1. Data Analysis and OLAP Dr. Ms. Pratibha S. Yalagi Topic Title
Name & Affiliation with mail-id
Data Analysis and OLAP
Institute Logo
Institute name & website
Dr. Ms. Pratibha S. Yalagi
Assistant Professor, Department of Information Technology.
Walchand Institute of Technology, Solapur.

2. Learning Outcomes At the end of this lecture, students will be able to
Specify the Learning Objectives & Outcomes
At the end of this lecture, students will be able to
Design the data cube for a given data set.
Can apply the OLAP operations for a data cube.
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Do not put date in the footer
Walchand Institute of Technology, Solapur

3. Data Analysis and OLAP Be specific to the contents
Online Analytical Processing (OLAP)
Interactive analysis of data, allowing data to be summarized and viewed in different ways in an online fashion (with negligible delay)
Data that can be modeled as dimension attributes and measure attributes are called multidimensional data.
Highlight & Differentiate the points wherever necessary
Dimension attributes
define the dimensions on which measure attributes (or aggregates thereof) are viewed
e.g. the attributes item_name, color, and size of the sales relation
Measure attributes
measure some value
can be aggregated upon
e.g. the attribute number of the sales relation
Walchand Institute of Technology, Solapur

4. Cross Tabulation of sales by item-name and color
Specify the reference of the data or contents used
cross-tabulation (cross-tab), also called as pivot-table
source: Database Systems Concepts Sixth Edition
The table above is an example of a
Values for one of the dimension attributes form the row headers
Values for another dimension attribute form the column headers
Other dimension attributes are listed on top
Values in individual cells are (aggregates of) the values of the dimension attributes that specify the cell
Walchand Institute of Technology, Solapur

5. Relational Representation of Cross-tabs
Cross-tabs can be represented as relations
We use the value all is used to represent aggregates
The SQL:1999 standard actually uses null values in place of all despite confusion with regular null values
Provide the data in readable & visible format
Walchand Institute of Technology, Solapur

6. Data Cube Use the proper & standard data for the example
A data cube is a multidimensional generalization of a cross-tab
Can have n dimensions; we show 3 below
Cross-tabs can be used as views on a data cube
source: Database Systems Concepts Sixth Edition
Walchand Institute of Technology, Solapur

7. Use the pointer & mouse click effects for stepwise elaboration
OLAP Operations
Pivoting: changing the dimensions used in a cross-tab
Slicing: creating a cross-tab for fixed values only
Sometimes called dicing, particularly when values for multiple dimensions are fixed.
Dicing: Creating a sub cube for all dimensions
Rollup: moving from finer-granularity data to a coarser granularity
Drill down: The opposite operation-that of moving from coarser-granularity data to finer-granularity data
Walchand Institute of Technology, Solapur

8. Pivoting
Pivot allows an analyst to rotate the cube in space to see its various faces. For example, cities could be arranged vertically and products horizontally while viewing data for a particular quarter. Pivoting could replace products with time periods to see data across time for a single product.
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Image source:
Walchand Institute of Technology, Solapur

9. Image source: https://en.wikipedia.org/wiki/OLAP_cube
Slicing
Slice is the act of picking a rectangular subset of a cube by choosing a single value for one of its dimensions, creating a new cube with one fewer dimension. The picture shows a slicing operation: The sales figures of all sales regions and all product categories of the company in the year 2004 are "sliced" out of the data cube.
Image source:
Walchand Institute of Technology, Solapur

10. Image source: https://en.wikipedia.org/wiki/OLAP_cube
Dicing
Dice operation produces a sub cube by allowing the analyst to pick specific values of multiple dimensions. The picture shows a dicing operation: The new cube shows the sales figures of a limited number of product categories, the time and region dimensions cover the same range as before.
Image source:
Walchand Institute of Technology, Solapur

11. Rollup & Drilldown
Drill Down/Up allows the user to navigate among levels of data ranging from the most summarized (up) to the most detailed (down).The picture shows a drill-down operation: The analyst moves from the summary category "Outdoor-Schutzausrüstung" to see the sales figures for the individual products.
Roll-up involves summarizing the data along a dimension. The summarization rule might be computing totals along a hierarchy or applying a set of formulas such as "profit = sales - expenses"
Walchand Institute of Technology, Solapur

12. Use the same font & color for similar points & vary the fontsize if necessary
OLAP Implementation
Early OLAP systems are precomputing all possible aggregates in order to provide online response
Space and time requirements for doing so can be very high
2n combinations of group by
It suffices to precompute some aggregates, and compute others on demand from one of the precomputed aggregates
Can compute aggregate on (item-name, color) from an aggregate on (item-name, color, size)
For all but a few “non-decomposable” aggregates such as median
is cheaper than computing it from scratch
Walchand Institute of Technology, Solapur

13. OLAP Implementation (Cont.)
Can use maximum three colors for effective representation
OLAP Implementation (Cont.)
Several optimizations available for computing multiple aggregates
Can compute aggregate on (item-name, color) from an aggregate on (item-name, color, size)
Can compute aggregates on (item-name, color, size), (item-name, color) and (item-name) using a single sorting of the base data
Walchand Institute of Technology, Solapur

14. Extended Aggregation in SQL:1999
Highlight the areas wherever necessary
The cube operation computes union of group by’s on every subset of the specified attributes
E.g. consider the query
select item-name, color, size, sum(number) from sales group by cube(item-name, color, size)
This computes the union of eight different groupings of the sales relation:
{ (item-name, color, size), (item-name, color), (item-name, size), (color, size), (item-name), (color), (size), ( ) }
where ( ) denotes an empty group by list
For each grouping, the result contains the null value for attributes not present in the grouping
Walchand Institute of Technology, Solapur

15. Provide the reflection spot or takeaways at the end
Exercise assignment
Provide the reflection spot or takeaways at the end
Apply the rollup and drill down operations on the above menswear and womenswear data separately and find out the results.
Walchand Institute of Technology, Solapur

16. References Refer the following websites for effective presentation
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Refer the following websites for screen cast softwares
coordinating-committee/tips-for-making-effective-powerpoint- presentations.aspx
Walchand Institute of Technology, Solapur