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Quality-Oriented Data Warehouse Design (Design Approaches DW (Demand…
Quality-Oriented Data Warehouse Design
Design Approaches DW
Demand-driven or top-down
conceptual schema without considering data sources
Supply driven or bottom-up
conceptual design is based on data sources
Hybrid
Attribute vs. Dimension
Difference in realizing real- world scenarios
Summarizability
Conditions
Completeness
e.g. one customer has at least one job
Drill-Down Incompleteness
element has no entry in lower hierarchy
(e.g. Andorra has no regions, this leads to SUM of sales are different in regions and countries
1, to 0,
into 1 to 1,
Roll-Up Incompleteness
element has no super hierarchy
e.g. Napkin is not assigned to super group
introduce category "other"
Disjointness/Strictness
e.g. every customer has at most one job
Non-Strictness
week can be in multiple months, but
never
many to many relationships inside dimensions
Type compatibility
e.g. do not SUM account balances over time
Multidimensional Normal Forms
Aims
Reasonable
Control over optional dimensions levels
Efficient physical design
1st MNF
formal definition via restrictions on FDs
Faithfulness
Completeness
Avoidance of redundancies
2nd MNF
addresses optional levels
formal definition via context-dependency for every optional level
conctext-sensititve summarizability
efficient physical design
3rd MNF
addresses implicit class hierarchies
defined via restrictions on context-dependencies
construction of explicit class hierarchies
DB and DW Design Process
Requirements analysis and specification
Conceptual design
(Logical design)
(Physical design)
2. Conceptual design
synthesize fact schema
synthesize hierarchies
add context dependencies
define summarizability constraints
Resulting fact schema are in 3rd MNF