2. Need for OLAP
• Business problems need query centric db.
• Need multidimensional approach.
Characteristics of above problems:
Extract large number of records from large
data set.
Data summary.
To solve these kind of problems we need OLAP
3. Introduction to OLAP
• Continuous iterative process.
• Operations are:
– Drill down
– Drill up
– pivot
4. Multidimensional data model
• How many students done the
conducted by department in college.
• Dimensions are:
exams
– Students
– Exams
– Department
– College.
Response time of multidimensional query depends upon the number of cells to be
added on the fly.
Number of dimension increases=no of cube cell increase
8. Classification of OLAP tools
• Based on multidimensional db.
• Allow the users to analyze the data using
views.
• Need MDDB.
• Classifications:
– MOLAP
– ROLAP
9. M(Multidimensional)OLAP
• Uses MDDBMS to organize and navigate data
• Data Structure: Array
• Segregate the OLAP thro API
Pros:
Excellent performance
Response time.
Cons:
Series analysis
iteration
10. Example
Organization tool
• Arbor software: ESSbase
• Oracle: Express server
• Pilot Software: Light Slip Server
• Snipper: TM/I
• Planning Science: Gentium
• Kenan technology: Multiway
11. Challenges
• Data structure to support multiple subject area of data.
• Analyze which data can be navigated and analyzed.
• When the navigation changes the data structure needs to be
physically reorganized.
• Need different skill set and tools for DBA to build, maintain
database.
• Need hybrid solution.
Hybrid Solution:
Integration of multidimensional data storage with
RDBMS,
Provide users with MDDS
Data maintained in RDBMS.
13. • This allows the MDDS to dynamically obtain
the detail maintained in RDBMS when the
application reaches the bottom of
multidimensional cells during drill down
analysis.
• Best for Sensitive applications.
14. ROLAP
• Fastest growing style of OLAP
• Products of ROLAP have been engineered to
support products directly through meta data.
• Enables multi dimensional views of 2D
relational tables.
• Pros:
– Flexibility
• Cons:
– Data base design
17. Managed Query Environment/HOLAP
• Provides user with ability to perform limited analysis
capability either directly with RDBMS products or
• Intermediate MOLAP.
• The ad hoc query converted to provide data cube.
Done by:
1. Convert the query to select data from DBMS
2. Deliver the data to desktop where it is placed in data
cube.
3. Data cube is stored locally to reduce overhead of
creation of the cube.
4. Now user can perform multi dimensional analysis.
19. • Pros:
– Simple installation
– Administration is easy
– Network traffic is less
• Cons:
– Redundancy
– Inconsistency.
20. OLAP tools and Internet
• Internet free resource, provides connectivity, can do
complex administration jobs, store and manage data
applications
• Data warehousing
General features of web enabled data access:
• 1st generation websites:
– Static distribution model
– Client access static html pages via browser.
– Decision support reports stored as html doc and delivered to
users.
• Deficiencies:
– Interaction with clients.
21. • 2nd generation:
– Supports interaction
– Multi tiered architecture
– Client submits the query in html to web server
– Server transform the request to CGI
– The gateway submits SQL queries to db and
receives and translates to html and sends to page
requester.
