules) why the
data item is needed, how often it should be updated, who has
the authority to update it, and on which forms and reports the
data appears
Some trends in databases
 Centralized and Distributed databases (Partial and fully
replicated databases)
 Information resource management

DBA and Data Administration Staff
 Linking Web Site Applications to Organizational
Databases (E-commerce applications)
 Data mining
 Online Transaction/Analytical Processing (OLTP/OLAP)
 Data Warehouse and Data Marts
Data Warehouse and Data Mart
 Data Warehouse
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Multidimensional large database suitable for direct querying,
analysis, processing, or reporting; Stores current and
historical data
Integrate multiple, large databases and other information
sources into a single repository; Appears to the user as a
storehouse of valuable data from the organization’s
disparate information systems and, perhaps, from other
external sources
Involve hundreds of gigabytes, and terabytes of data
Run on very powerful computers
Expensive
 Data Mart

A small data warehouse containing only a portion of the
organization’s data for a specified function or population of
users. It is a subset of a data warehouse (e.g., marketing
and sales data mart)
Data Mining
 Data mining provides a means of extracting previously unknown, predictive
information from the data warehouse
 Data mining uses sophisticated, automated algorithms to discover hidden
patterns, relationship among data
 Some Benefits:
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Market Segmentation
 Identify common characteristics of customers who
purchase the same products
Fraud Detection
 Identify which transactions are most likely to be fraudulent
Market Basket Analysis
 Understanding what products/services are commonly
purchased together (e.g, Beer/Diapers)
Trend Analysis
 Reveals the difference between a typical customer this
year versus last year
On-line Transaction Processing (OLTP)
and On-line Analytical Processing (OLAP)
 OLTP: Immediate processing/analysis and handling
of multiple concurrent transactions from
customers/users
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Example: B-C E-Commerce
 OLAP: Capability for manipulating and analyzing
large volumes of data from multiple perspectives
(multidimensional analysis)
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Example: Product vs. Region vs. Sales
Why learn about databases?
 To reduce problems encountered with traditional file environment
 Improve productivity on personal and professional fronts
 Without support and understanding of management at different levels,
database efforts fail
 Budget vs. Cost
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Could be expensive in the long run
 Maintaining qualified DBA staff, Data Warehouse
 Information Resource Management
 Communicate effectively with database administrator/staff
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Data model should reflect key business processes and
decision-making requirements
Information Policy
Which current trends in database are important for your
unit/firm?
Smooth transition for newly hired DBA staff