MULTIDIMENSIONAL DATA MODEL Niall Cosgrave Timothy Halpin Kevin McCarthy Cian O’Brien Amanda O’Donovan 112223751 112222171 107476477 108580235 108385581 1
MULTIDIMENSIONAL DATA MODEL
Niall CosgraveTimothy HalpinKevin McCarthyCian O’BrienAmanda O’Donovan
1122237511122221711074764771085802351083855811
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.
WHAT IS MULTIDIMENSIONAL DATA
2
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.
WHAT IS MULTIDIMENSIONAL DATA
3
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.
WHAT IS MULTIDIMENSIONAL DATA
4
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.
WHY MULTIDIMENSIONAL DATABASE
5
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."
WHY MULTIDIMENSIONAL DATABASE
6
The multidimensional data model is composed of logical cubes, measures, dimensions, hierarchies, levels, and attributes.
WHY MULTIDIMENSIONAL DATABASE
7
DIAGRAM OF THE MULTIDIMENSIONAL MODEL:
8
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.
DIAGRAM OF THE MULTIDIMENSIONAL MODEL:
9
EXAMPLES OF DATA CUBES IN USE AND HOW THEY WORK
10
1991 CANADIAN CENSUS
11
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 12
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 the cube.
13
DICING AND ROTATING• 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.
• http://demodc.chass.utoronto.ca/iassist/
14
Ontario
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
VISUAL REPRESENTATION OF MULTIDIMENSIONAL CUBE
16
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.
17
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
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-us/library/office/aa140038(v=office.10).aspx#odc_da_whatrcubes_topic5
19
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• Relation if any to Data Cubes
20
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 21
DATA WAREHOUSING
22
“...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 Inmom
DATA WAREHOUSING
23
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 … 6. Duration …
24
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 …25
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
Tumbleweed, oak tree example 26
DATA MART Differences between Data Warehouse & Mart:
27
DATA MART
28
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. Functionality2. Size3. Load performance4. Administration5. Setup and configuration 29
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) 30
MULTI-DIMENSIONAL MODEL VS. RELATIONAL DATABASES
31
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
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 unsuitable
33
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 data34
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 Determines data inserted into warehouse
35
RELATIONAL VS. MULTI-DIMENSIONALRelational Database Multi-Dimensional Cube
1 ComplexDifferent tables and relationships
SimpleDimension table has a direct relationship with the fact table
2 Flexible Rigid3 Normalization common Repetition allowed4 OLTP
Data updated frequentlyOLAPMinimum 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 Cubes6 Table fields store actual data Dimensions and measures store
actual data7 Table size is measured in
recordsCube size is measured in cell-sets
8 Keywords Questions or “Verbiage”9 Fundamental business tasks Planning, problem solving,
decision making
36
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 37
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
ADVANTAGES AND DRAWBACKS OF MULTI-DIMENSIONAL MODELLING
39
MULTIDIMENSIONAL CUBE OVERVIEW - ADVANTAGESo 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
40
MULTIDIMENSIONAL CUBE OVERVIEW - DRAWBACKSo User Misuse and misunderstanding.o Ridged and Inflexible nature.o Too specific – Manipulation of Datao Not suitable for ad-hoc queries, unless within the
dimensions of the "cube space“
MOLAP
ROLAP
Simple Complex
Good
OK
Analysis
QueryPerformance
41
MOLAP SERVER
Advantageso Performance Constraint Environment.o Used in Mission Critical Operations.
Disadvantageso Inflexible and limited data allowance.o Unavailable data.o Specifics of summarised data.
Server
userWarehouse
Query
Data
MDDB
Periodicload
42
ROLAP SERVER
Advantageso Not Limited by Cube Data – ‘Live fetch’.o Maintains functionality of relational Database
Disadvantageso Inhibited Performance on large databases.o Limitations by SQL functionalities
Server userWarehouse
Datacache
Livefetch
Cache
Query
Data
43
THANK YOU FOR LISTENINGAny questions?
44
We Guarantee this Presentation was made with 100% natural sources, 0% Wikipedia