Database slide

1. Data Analysis, OLAP
and
Extended Aggregate
Functions

2. Data Analysis
Data analysis is a process of inspecting, cleansing,
transforming, and modeling data with the goal of
discovering useful information, suggesting conclusions,
and supporting decision-making.

3. The Process
Of
Data Analysis
1. Data requirements
2. Data collection
3. Data processing
4. Data cleaning
5. Data analysis
6. Communication
CONT.

4. The Process Of Data Analysis

5. Online Analytical Process
OLAP (online analytical processing) is computer
processing that enables a user to easily and selectively
extract and view data from different points of view.

6. Online Analytical Process
● OLAP allows users to analyze database information
from multiple database systems at one time.
● OLAP data is multidimensional, meaning the
information can be compared in many different ways.

7. Types of
Online
Analytical
Process
1. Relational OLAP(ROALP):
Stored data in a relation data-base.
2. Multidimensional OLAP(MOLAP):
Implemented operation in multidimensional
data.
3. Hybrid Online Analytical
processing(HOLAP):
HOLAP store some summaries in memory and
store the base data and other summaries in a
relational database.

8. Online Analytical Process Impementation

9. Applications of OLAP
● Some popular OLAP server software programs include Oracle
Express Server and Hyperion Solutions Essbase . Because of its
powerful data analysis capabilities , OLAP processing is often used
for data mining, which aims to discover new relationships between
different sets of data.

10. Decision Support System
Decision support system (DSS) is a information system
that analyzes business data or organizational data and
presents it so that users can make business decisions
more easily.
CONT.

11. Decision Support System
● DSSs serve the management, operations and planning levels of
an organization and help organization to make decisions about
problems that may be rapidly changing and not easily specified
in advance.
● Decision support systems can be either fully computerized or
human-powered, or a combination of both.

12. Property of a Decision Support System
● DSS tends to be aimed at the less well structured,
underspecified problem that upper level managers typically
face.
● DSS attempts to combine the use of models or analytic
techniques with traditional data access and retrieval functions.
CONT.

13. Property of a Decision Support System
● DSS specifically focuses on features which make them easy to
use by non-computer-proficient people in an interactive mode.
● DSS emphasizes flexibility and adaptability to accommodate
changes in the environment and the decision making approach
of the user.

14. The users themselves are also important components of the architecture
Component of a Decision Support System
Three fundamental components of a DSS architecture are
1. Database .
2. Model.
3. User Interface.

15. Extended Aggregation
The SQL-92 aggregation functionality is limited. But, The
SQL-1999 Standard has a rich set of aggregation
functionality. Such as the generalization of group by
construct using cube and rollup construct.
CONT.

16. Extended Aggregation(Cube construct)
A representative use cube construct is :
select item_name,color
from sales
group by cube(item_name, color)
This query computes the union of 4 different groupings in sales relation:
{(item_name,color), (item_name), (color), {}}
CONT.

17. Extended Aggregation(Rollup construct)
A representative use rollup construct is :
select item_name,color
from sales
group by cube(item_name, color)
This query computes the union of 3 different groupings in sales relation:
{(item_name,color), (item_name), {}}

18. Ranking
Ranking is done in conjunction with an order by specification.
From a relation students_mark(id, marks) to store the students by their
obtained marks:
select id, rank() over(order by (marks) desc) as s.rank
from students_mark order by s.rank

19. Windowing
Windowing functions, which support summarization on moving windows.
A window function defines a frame or window of rows with a given length
around the current row, and performs a calculation across the set of data in the
window.
1. Defines window partitions to from groups of rows.(PARTITIONS BY CLAUSE)
2. Orders rows within a partition.(ORDER BY CLAUSE)
CONT.

20. Windowing
● SYNTAX
window_function(expression)over(
[PARTITION BY expr_list]
[ORDER BY order_list] [frame_clause])
● 1. OVER () is a mandatory clause that defines a window within a query
result set.OVER() is a subset of select and part of the aggregate function.
CONT.

21. Windowing
● 2. PARTITION BY clause subdivides the window into partitions.
Including the partition clause divides the query result set into partition and window
function is applied to each partition separately.
● 3.ORDER BY clause defines the logical order of the rows within each partition of the
result set, if no PARTITION BY is specified, ORDER BY uses the entire table.
● Windows functions are applied to the rows within each partition and stored
according to the order specification.

22. Thank You