Types of measure

Type of measure
1.Additive measure: - Can be summed across all dimensions such as sales amount.
2.Semi-additive measure: - can be aggregated across all dimensions except Time such as Account and inventory balance. If we attempt to aggregate these measures across time then we will get the wrong value. Exp. Bank account has balance 400 in Jan and 600 in Feb.
3.Non-Additive measure: - can’t be meaningfully aggregated across any dimension, such as Rates and percentages. We can use MDX Expression to define how they aggregated.

Data Modeling Techniques

Data Modeling Techniques
Data modeling is a method used to define and analyze data requirements needed to support the business processes of an organization. The data requirements are recorded as a conceptual data model with associated data definitions. Actual implementation of the conceptual model is called a logical data model. To implement one conceptual data model may require multiple logical data models. Data modeling defines not just data elements, but their structures and relationships between them Data modeling techniques and methodologies are used to model data in a standard, consistent, predictable manner in order to manage it as a resource.

 

 

 

Three basic styles of data model:

 

 

• Conceptual data models: These models, sometimes called domain models, are typically used to explore domain concepts with project stakeholders. high-level conceptual models are often created as part of your initial requirements envisioning efforts as they are used to explore the high-level static business structures and concepts

• Logical data models (LDMs) :- LDMs are used to explore the domain concepts, and their relationships, of your problem domain. This could be done for the scope of a single project or for your entire enterprise. LDMs depict the logical entity types, typically referred to simply as entity types, the data attributes describing those entities, and the relationships between the entities.

• Physical data models (PDMs):- PDMs are used to design the internal schema of a database, depicting the data tables, the data columns of those tables, and the relationships between the tables.

Modeling methodologies

 

• Bottom-up models 
• Top-Down models 

Data Modeling Techniques for Datawarehouse

Two data modeling techniques that are relevant in a data warehousing

environment are ER modeling and dimensional modeling.
ER modeling produces a data model of the specific area of interest, using two
basic concepts: entities and the relationships between those entities. Detailed
ER models also contain attributes, which can be properties of either the entities
or the relationships. The ER model is an abstraction tool because it can be used
to understand and simplify the ambiguous data relationships in the business
world and complex systems environments.

Dimensional modeling uses three basic concepts: measures, facts, and
dimensions. Dimensional modeling is powerful in representing the requirements
of the business user in the context of database tables.
Both ER and dimensional modeling can be used to create an abstract model of a
specific subject. However, each has its own limited set of modeling concepts
and associated notation conventions. Consequently, the techniques look
different, and they are indeed different in terms of semantic representation

Dimension modeling VS ER modeling

Difference between Dimension modeling and E-R modeling(Relational modeling)

•The E-R diagram is split as per the entities like Customer,Product etc and its attributes would be textual and numeric(fact or calculation) . A dimension model is split as per the dimensions and facts. dimension only contains textual attributes and numeric values will comes under fact table.

. The E-R modeling will be more normalized (used as a OLTP) with optimum index but in Dimension moduling would be less normalized (used as a OLAP) with maximus number of index to boost the search performance.

. Dimension modeling will help you to desing database for desicion support system but E-R modeling is used for Transaction processing database.

.An E-R diagram is a complex maze of hundreds of entities linked with each other (more relationship between entities), whereas the Dimensional model has logical grouped set of star-schemas or snowflacs.

. Performance of Insert,Delete and update operation would be high and search would be comparatively slow in ER modeling but Dimension modeling Search would be faster and lest operation would be slower.

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