[計算機軟件及應(yīng)用]chapter3thedatawarehouseanddesign-wenkub
2022-10-31 23:11:10 本頁面
【正文】 Beginning with Operational Data 4. Modify application code Never a popular option (application code is old and fragile) 5. Comparison between a “before” and an “after” image of the operational file A snapshot is taken at the moment of extraction Complex and simply a last resort 9 Beginning with Operational Data ? Issue 3: a timebasis shift Operational data: currentvalue, can be updated Data warehouse data: update forbidden, an element of time attached 10 Beginning with Operational Data ? Issue 4: data condensation 11 Process/Data Models and the Architected Environment 12 The Data Warehouse and Data Models ? Data model is applicable to both the operational environment and the data warehouse environment ? An overall Corporate Data Model is created 13 Corporate Data Models ? Corporate Data Model ? Focuses on and represents only primitive data ? When transported to the operational environment: performance factors are added ? When applied to the data warehouse environment: Remove pure operational data Add element of time to key Add derived data where appropriate Create artifacts of relationships Stability analysis (shown as bellow) 14 Stability Analysis ? Stability analysis involves grouping attributes of data together based on their propensity for change 15 The Data Warehouse Data Model Three levels of data modeling: 1. Highlevel modeling (the entity relationship diagram, ERD) ? Features entities and relationships 16 Entity Relationship Diagram ? Scope of integration: Defines the boundaries of the data model and must be defined before the modeling process mences 17 Corporate ERD ? Reflect the different views of people across the corporation 18 The Midlevel Data Model 2. The Midlevel Data Model (DIS) ? After the highlevel data model is created, the midlevel model, or the DIS (Data Item Set，數(shù)據(jù)項集 ) is established ? For each major subject area, or entity, identified in the highlevel data model, a midlevel model is created 19 The Midlevel Data Model Four basic constructs at the midlevel model: 1. A primary grouping of data（主要數(shù)據(jù)分組） ? Exists once, and only once, for each major subject area ? It holds attributes and keys for each major subject area 20 The Midlevel Data Model 2. A secondary grouping of data（二級數(shù)據(jù)分組） ? Hold data attributes that can exist multiple times 3. A connector（連接器） ? Signifies the relathionships of data between major subject areas 4. “Type of” da
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