This document discusses multidimensional databases and provides comparisons to relational databases. It describes how multidimensional databases are optimized for data warehousing and online analytical processing (OLAP) applications. Key aspects covered include dimensional modeling using star and snowflake schemas, data storage in cubes with dimensions and members, and performance benefits of multidimensional databases for interactive analysis of large datasets to support decision making.

1. Shan Hasan
Abdul Samad (IS Advisor)

2.  Why Multidimensional Databases.
 Comparison between Relational &
Multidimensional Databases.
 Multidimensional Database design & Architecture.
 Dimensional Modeling.
 Conclusion.
◦ Where to use multidimensional database.

3.  A multidimensional database (MDB) is a type of
database that is optimized for data warehouse
and online analytical processing (OLAP)
applications.
 Multidimensional data-base technology is a key
factor in the interactive analysis of large amounts
of data for decision-making purposes.

4.  Multi-dimensional databases are especially useful
in sales and marketing applications that involve
time series. Large volumes of sales and inventory
data can be stored to ultimately be used for
logistics and executive planning.

5.  Why Multidimensional Database
◦ Enables interactive analyses of large amounts of data for
decision-making purposes.
◦ Differ from previous technologies by viewing data as
multidimensional cubes, which have proven to be
particularly well suited for data analyses.
◦ Rapidly process the data in the database so that
answers can be generated quickly.
◦ A successful OLAP application provides "just-in-time"
information for effective decision-making.

6.  Comparison Between Relational &
Multidimensional Database
◦ Relational Database
 The relational database model uses a two-dimensional
structure of rows and columns to store data. Tables can be
linked by common key values.
 Accessing data from relational databases may require
complex joins of many tables and is distinctly non-trivial for
untrained end-users.

7.  Comparison Between Relational &
Multidimensional Database
◦ Relational Database
• To get the desired information from the data, organizations forced to
hire IT professionals to structure such complex queries and also
these complex queries takes huge time to return the results.
• When writing queries such as INSERT, DELETE and UPDATE on
tables, the consequences of getting it wrong are greatly increased
when they are employed on a live system environment.

8.  Comparison Between Relational &
Multidimensional Database
◦ Multidimensional Database
 Enhance data presentation and navigation by intuitive
spreadsheet like views that are difficult to generate in
relation database.
 Easy to maintain because data is stored in the same way as
it is viewed, so no additional computational overhead is
required.

9.  Comparison Between Relational &
Multidimensional Database
◦ Multidimensional Database
• Data analysis and decision making is much easier through
multidimensional database as compare relational databases.

10.  Cubes
◦ Data cubes provide true multidimensionality. They
generalize spreadsheets to any number of dimensions.
◦ Although the term “cube” implies 3 dimensions, a cube
can have any number of dimensions.
◦ A collection of related cubes is commonly referred to as
a multidimensional database.

11.  Dimensions and Members
◦ Dimension provides the means to slice and dice the data.
It provides filtering and grouping of the data.
◦ Members are the individual components of a dimension.
For example, Product A, Product B, and Product C might
be members of the Product dimension. Each member
has a unique name.

12.  Sparse & Dense Dimensions
◦ A sparse dimension is a dimension with a low
percentage of available data positions filled.
◦ A dense dimension is a dimension with a high probability
that one or more data points is occupied in every
combination of dimensions.

13.  Data Storage
◦ Each data value is stored in a single cell in the database,
in the form of multidimensional array.
 Data Value
◦ The intersection of one member from one dimension with
one member from each of the other dimensions
represents a data value.

15.  Multidimensional Expression
◦ Multi-dimensional Expressions (MDX) is the most widely
supported query language to date for reporting from
multi-dimensional data stores.
◦ With MDX / mdXML, a robust set of functions makes
accessing multi-dimensional data easier and more
intuitive.
◦ MDX / mdXML does not have the data definition
capabilities (DDL) that SQL has.

16.  Dimensional Modeling is a logical design
technique that present the data in a standard,
intuitive framework that allows for high-
performance access.
 In DM, a model of tables and relations is
constituted with the purpose of optimizing decision
support query performance in relational
databases.

17.  Fact Table
◦ Fact table consists of the measurements and facts of the
business process.
◦ A fact table typically has two types of columns: those that
contains facts(numerical values) and those that are
foreign key to dimension tables.

18.  Dimension Table
◦ The dimension table provides the detailed information
about the attributes in the fact table.
◦ Fact tables connect to one or more dimension tables, but
fact tables do not have direct relationships to one
another.

19.  Star Scheme
◦ In the star schema design, a single object (the fact table)
sits in the middle and is connected to other surrounding
objects (dimension tables) like a star.
◦ A star schema has one dimension table for each
dimension.

20. Star Scheme For Sales Cube

21.  Snowflake Scheme
◦ Snowflake schemas contain several dimension tables
for each dimension.
◦ The main advantage of the snowflake schema is that it
reduces the space required to hold the data and the
number of places where it need to be updated if the data
changes.
◦ The main disadvantage of the snowflake schema is that
it increase the number of tables that need to join in order
to perform the given query.

22. Snowflake scheme for Sales Cube

23.  Performance
◦ Multidimensional Database server typically contain
indexes that provide direct access to the data, making
MDD servers quicker when trying to solve a
multidimensional business problem.
◦ MDDs deliver impressive query performance by pre-
calculating or pre-consolidating transactional data rather
than calculating on-the-fly.

24.  Data Volume & Scalability
◦ To fully pre-consolidate incoming data, MDDs require an
enormous amount of overhead both in processing time
and in storage. An input file of 200MB can easily expand
to 5GB; obviously, a file of this size takes many minutes
to load and consolidate.
◦ Some data is stored redundantly in the database .
◦ It is not suited for transaction processing as it takes time
to store the calculated result in the database.

25.  Multidimensional Databases Torben Bach Pedersen Christian S. Jensen Department of
Computer Science, Aalborg University.
 Understanding Multidimensional
Databases.http://download.oracle.com/docs/cd/E12032_01/doc/epm.921/html_esb_dbag/frames
et.htm?/docs/cd/E12032_01/doc/epm.921/html_esb_dbag/dinconc.htm
 Data Mining & Analysis, LLC. Data Warehousing Service. http://www.donmeyer.com/art3.html
 A Dimensional Modeling Manifesto by Ralph Kimball.
http://www.dbmsmag.com/9708d15.html#figure2
 Multidimensional expressions for Analysis. http://www.xmlforanalysis.com/mdx.htm
 Comparison of Relational and Multidimensional database Structures. John Collins
 Data Warehousing Architecture & major Components. Anupam Gupta. Anenues International
Inc.
 Dimensional Modeling and ER Modeling In The Data Warehouse by Joseph M. Firestone.
 Online Analytical Processing (OLAP), Douglas S.Kerr.