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Revisit to OLAP

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What is OLAP ? OLAP = Online Analytical Processing

Support (almost) ad-hoc querying for business

analyst

Think in terms of spreadsheets View sales data by geography, time, or product

Extend spreadsheet analysis model to work with

warehouse data Large data sets

Semantically enriched to understand business terms

Combine interactive queries with reporting functions

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OLAP Models

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OLAP Models

Relational OLAP (ROLAP): Extended relational DBMS that maps

operations on multidimensional data to

standard relations operations

Store all information, including fact tables,

as relations

Multidimensional OLAP (MOLAP):

Special purpose server that directly

implements multidimensional data and

operations

store multidimensional datasets as arrays

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OLAP Models

Hybrid OLAP (HOLAP):

Give users/system administrators freedom to

select different partitions.

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OLAP & Cube

Cubes are used to have a data summarized, copied, or read directly

from the data warehouse.

Cubes allows

Rapid analytical access

Spares end users from writing language-based queries

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nai

nnai

nnai

nnai

GroceriesGroceries

9797

9898

9999

ClothingClothing

AppliancesAppliances

Mum

ba

i

Mum

ba

i

Pune

Pune

Delhi

Delhi

9696

9595

ka

tta

ka

tta

ka

tta

ka

tta

(Products.Groceries, Location.Pune,Time.95,Measures.Sales)(Products.Groceries, Location.Pune,Time.95,Measures.Sales)

The Sales CubeThe Sales Cube

(Products.Clothing, Location.Mumbai,Time.97,Measures.Sales)(Products.Clothing, Location.Mumbai,Time.97,Measures.Sales)

(Products.Clothing, Location.Delhi,Time. 98,Measures.Sales)(Products.Clothing, Location.Delhi,Time. 98,Measures.Sales)

Time

Product

Location

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OLAP and Data Warehouses are complementary.

A Data Warehouse stores and manages data. OLAP transforms Data Warehouse data into

strategic information.

OLAP enables decision-making about future actions.

OLAP vs. Data Warehouse

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Product

Warehouse

Sales

Budget

Store Sales

OLAP

OLAP vs. Data Warehouse

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Typical OLAP Operations

Roll up (drill-up): summarize data

by climbing up hierarchy or by dimension reduction

Drill down (roll down): reverse of roll-up

from higher level summary to lower level summary or detailed data, or

introducing new dimensions

Slice and dice:

project and select

Pivot (rotate):

reorient the cube, visualization, 3D to series of 2D planes.

Other operations

drill across: involving (across) more than one fact table

drill through: through the bottom level of the cube to its back-end

relational tables (using SQL)

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OLAP Queries

Roll up: summarize data along a

dimension hierarchy

if we are given total sales volume per city wecan aggregate on the Location to obtain sales

per states

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OLAP Queries

Roll down, drill down: go from higher

level summary to lower level summary or

detailed data For a particular product category, find the

detailed sales data for each salesperson by

date Given total sales by state, we can ask for sales

per city, or just sales by city for a selected state

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OLAP Queries

day 2c1 c2 c3

p1 44 4

p2 c1 c2 c3

p1 12 50

p2 11 8

c1 c2 c3

p1 56 4 50

p2 11 8

c1 c2 c3

sum 67 12 50

sum

p1 110

p2 19

129

drill-down

rollup

day 1

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OLAP Queries

Slice and dice: select and project Sales of video in USA over the last 6 months

Slicing and dicing reduce the number of

dimensions Pivot: reorient cube

The result of pivoting is called a cross-

tabulation

If we pivot the Sales cube on the Client and

Product dimensions, we obtain a table for

each client for each product value

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OLAP Queries

Pivoting can be combined with aggregation

sale prodId clientid date amt

p1 c1 1 12

p2 c1 1 11

p1 c3 1 50

p2 c2 1 8

p1 c1 2 44

p1 c2 2 4

day 2c1 c2 c3

p1 44 4

p2 c1 c2 c3

p1 12 50

p2 11 8

day 1

c1 c2 c3 Sum

p1 56 4 50 110

p2 11 8 19

Sum 67 12 50 129

c1 c2 c3 Sum

1 23 8 50 81

2 44 4 48

Sum 67 12 50 129

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OLAP- Slice

Slice for

Year = 98

GroceriesGroceries

9595

9797

9898

9999

ClothingClothingAppliancesAppliances

9696

Mum

ba

i

Mum

ba

i

Pune

Pune

Delhi

Delhi

Ko

lka

tta

Ko

lka

tta

Ko

lka

tta

Ko

lka

tta

Ch

enn

ai

Ch

enn

ai

Ch

enn

ai

Ch

enn

ai

GroceriesGroceries

AppliancesAppliances

ClothingClothing

Mum

ba

i

Mum

ba

i

Pune

Pune

Delhi

Delhi

atta

ka

tta

ka

tta

ka

tta

nai

nainai

nai

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OLAP- Dice

Dice for

Groceries, Clothing

Year = 97 & 98 andSales & Cost

GroceriesGroceries

9595

9797

9898

9999

ClothingClothingAppliancesAppliances

9696

Mum

ba

i

Mum

ba

i

Pune

Pune

Delhi

Delhi

Ko

lka

tta

Ko

lka

tta

Ko

lka

tta

Ko

lka

tta

Ch

enn

ai

Ch

enn

ai

Ch

enn

ai

Ch

enn

ai

GroceriesGroceriesClothingClothing

Mum

ba

i

Mum

ba

i

Pune

Pune

9797

9898

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OLAP Queries

Ranking: selection of first n elements (e.g. select 5

best purchased products in July)

Others: stored procedures, selection, etc.

Time functions

e.g., time average

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Multi-Dimensional View

of Data

ProfitProfit

by Divisionby Division

by Countryby Country

by Monthby Month

by Actual/Budgetby Actual/Budget

FinanceFinance

RevenueRevenueby Productby Product

by Regionby Region

by Sales Repby Sales Rep

by Quarterby Quarter

SalesSalesRevenueRevenue

by Customerby Customer

by Industryby Industry

by Channelby Channel

by Weekby Week

MarketingMarketing

VolumeVolume

by Plantby Plant

by Shiftby Shift

by Productby Product

by Dayby Day

OperationsOperations

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Multidimensional Data Model

Sales of products may be represented in

one dimension (as a fact relation) or

in two dimensions, e.g. : clients andproducts

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Multidimensional Data Model

sale Product Client Amt

p1 c1 12

p2 c1 11

p1 c3 50p2 c2 8

c1 c2 c3

p1 12 50p2 11 8

Fact relation Two-dimensional cube

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Multidimensional Data Model

sale Product C lient Date Amt

p1 c1 1 12

p2 c1 1 11

p1 c3 1 50

p2 c2 1 8

p1 c1 2 44

p1 c2 2 4

day 2 c1 c2 c3p1 44 4p2 c1 c2 c3

p1 12 50

p2 11 8

day 1

Fact relation 3-dimensional cube

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Multidimensional Data Model and

Aggregates

Add up amounts for day 1

In SQL: SELECT sum(Amt) FROM SALE

WHERE Date = 1sale Product Client Date Amt

p1 c1 1 12

p2 c1 1 11

p1 c3 1 50

p2 c2 1 8

p1 c1 2 44p1 c2 2 4

81result

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Multidimensional Data Model and

Aggregates

Add up amounts by day

In SQL: SELECT Date, sum(Amt)

FROM SALE GROUP BY Date

sale Product Client Date Amt

p1 c1 1 12

p2 c1 1 11

p1 c3 1 50

p2 c2 1 8

p1 c1 2 44

p1 c2 2 4

Date sum

1 81

2 48

result

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Multidimensional Data Model and

Aggregates

Add up amounts by client, product

In SQL: SELECT client, product, sum(amt)

FROM SALEGROUP BY client, product

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Multidimensional Data Model and

Aggregates

sale Product Client Date Amt

p1 c1 1 12

p2 c1 1 11

p1 c3 1 50p2 c2 1 8

p1 c1 2 44

p1 c2 2 4

sale Product Client Sum

p1 c1 56

p1 c2 4

p1 c3 50

p2 c1 11

p2 c2 8

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Multidimensional Data Model and

Aggregates

In multidimensional data model together

with measure values usually we store

summarizing information (aggregates)

c1 c2 c3 Sum

p1 56 4 50 110

p2 11 8 19

Sum 67 12 50 129

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Aggregates

Operators: sum, count, max, min,median, ave

Having clause

Using dimension hierarchyaverage by region (within store)

maximum by month (within date)

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Cube Aggregation

day 2c1 c2 c3

p1 44 4

p2 c1 c2 c3

p1 12 50

p2 11 8

c1 c2 c3

p1 56 4 50

p2 11 8

c1 c2 c3

sum 67 12 50

sum

p1 110

p2 19

129

. . .

Example: computing sums

day 1

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Cube

c1 c2 c3 *

p1 56 4 50 110

p2 11 8 19

* 67 12 50 129day 2

c1 c2 c3 *

p1 44 4 48

p2

* 44 4 48c1 c2 c3 *

p1 12 50 62

p2 11 8 19

* 23 8 50 81

day 1

*

sale(*,p2,*)

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Aggregation Using Hierarchies

day 2c1 c2 c3

p1 44 4

p2 c1 c2 c3

p1 12 50

p2 11 8day 1

region Aregion B

p1 1 2 5 0p2 1 1 8

customer

region

country

(customer c1 in Region A;

customers c2, c3 in Region B)

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Aggregation Using Hierarchies

c1

c2

c3

c4

video

Camera

New

Orleans

Pozna

CD

Date of

sale

10

12

1112

3

5

711

21

9

715

Video Camera CD

NO 22 8 30

PN 23 18 22

aggregation with

respect to city

client

city

region

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A Sample Data Cube

sum

sum

sum

USA

Canada

Mexico

C

o

un

t

r

y

Date

Prod

uct

CD

videocamera

1Q 2Q 3Q 4Q

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Exercise

Suppose the AAA Automobile Co. builds a datawarehouse to analyze sales of its cars.

The measure - price of a car

We would like to answer the following typical

queries:

find total sales by day, week, month and year

find total sales by week, month, ... for each dealer find total sales by week, month, ... for each car model

find total sales by month for all dealers in a given city,

region and state.

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Exercise

Dimensions: time (day, week, month, quarter, year)

dealer (name, city, state, region, phone)

cars (serialno, model, color, category , )

Design the conceptual data warehouse schema

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Who Uses OLAP and Why?

Finance department

budgeting,

activity-based costing (allocations),

financial performance analysis financial modeling.

Marketing department

Market research analysis,

sales forecasting,

promotions analysis,

customer analysis,

market/customer segmentation.

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Sales department

Sales analysis

Sales forecasting.

Manufacturing department

Production planning

Defect analysis.

Who Uses OLAP and Why?

Contd..

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Key features of OLAP Multidimensional views of data

Ability to "slice and dice";

View financial data by scenario (for example, actual vs. budget), organization,

line items, and time

View sales data by product, geography, channel, and time.

Calculation-intensive capabilities

Share calculations (percentage of total)

Moving averages and percentage growth

Time intelligence This year's vs. last year's

This month vs. the same month last year.

Aggregation

Allows pre-aggregated Values

Drill Down and Drill Up

Pivot

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Questions