1. 1 Data Warehousing Data Warehousing

2. 2 Objectives Definition of terms Definition of terms Reasons for information gap between information needs and availability Reasons for information gap between information needs and availability Reasons for need of data warehousing Reasons for need of data warehousing Describe three levels of data warehouse architectures Describe three levels of data warehouse architectures List four steps of data reconciliation List four steps of data reconciliation Describe two components of star schema Describe two components of star schema Estimate fact table size Estimate fact table size Design a data mart Design a data mart

3. 3Definition Data Warehouse: Data Warehouse: A subject-oriented, integrated, time-variant, non- updatable collection of data used in support of management decision-making processes A subject-oriented, integrated, time-variant, non- updatable collection of data used in support of management decision-making processes Subject-oriented: e.g. customers, patients, students, products Subject-oriented: e.g. customers, patients, students, products Integrated: Consistent naming conventions, formats, encoding structures; from multiple data sources Integrated: Consistent naming conventions, formats, encoding structures; from multiple data sources Time-variant: Can study trends and changes Time-variant: Can study trends and changes Nonupdatable: Read-only, periodically refreshed Nonupdatable: Read-only, periodically refreshed Data Mart: Data Mart: A data warehouse that is limited in scope A data warehouse that is limited in scope

4. 4 Need for Data Warehousing Integrated, company-wide view of high-quality information (from disparate databases) Integrated, company-wide view of high-quality information (from disparate databases) Separation of operational and informational systems and data (for improved performance) Separation of operational and informational systems and data (for improved performance)

5. 5 Source: adapted from Strange (1997).

6. 6 Data Warehouse Architectures Generic Two-Level Architecture Generic Two-Level Architecture Independent Data Mart Independent Data Mart Dependent Data Mart and Operational Data Store Dependent Data Mart and Operational Data Store Logical Data Mart and Real-Time Data Warehouse Logical Data Mart and Real-Time Data Warehouse Three-Layer architecture Three-Layer architecture ETL All involve some form of extraction, transformation and loading (ETL)

7. 7 Figure 11-2: Generic two-level data warehousing architecture E T L One, company- wide warehouse Periodic extraction  data is not completely current in warehouse

8. 8 Figure 11-3 Independent data mart data warehousing architecture Data marts: Mini-warehouses, limited in scope E T L Separate ETL for each independent data mart Data access complexity due to multiple data marts

9. 9 Figure 11-4 Dependent data mart with operational data store: a three-level architecture E T L Single ETL for enterprise data warehouse(EDW) Simpler data access ODS ODS provides option for obtaining current data Dependent data marts loaded from EDW

10. 10 E T L Near real-time ETL for Data Warehouse ODS data warehouse ODS and data warehouse are one and the same Data marts are NOT separate databases, but logical views of the data warehouse  Easier to create new data marts Figure 11-5 Logical data mart and real time warehouse architecture

11. 11 Figure 11-6 Three-layer data architecture for a data warehouse

12. 12 Data Characteristics Status vs. Event Data Status Event = a database action (create/update/delete) that results from a transaction Figure 11-7 Example of DBMS log entry

13. 13 Data Characteristics Transient vs. Periodic Data With transient data, changes to existing records are written over previous records, thus destroying the previous data content Figure 11-8 Transient operational data

14. 14 Other Data Warehouse Changes New descriptive attributes New descriptive attributes New business activity attributes New business activity attributes New classes of descriptive attributes New classes of descriptive attributes Descriptive attributes become more refined Descriptive attributes become more refined Descriptive data are related to one another Descriptive data are related to one another New source of data New source of data

15. 15 The User Interface Metadata (data catalog) Identify subjects of the data mart Identify subjects of the data mart Identify dimensions and facts Identify dimensions and facts Indicate how data is derived from enterprise data warehouses, including derivation rules Indicate how data is derived from enterprise data warehouses, including derivation rules Indicate how data is derived from operational data store, including derivation rules Indicate how data is derived from operational data store, including derivation rules Identify available reports and predefined queries Identify available reports and predefined queries Identify data analysis techniques (e.g. drill-down) Identify data analysis techniques (e.g. drill-down) Identify responsible people Identify responsible people

16. 16 Data Mining and Visualization Knowledge discovery using a blend of statistical, AI, and computer graphics techniques Knowledge discovery using a blend of statistical, AI, and computer graphics techniques Goals: Goals: Explain observed events or conditions Explain observed events or conditions Confirm hypotheses Confirm hypotheses Explore data for new or unexpected relationships Explore data for new or unexpected relationships Techniques Techniques Statistical regression Statistical regression Decision tree induction Decision tree induction Clustering and signal processing Clustering and signal processing Affinity Affinity Sequence association Sequence association Case-based reasoning Case-based reasoning Rule discovery Rule discovery Neural nets Neural nets Fractals Fractals Data visualization–representing data in graphical/multimedia formats for analysis Data visualization–representing data in graphical/multimedia formats for analysis