数据仓库及应用-数据模型-元数据ppt课件

1、元数据和数据模型元数据和数据模型杭诚方杭诚方 教授教授MetadataMetadataMetadata is very important in the data warehouse environment. Metadata is often described as data about data. Metadata contains information on the location, the structure, and meaning of data, mapping information, and a guide to the algorithms used for summ

2、arization between detail and summary data. Metadata Metadata Metadata contains detailed descriptions of the location, structure, and meaning of data; keys and indexes of the data; the algorithms and business rules used to transform and summarize data.Metadata is used throughout the DW, from extracti

3、on stage through the access stage. Metadata is used throughout the DW, from extraction stage through the access stage. Metadata Metadata Metadata answers the following types of question:What information is available, by subject area, and when did we start collecting that data?How was this summarizat

4、ion created?What queries are available to access the data? What business assumptions have been made? How do I find the data I need?How old is the data?What does that value mean? Metadata Metadata Metadata can be classified into:Technical metadata that contains information about data warehouse data f

5、or use by data warehouse designers and administrators when carrying out data warehouse development and management tasks. Business metadata contains information that gives users an easy-to-understand perspective of the information stored in the data warehouse.Data warehouse operational information. O

6、perational InformationOperational InformationData history (snapshots, versions); Data ownership; Data extract audit trail; Data usage data;Used by the load, management, and access processes for scheduling data loads or end user access. Metadata UsersMetadata UsersChoosing the Metadata Location Choos

7、ing the Metadata Location Where it is stored is product-specific, the metadata resides in the database and usually on the data warehouse server. This is the preferred method. Metadata may be located on a separate database on another machine. What is Data ModelingWhat is Data ModelingData modeling ha

8、s been an art that first gained recognition since Dr. Peter Chens 1976 article which illustrated his new-found approach called Entity-Relationship Modeling. Since then it has become the standard approach used towards designing databases. By properly modeling an organizations data, the database desig

9、ner can eliminate data redundancies which are a key source for in-accurate information and ineffective systems. Why Data Modeling Is ImportantWhy Data Modeling Is ImportantVisualization of the business world: Generally speaking, a model is an abstraction and reflection of the real world.The essence

10、of the database architecture: The data model plays the role of a guideline, or plan, to implement the database.Data Warehouse ModelingData Warehouse ModelingHow should the data warehouse databases be designed to best support the needs of the data warehouse users? Answering that question is the task

11、of the data modeler. Data modeling is, by necessity, part of every data processing task, and data warehousing is no exception. Three Types of Models Three Types of Models in DW Environmentin DW EnvironmentIt is important to understand the three types of models involved in the transformation process

12、from the operational environment to a decision support system:The corporate data modelThe data warehouse data modelThe departmental data warehouse design The Corporate Data Model The Corporate Data Model The corporate data model is an enterprise-wide view of the data and its relationships. It normal

13、ly includes a high-level model which is an overview of each subject data area and the relationships between them, as well as logical data models for each subject data area. These models are the basis for developing both the enterprises online transaction processing (OLTP) systems and data warehouses

14、. The corporate data model is a very good place to start the process of building a data warehouse. It provides a foundation for integration and unification at an intellectual level. The Corporate Data ModelThe Corporate Data ModelThe Data Warehouse Data Model The Data Warehouse Data Model The data w

15、arehouse data model is sometimes referred to as an enterprise data warehouse model or data warehouse design. It represents an integrated, subject-oriented, and very granular base of strategic information which serves as a single source for the decision support environment. The data warehouse data mo

16、del maintains this integrated, detailed level of information so that all the departments and other internal organizations of the enterprise can benefit from a consistent, integrated source of decision support information. Corporate Data Model to Data Corporate Data Model to Data Warehouse Model Tran

17、sformation Warehouse Model Transformation Once the enterprise has a corporate data model, the transformation process into the data warehouse data model can begin:Removal of purely operational dataAddition of an element of time to the key structure of the data warehouse if one is not already presentA

18、ddition of appropriate derived dataTransformation of data relationships into data artifactsAccommodating the different levels of granularity found in the data warehouseMerging like data from different tables togetherRemoving Operational Data Removing Operational Data Adding an Element of Time to the

19、 Adding an Element of Time to the Warehouse Key Warehouse Key Adding Derived Data Adding Derived Data Creating Relationship Artifacts Creating Relationship Artifacts Changing Granularity of Data Changing Granularity of Data Merging Tables Merging Tables The conditions are: The tables share a common key (or partial key)The data from the different