Transcript Analyzing User Query Needs

Analyzing User Query
Needs
Chapter 6
Types of Users
Executives
Managers
Business analysts
User Access
Types of Users
Executives
Casual users
or managers
Business
analysts or
power users
Structured
Unstructured
Gathering User
Requirements
Areas to focus:
How users do business and what the business
drivers are
What attributes users need (required versus
good to have)
What are the business hierarchies are
What data users use and what they like to have
What levels of detail or summary needed
What types of front-end data access tool used
How users expect to see the query results
Gathering User Requirements:
Possible Obstacles
The following are some of the possible
obstacles:
Business objective of the data warehouse
has not been specifically defined
Scope of the data warehouse is too broad
Misunderstanding about the purpose and
function of a decision support systems
and operational systems
User Query Progression
Starts simple
Becomes more
analytical
Requires different
techniques and
flexible tools
Why?
What?
Why?
Why?
Training
 Methods
- Informal: one-to-one or small class
- Formal: larger class
- Self-study
 Basic topics
- Logging on
- Accessing metadata
- Creating and submitting a query
- Interpreting results
- Saving queries and storing results
- Utilizing resources
- Learning warehouse fundamentals
Query Efficiency
User considerations
Successful completion
Faster query execution
Less CPU used
More opportunity for further analysis
Query Efficiency
Designer considerations
Use indexes
Select minimum data
Employ resource governmors
Minimize bottlenecks
Develop metrics
User prepared and tested queries
Use quiet periods
Charge Models
Examples of charge models:
- Flat allocation model
- Transaction-based model
- Telephone service model
- Cable TV model
Develop your own unique model
Avoid a charge model that discourages
users from using the warehouse
Query Scheduling and
Monitoring
 Query scheduling
- Manages information usage
- Directs queries
- Executes queries
- Sets job queue priorities
 Query monitoring
- Track resource-intensive queries
- Detect unused queries
- Catch queries that use summary data inefficiently
- Catch queries that perform regular summary
calculations at the time of query execution
- Detect illegal access
Query Management and
Monitoring Tools
Use tools, schedulers, Oracle Enterprise
Manager
Consider
- Automation levels
- Technology interfaces
- Cost
Security
Do not overlook
Subject area sponsors:
- Review and authorize request for
access rights
- Identify enhancements
Transparent security
Easy to implement, maintain, and manage
Security Plan
Define a strategy:
- Allocate business area owners
- Ensure invisibility
Ensure easy management
Consider auditing
Manage passwords
Role-Based Security
Subject area access:
- Summary data for new users
- All data for experienced users
Departmental access
Limited object access
Access during load
Application Context and FineGrained Access Control in
Oracle8i
Who am I?
Where am I?
Table
Access
policy
Application
context
Comparing OLAP and DSS
OLAP is used for multidimensional analysis.
DSS provides a system enabling decision
making.
OLAP tools provide a DSS capability
OLAP for the warehouse provides analytical
power.
Other terms:
- EIS
- KBS
The Functionality of OLAP
Rotate and drill down to successive levels of
detail.
Create and examine calculated data interactively
on large volumes of data.
Determine comparative or relative differences.
Perform exception and trend analysis.
Perform advanced analytical functions for
example forecasting, modeling, and regression
analysis
Original OLAP Rules
Multidimensional conceptual view
Transparency
Accessibility
Consistent reporting performance
Client-server architecture
Original OLAP Rules
Generic dimensionality
Dynamic sparse matrix handling
Multiuser support
Unrestricted cross-dimensional operations
Intuitive data manipulation
Flexible reporting
Unlimited dimensions and aggregation
levels
Relational Database Model
Attribute 1
Name
Row 1
Row 2
Row 3
Row 4
Anderson
Green
Lee
Ramos
Attribute 1
Age
31
42
22
32
Attribute 1
Gender
F
M
M
F
Attribute 1
Emp No.
1001
1007
1010
1020
The table above illustrates the employee relation.
Multidimensional Database
Model
Customer
Store
Store
Time
Time
SALES
FINANCE
Product
GL-Line
The data is found at the intersection of dimensions.
Relational Server
 Benefits:
- Well-known environment with many experts in most
organizations able to support the product
- Can be used with data warehousing and operational
systems
- Many tools available with advanced features including
improvements made to performance with report servers
 Disadvantages:
- Does not have any complex functions or analysi s
capabilities provided by OLAP tools
- These products may also be restricted to the volumes of
data they can access
Multidimensional Server
 Benefits:
- Quick access to very large volumes of data
- Extensive and comprehensive libraries of complex
functions specifically for analysis
- Strong modeling and forecasting capabilities
- Can access multidimensional and relational database
structures
 Disadvantages:
- Difficulty of changing dimensions without reaggregating
to time
- Lack of support for very large volumes of data