1. Data Warehouse: Refers to the theme-oriented, consistent, different time, stable data collection, to support the management of the decision support process. In a broad sense, a data warehouse is a database that stores a large amount of historical data. Each record represents a single data at a particular point in time.
It is an information technology that transforms all the data collected into commercial value, and the information collected is reflected in the report. This includes collecting data, filtering data, storing data, and then applying the data to applications such as analysis and reporting.
2. Data Warehouse objectives: To identify the structure, find trends, assist decision-making, and provide decision-making information for business management.
3.. DSS: Decision support process.
4. Data Warehouse Components: Data market, relational database, data source, data preparation, service tools
5. Dimensions:
6. Multidimensional:
7. Aggregation: The structure of acquiring and centralizing a group or sum. Aggregation is the concept of moving data within a multidimensional hierarchy.
9. Category: A classification defined for categories and distinguishing specific data, within a dimension, for providing a detailed classification system.
10. Detail Category: The lowest level of classification within a dimension.
11. Decomposition and synthesis:
12. Index Quantity:
13. OLAP: Online Analytics
14. OLTP online transaction processing
Two Data Model Normalization
1. Concept:
Normalization: A formal method that applies a set of rules to associate a property with an entity.
Entity: is a primary data object that is critical to the user. It is usually a person, place, thing, or thing that will be recorded in the database.
Attributes: An entity includes an attribute, which is a feature, a cosmetic component, a quality, a quantity, or an attribute.
Paradigm: Normalization consists of a few steps that can reduce the babysitter to get more satisfying physical me, these steps are called paradigms.
First paradigm: A table that does not contain duplicate columns is attributed to the first normal form.
Second paradigm: If a table is attributed to the first normal form and contains only columns that depend on the primary key, then the second paradigm.
Third paradigm: If a table is attributed to the second normal form and contains only those columns that are not transitive and dependent on the primary key, then the third paradigm.
Two Information Requirements Modeling:
1. Top-down Modeling approach: The use of concrete data elements to organize these elements into various dimensions and indicators,
2. Bottom-up modeling approach: From the user's point of view design, the advantage is that the designer can transfer paper a common theme or business field to transport
3. Development. Is the combination of top-down and bottom-up approaches.
4. For example: Sales revenue should be expressed in terms of budget and reality.
Index: The actual income of product sales, the budget of product sales, the estimated collection of product sales
Dimensions: Products that have been sold.
Three Design Data Warehouse, frequently ask the department user several questions?
1. Tasks undertaken by the user's department
2. User's responsibilities in the Department
3. What reports do users need to complete the task?
4. Where do you get this information now?
5. What is the information to be handled?
6. Is the information generated in response to user needs or in periodic statements?
7. Did the user enter the information into the worksheet for further analysis?
Indicators (forecast sales, actual sales, forecast deviations)
Four Building a multidimensional data model
To build a multidimensional database:
1. Select the business process that is used to analyze the subject of the modeling.
Modeling topics: For example, to create a marketing strategy by analyzing the consumer's propensity to purchase through product lines and regions, the data model theme is "sales."
2. Determines the granularity of the fact table.
The fact table granularity usually represents the lowest level of each related dimension. Selecting the granularity of "days" means that each record in the time dimension represents one day.
3. A peacekeeping layer that distinguishes each fact table.
The defined granularity is related to the dimension.
4. A metric that distinguishes a fact table.
Metrics include not only the data itself, but also the new values that you can calculate from existing data. When designing a data model, you must make a decision: whether to store the results of the calculations in the fact table or to obtain these values at run time. such as: ratio.
5. Determine the properties of each dimension table.
In general, the number of attributes for each dimension table defined should be kept to a minimum.
6. Let the user validate the data model.
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