Datawarehouse2-multidimensional modelling

Micro data:
• correspond to single observations
• they are the result of the loading phase
->Base data
Macro data
• correspond to preparation/aggregation of base data for analysis reasons
• they are the result of the evaluation phase
->Prepared data (data warehouse, data mart)
Meta data
• describe the properties of micro and macro data, how they are produced, stored, aggregated, analysed throughout the complete data warehousing process

Multidimensional Perspective:

• qualifying data – attributes of categories

• quantifying data – attributes for summing up sales

Modelling Approaches:
data models are desired that focus on the representation of the macro data as statistical table

Multidimensional Data Modelling:
qualifying information – dimensions of the cube

• Hierarchies, dimensional attributes
• form the starting point for selection and aggregation

quantifying information – cells of the cube

• Facts, measures – plain
• Facts, measures – computed

Graphical Design:

"->" denotes functional dependency

Orthogonality: There is no D.Di -> D’.Dj with Di =/D’

• Functional dependencies impose tree structure on instances, are 1:n relationships
• Each path from a dimensional attribute to Top defines a class hierarchy

Sales and turnover per article, shop and day:

• CSales[(P.Article, S.Shop, T.Day), (Sales, Turnover)]

Sales per product group, region and quarter:

• CSales[(P.Group, S.Region, T.Quarter), (Sales, Turnover)]