- ROLAP implements OLAP using relational database technologies like SQL Server instead of proprietary multidimensional databases. - It allows for larger dimension tables by storing and aggregating data in relational tables rather than pre-computed cubes. - Front-end tools allow for multi-dimensional analysis of data stored relationally using techniques like aggregate awareness and star schema designs.

1. Lecture
OLAP Implementation TechniquesOLAP Implementation TechniquesOLAP Implementation TechniquesOLAP Implementation Techniques

2. Aggregation in MOLAP
Sales volume as a function of (i) product, (ii) time, and (iii)
geography
A cube structure created to handle this.
Dimensions: Product, Geography, Time
Industry
Category
Product
Hierarchical summarization paths
Product
Time
w1 w2 w3 w4 w5 w6
Milk
Bread
Eggs
Butter
Jam
Juice
N
E
W
S
12
13
45
8
23
10
Province
Division
District
City
Zone
Year
Quarter
Month Week
Day

3. Drill down: get more details
e.g., given summarized sales as above, find breakup of sales by city
within each region, or within Sindh
Rollup: summarize data
e.g., given sales data, summarize sales for last year by product
category and region
Slice and dice: select and project
e.g.: Sales of soft-drinks in Karachi during last quarter
Pivot: change the view of data
Cube Operations

4. Querying the Cube
Drill-down
-
2,000
4,000
6,000
8,000
10,000
12,000
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
OJ RK 8UP PK MJ BU AJ
2001 2002
-
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
2001 2002
Juices Soda Drinks
-
2,000
4,000
6,000
8,000
10,000
12,000
14,000
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
Juices Soda Drinks
2001 2002
Drill-Down
Roll-Up

5. Querying the Cube (Pivoting)
-
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
2001 2002
Juices Soda Drinks
-
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
Orange
juice
Mango
juice
Apple
juice
Rola-
Kola
8-UP Bubbly-
UP
Pola-
Kola
2001 2002

6. No standard query language for querying MOLAP
- No SQL !
Vendors provide proprietary languages allowing business users to
create queries that involve pivots, drilling down, or rolling up.
- E.g. MDX of Microsoft
- Languages generally involve extensive visual (click and drag) support.
- Application Programming Interface (API)’s also provided for probing the
cubes.
MOLAP Implementations

7. Need to consider both maintenance and storage implications
when designing strategy for when to build cubes.
Maintenance Considerations: Every data item received into
MDD must be aggregated into every cube (assuming “to-date”
summaries are maintained).
Storage Considerations: Although cubes get much smaller
(e.g., more dense) as dimensions get less detailed (e.g., year
vs. day), storage implications for building hundreds of cubes
can be significant.
MOLAP Implementations

8. Virtual cubes are used when there is a need to join information from
two dissimilar cubes that share one or more common dimensions.
Similar to a relational view; two (or more) cubes are linked along
common dimension (s).
Often used to save space by eliminating redundant storage of
information.
Example: Build a list price cube that can be used to compute
discounts given across many stores in a retail chain without
redundant storage of the list price data through use of a virtual
cube.
Virtual Cubes

9. Typically outperform relational database technology because all answers
are pre-computed into cubes.
Difficult to scale because of combinatorial explosion in the number and
size of cubes when dimensions of significant cardinality are required.
Beyond tens (sometimes small hundreds) of thousands of entries in a
single dimension will break the MOLAP model because the pre-
computed cube model does not work well when the cubes are very
sparse in the population of individual cells.
See www.olapreport.com/DataExplosion.htm
MOLAP Implementations

10. What is ROLAP?
Why ROLAP?
How to implement ROLAP
ROLAP

11. Advances in database technologies and front-end tools have begun to
allow deployment of OLAP usingANSI SQL RDBMS implementations.
ROLAP facilitates deployment of much larger dimension tables than
MOLAP implementations.
Front-end tools to facilitate GUI access to multi-dimensional analysis
capabilities.
Aggregate awareness allows exploitation of pre-built summary tables for
some front-end tools.
Star schema designs are often used to facilitate OLAP against relational
databases.
ROLAP Implementations

12. Data Cube Schema
( a multidimensional array of summaries)( a multidimensional array of summaries)( a multidimensional array of summaries)( a multidimensional array of summaries)
SALES
Store ID
Time ID
Product ID
Customer ID
Unit Sales
Store Cost
Store Sales
STORE
Store ID
Store Name
Store City
Store State
Store Country
TIME
Time ID
Month
Quarter
Year
PRODUCT
Product Class ID
Product ID
Brand Name
CUSTOMER
Customer ID
Last Name
City
State
Country
PRODUCT CLASS
Product Class ID
Product Category
Product Subcategory
Time: Month → Quarter → Year → (all)
Store: Name → City → State → Country → (all)
Product: Brand Name → Subcategory → Category → (all)
Customer: Last Name → City → State → Country → (all)

13. Issue of scalability i.e. curse of dimensionality for MOLAP
Deployment of significantly large dimension tables as compared to
MOLAP using secondary storage.
Aggregate awareness allows using pre-built summary tables by some
front-end tools.
Star schema designs usually used to facilitate ROLAP querying (in
next lecture).
Why ROLAP?

14. OLAP data is stored in a relational database (e.g. a star
schema)
The fact table is a way of visualizing as a “un-rolled” cube.
So where is the cube?
It’s a matter of perception
Visualize the fact table as an elementary cube.
ROLAP as a “Cube”
Product
Time
500Z1P2M2
250Z1P1M1
Sale K Rs.ZoneProductMonth
FactTable

15. Cube is a logical entity containing values of a certain fact at a
certain aggregation level at an intersection of a combination of
dimensions.
The following table can be created using 3 queries
How to Create Cube in ROLAP?
SUM
(Sales_Amt)
M1 M2 M3 ALL
P1
P2
P3
Total
Month_ID
Product_ID

16. For the table entries, without the totals
SELECT S.Month_Id, S.Product_Id,
SUM(S.Sales_Amt)
FROM Sales
GROUP BY S.Month_Id, S.Product_Id;
For the row totals
SELECT S.Product_Id, SUM (Sales_Amt)
FROM Sales
GROUP BY S.Product_Id;
For the column totals
SELECT S.Month_Id, SUM (Sales)
FROM Sales
GROUP BY S.Month_Id;
How to Create Cube in ROLAP using
SQL?

17. Number of required queries increases exponentially with the
increase in number of dimensions.
Its wasteful to compute all queries.
In the example, the first query can do most of the work of the other
two queries
If we could save that result and aggregate over Month_Id and
Product_Id, we could compute the other queries more efficiently
Problem with Simple Approach

18. The CUBE clause is part of SQL:1999
GROUP BY CUBE (v1, v2, …, vn)
Equivalent to a collection of GROUP BYs, one for each of the
subsets of v1, v2, …, vn
Cube Clause in SQL