In class student presentation for Business Intelligence.

1. MULTIDIMENSIONAL DATA
MODEL
Niall Cosgrave 112223751
Timothy Halpin 112222171
Kevin McCarthy 107476477
Cian O’Brien 108580235
1 Amanda O’Donovan 108385581

2. WHAT IS MULTIDIMENSIONAL DATA
 Multi-Dimensional Data Model is a model for data
management whereby the databases are
developed according to user's preferences, in order
to be used for specific types of retrievals.
 This model views data in the form of data cube. A
data cube allow data to be modelled and viewed in
multiple dimensions. It is define by dimensions and
fact.
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3. WHAT IS MULTIDIMENSIONAL DATA
 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.
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4. WHAT IS MULTIDIMENSIONAL DATA
 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.
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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.
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6. WHY MULTIDIMENSIONAL DATABASE
 The multidimensional data model is important
because it enforces simplicity
 As Ralph Kimball states in his landmark book, The
Data Warehouse Toolkit:
 "The central attraction of the dimensional model of
a business is its simplicity.... that simplicity is the
fundamental key that allows users to understand
databases, and allows software to navigate
databases efficiently."
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7. WHY MULTIDIMENSIONAL DATABASE
 The multidimensional data model is composed of
logical cubes, measures, dimensions, hierarchies,
levels, and attributes.
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8. DIAGRAM OF THE MULTIDIMENSIONAL MODEL:
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9. DIAGRAM OF THE MULTIDIMENSIONAL MODEL:
 Logical Dimensions: Logical Dimensions are
dimensions contain a set of unique values that
identify and categorise data.
 Hierarchies and Levels : A hierarchy is a way to
organize data at different levels of aggregation.
 Attributes: An attribute provides additional
information about the data. Some attributes are
used for display.
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10. EXAMPLES OF DATA CUBES IN USE
AND HOW THEY WORK
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11. 1991 CANADIAN CENSUS
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12. SLICING, DICING AND ROTATING
 In the above cube we have the results of the 1991 Canadian
Census with ethnic origin, age group and geography
representing the dimensions of the cube, while 174 represents
the measure. The dimension is a category of data. Each
dimension includes different levels of categories. The
measures are actual data values that occupy the cells as
defined by the dimensions selected.
 Three important concepts are associated with data cubes
- Slicing
- Dicing
- Rotating
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13. SLICING THE DATA CUBE
• Figure 2 illustrates slicing the
Ethnic origin Chinese. When the
cube is sliced like in this
example, we are able to
generate data for Chinese origin
for the geography and age
groups as a result.
• The data that is contained within
the cube has effectively been
filtered in order to display the
measures associated only with
the Chinese ethnic origin.
• From an end user
perspective, the term slice most
often refers to a two-
dimensional page selected from 13
the cube.

14. DICING AND ROTATING
Ontario • Dicing is a related operation to
slicing in which a sub-cube of the
original space is defined
• Dicing provides the user with the
smallest available slice of
data, enabling you to examine each
sub-cube in greater detail.
• Rotating, which is sometimes called
pivoting changes the dimensional
orientation of the report or page
display from the cube data. Rotating
may consist of swapping the rows
an columns, or moving one of the
row dimensions into the column
dimension.
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• http://demodc.chass.utoronto.ca/ias
sist/

15. EXAMPLE OF A DATA CUBE IN USE
 „Design and development of data mart for animal resources’ is a
2008 paper by Rai et al that critically examines the development of a
Central Data Warehouse for a multitude of agricultural areas.
 www.sciencedirect.com/science/article/pii/S0168169908001245
 The paper provides a visual representation of a data cube that shows
the livestock population census multidimensional cube which is
accessed through Internet browser for OLAP.
 In this cube, hierarchies are All States, All Species and All Years. All
States has state names as a top level and district as bottom level of
data flow hierarchy. All Species has top level as species name,
second level as sex, third level as age group and bottom level as
working categories of animals. All Years has only one level, i.e.
years. 15

16. VISUAL REPRESENTATION OF
MULTIDIMENSIONAL CUBE
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17. EXAMPLE OF A DATA CUBE IN USE
 This on-line system has drag and drop option for creation of
nested tables, drill up and drill down functionalities based on
hierarchies of various dimensions.
 The system also has simple calculation options on tabular
data, hide and show options to hide certain undesirable rows
or columns to be displayed on the screen.
 Find and search options are available for finding a particular
piece of information in tabular data of a cube.
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18. CREATING YOUR OWN DATA CUBE
 There are a variety of tools available that allow you to build your own
data cube such as Microsoft Excel and Microsoft SQL server.
 The processes required are:
1 Chose a data source:
2 Create the query that extracts data from the database.
3 Create the cube from the extracted data.
 The Contoso database that we used for the Dashboard project is a
good example of a data source from which we can generate data
cubes
 Use the query wizard to generate the query that you wish to build
your cube on. 18

19. CREATING YOUR OWN DATA CUBE
 In the Query Wizard Finish screen, select Create an OLAP Cube from this
query and click Finish.
 The third step is to then use the OLAP Cube Wizard. This application allows
you to turn your table columns into dimensions. i.e. Drag product_category,
product_subcategory, and brand_name so that they appear in that order, in
the available dimension box. Rename the dimension „Product.‟
 The next step is to select the option that best fits the type of cube you want to
create. For example, select Save a cube file containing all data for the cube.
Enter a path and filename for the cube, and then click Finish.
 Save the query definition that you have created. The cube wizard then
creates the cube file. Once the cube is created the PivotChart Wizard allows
you to create a PivotTable report from the data in the cube.
 http://msdn.microsoft.com/en- 19
us/library/office/aa140038(v=office.10).aspx#odc_da_whatrcubes_topic5

20. DATA WAREHOUSING & DATA
MARTS
How do Data Cubes relate to Data Warehousing & Data
Marts? Are they the same?
• Data Warehousing (DW) Definition
• Pros/Cons of DW
• Relation if any to Data Cubes
• Data Marts (DM) Definition
• Pros/Cons of DM
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• Relation if any to Data Cubes

21. DATA WAREHOUSING
 What is a Data Warehouse?
 A DW contains historical data derived from transaction data,
but it can include data from other sources
 It separates analysis workload from transaction workload and
enables an organisation to consolidate data from several
sources to business users
 “Data Mining: Concepts & Techniques” , J. Han & M. Kamber
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22. DATA WAREHOUSING
“...The data warehouse
is nothing more than the
union of all the data
marts...”- Ralph Kimball
“You can catch all the
minnows in the ocean
and stack them together
and they still do not
make a whale”- Bill 22
Inmom

23. DATA WAREHOUSING
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24. DATA WAREHOUSING
 Benefits:
1. Gives the data …
2. Removes …
3. Potential for …
4. Increased productivity …
5. Example : US Insurance Company, B. Shin 2001
 Problems:
1. Increased …
2. Maintenance …
3. Complexity …
4. Required …
5. Ownership … 24
6. Duration …

25. DATA WAREHOUSING
 Comparisons to Data Cubing:
1. Data cubes provide a …
2. Data cubes are used to …
3. From a design standpoint, it‟s important to …
4. To put data in and get data out …
5. Some or all of these …
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26. DATA MART
 The single most important issue …
 A subset of a data warehouse that …
 Characteristics include:
1. Focuses on …
2. Do not normally …
3. More easily …
 How Is a Data Mart different from a Data Warehouse?
 A data warehouse, unlike a data mart …
 Are essentially different architectural structures, even
though when viewed from afar and superficially, they look
to be very similar 26
 Tumbleweed, oak tree example

27. DATA MART
 Differences between Data Warehouse & Mart:
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28. DATA MART
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29. DATA MART
 Benefits of creating a data mart:
1. To give users …
2. To improve …
3. Building a data mart …
4. The cost of implementing …
 Problems:
1. Functionality
2. Size
3. Load performance
4. Administration
5. Setup and configuration 29

30. DATA MART
 Comparisons to Data Cubing:
1. The data mart is typically housed in multidimensional
technology which is great for …
2. Data Cubing provides a solid base for …
3. Data Cubing gives end users …
4. “To me, a Data Mart is just place where data gets dumped
in a relatively flat, unusable format. Data Cubes is taking
that data and making it dance.” (B. Quinn, 2008)
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31. MULTI-DIMENSIONAL MODEL VS.
RELATIONAL DATABASES
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32. RELATIONAL DATABASES
 Data is stored in Relations
 Tables with rows and columns.
 Records and Fields in each Table
 Relationships between tables
 “A shared repository of data”
 Sarma et.al (2011) 32

33. OLTP
 Online Transaction Processing
 Data is processed immediately and is always kept
current
 Banking, inventory, scheduling, reservation systems.
 Simple queries
 Insert; update; select
 For complex queries, relational databases are
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unsuitable

34. DATA WAREHOUSE
 A large store of data accumulated from various
databases
 ETL Process
 Extract Data
 Transform Data
 Data Cleaning
 Load Data
 Data Cube used for representing this data
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35. DIMENSIONS AND MEASURES
 Multi-dimensional model defined by fact table and
dimension tables
 Measure attribute: Saved from relational into the fact
table
 Defines data in MDM model
 Meta Data: Describes all the pertinent aspects of the
data in the database fully and precisely
 Required for sources from relational database
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 Determines data inserted into warehouse

36. RELATIONAL VS. MULTI-DIMENSIONAL
Relational Database Multi-Dimensional Cube
1 Complex Simple
Different tables and relationships Dimension table has a direct
relationship with the fact table
2 Flexible Rigid
3 Normalization common Repetition allowed
4 OLTP OLAP
Data updated frequently Minimum number of joins, which is
provided in multi-diensional by a single
join to a fact table
5 Data is stored in Tables Data is stored in Cubes
6 Table fields store actual data Dimensions and measures store actual
data
7 Table size is measured in records Cube size is measured in cell-sets
8 Keywords Questions or “Verbiage” 36
9 Fundamental business tasks Planning, problem solving, decision
making

37. ONLINE ANALYTICAL PROCESSING (OLAP)
 “Multi-dimensional models lie at the core of OLAP”
 Jensen et.al (2007)
 Provide quick answers to queries that aggregate
large amounts of data to find trends and patters.
 Well-suited for multidimensional data organization
 Specific Questions
 Answers needed quickly
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38. SIMPLICITY AND CONSTRUCTION
 "The central attraction of the dimensional model of
a business is its simplicity.... that simplicity is the
fundamental key that allows users to understand
databases, and allows software to navigate
databases efficiently."
 Measures have same relationships
 Easily analysed and displayed together
 Those with little experience find multidimensional
model queries only take a short time to master.” 38

39. ADVANTAGES AND DRAWBACKS OF
MULTI-DIMENSIONAL MODELLING
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40. MULTIDIMENSIONAL CUBE OVERVIEW -
ADVANTAGES
o Tables - nature and structure no Longer forced on user.
o Captures health of Organisation – allows drill down options.
o Incorporates business rules automatically – and not exposed to
users.
o Automatic pre-populated data – Saving time and Resources
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41. MULTIDIMENSIONAL CUBE OVERVIEW -
DRAWBACKS
o User Misuse and misunderstanding.
o Ridged and Inflexible nature.
o Too specific – Manipulation of Data
o Not suitable for ad-hoc queries, unless within the dimensions of the
"cube space“
Good
MOLAP
Query
Performance
ROLAP
OK
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Simple Complex
Analysis

42. MOLAP SERVER
MDDB
Server
Query
Periodic
load Data
Warehouse user
Advantages
o Performance Constraint Environment.
o Used in Mission Critical Operations.
Disadvantages
o Inflexible and limited data allowance. 42
o Unavailable data.
o Specifics of summarised data.

43. ROLAP SERVER
Cache
Warehouse
user
Server
Live
fetch Query
Data Data
cache
Advantages
o Not Limited by Cube Data – „Live fetch‟.
o Maintains functionality of relational Database
Disadvantages
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o Inhibited Performance on large databases.
o Limitations by SQL functionalities

44. We Guarantee this Presentation was made with 100%
natural sources, 0% Wikipedia
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