1. © 2009 Pearson Education, Inc. Publishing as Prentice Hall 1 Lecture 14: Data Warehousing Modern Database Management 9 th Edition Jeffrey A. Hoffer, Mary B. Prescott, Heikki Topi

2. Chapter 11 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 2Definition 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 Non-updatable: Read-only, periodically refreshed Non-updatable: Read-only, periodically refreshed Data Mart: Data Mart: A data warehouse that is limited in scope A data warehouse that is limited in scope

3. Chapter 11 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 3 History Leading to Data Warehousing Improvement in database technologies, especially relational DBMSs Improvement in database technologies, especially relational DBMSs Advances in computer hardware, including mass storage and parallel architectures Advances in computer hardware, including mass storage and parallel architectures Emergence of end-user computing with powerful interfaces and tools Emergence of end-user computing with powerful interfaces and tools Advances in middleware, enabling heterogeneous database connectivity Advances in middleware, enabling heterogeneous database connectivity Recognition of difference between operational and informational systems Recognition of difference between operational and informational systems

4. Chapter 11 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 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) Table 11-1 – Comparison of Operational and Informational Systems

5. Chapter 11 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 5 Issues with Company-Wide View Inconsistent key structures Inconsistent key structures Synonyms Synonyms Free-form vs. structured fields Free-form vs. structured fields Inconsistent data values Inconsistent data values Missing data Missing data See figure 11-1 for example

6. Chapter 11 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 6 Figure 11-1 Examples of heterogeneous data

7. Chapter 11 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 7 Organizational Trends Motivating Data Warehouses No single system of records No single system of records Multiple systems not synchronized Multiple systems not synchronized Organizational need to analyze activities in a balanced way Organizational need to analyze activities in a balanced way Customer relationship management Customer relationship management Supplier relationship management Supplier relationship management

8. Chapter 11 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 8 Data Warehouse Architectures 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)

9. Chapter 11 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 9 Figure 11-2 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

10. Chapter 11 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 10 Figure 11-3 Dependent data mart with operational data store: a three-level architecture E T L Single ETL for (EDW) enterprise data warehouse (EDW) Simpler data access ODS ODS provides option for obtaining current data Dependent data marts loaded from EDW

11. Chapter 11 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 11 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 11-4 Logical data mart and real time warehouse architecture

12. Chapter 11 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 12 Source: adapted from Strange (1997). Table 11-2 – Data Warehouse Versus Data Mart

13. Chapter 11 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 13 Figure 11-5 Three-layer data architecture for a data warehouse

14. Chapter 11 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 14 Data Characteristics Status vs. Event Data Status Event = a database action (create/update/delete) that results from a transaction Figure 11-6 Example of DBMS log entry

15. Chapter 11 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 15 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 11-7 Transient operational data

16. Chapter 11 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 16 Periodic data are never physically altered or deleted once they have been added to the store Data Characteristics Transient vs. Periodic Data Figure 11-8 Periodic warehouse data

17. Chapter 11 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 17 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

18. Chapter 11 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 18 Derived Data Objectives Objectives Ease of use for decision support applications Ease of use for decision support applications Fast response to predefined user queries Fast response to predefined user queries Customized data for particular target audiences Customized data for particular target audiences Ad-hoc query support Ad-hoc query support Data mining capabilities Data mining capabilities Characteristics Characteristics Detailed (mostly periodic) data Detailed (mostly periodic) data Aggregate (for summary) Aggregate (for summary) Distributed (to departmental servers) Distributed (to departmental servers) star schema Most common data model = star schema (also called “dimensional model”)

19. Chapter 11 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 19 star schema Figure 11-9 Components of a star schema Fact tables contain factual or quantitative data Dimension tables contain descriptions about the subjects of the business 1:N relationship between dimension tables and fact tables Excellent for ad-hoc queries, but bad for online transaction processing Dimension tables are denormalized to maximize performance

20. Chapter 11 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 20 Figure 11-10 Star schema example Fact table provides statistics for sales broken down by product, period and store dimensions

21. Chapter 11 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 21 Figure 11-11 Star schema with sample data

22. Chapter 11 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 22 Issues Regarding Star Schema Dimension table keys must be surrogate (non- intelligent and non-business related), because: Dimension table keys must be surrogate (non- intelligent and non-business related), because: Keys may change over time Keys may change over time Length/format consistency Length/format consistency Granularity of Fact Table–what level of detail do you want? Granularity of Fact Table–what level of detail do you want? Transactional grain–finest level Transactional grain–finest level Aggregated grain–more summarized Aggregated grain–more summarized Finer grains  better market basket analysis capability Finer grains  better market basket analysis capability Finer grain  more dimension tables, more rows in fact table Finer grain  more dimension tables, more rows in fact table Duration of the database–how much history should be kept? Duration of the database–how much history should be kept? Natural duration–13 months or 5 quarters Natural duration–13 months or 5 quarters Financial institutions may need longer duration Financial institutions may need longer duration Older data is more difficult to source and cleanse Older data is more difficult to source and cleanse

23. Chapter 11 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 23 Size of Fact Table Depends on the number of dimensions and the grain of the fact table Depends on the number of dimensions and the grain of the fact table Number of rows = product of number of possible values for each dimension associated with the fact table Number of rows = product of number of possible values for each dimension associated with the fact table Example: assume the following for Figure 11-11: Example: assume the following for Figure 11-11: Total rows calculated as follows (assuming only half the products record sales for a given month): Total rows calculated as follows (assuming only half the products record sales for a given month):

24. Chapter 11 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 24 Figure 11-12 Modeling dates Fact tables contain time-period data  Date dimensions are important

25. Chapter 11 © 2009 Pearson Education, Inc. Publishing as Prentice Hall Variations of the Star Schema Multiple Facts Tables Multiple Facts Tables Can improve performance Can improve performance Often used to store facts for different combinations of dimensions Often used to store facts for different combinations of dimensions Conformed dimensions Conformed dimensions Factless Facts Tables Factless Facts Tables No nonkey data, but foreign keys for associated dimensions No nonkey data, but foreign keys for associated dimensions Used for: Used for: Tracking events Tracking events Inventory coverage Inventory coverage 25

26. Chapter 11 © 2009 Pearson Education, Inc. Publishing as Prentice Hall Normalizing Dimension Tables Multivalued Dimensions Multivalued Dimensions Facts qualified by a set of values for the same business subject Facts qualified by a set of values for the same business subject Normalization involves creating a table for an associative entity between dimensions Normalization involves creating a table for an associative entity between dimensions Hierarchies Hierarchies Sometimes a dimension forms a natural, fixed depth hierarchy Sometimes a dimension forms a natural, fixed depth hierarchy Design options Design options Include all information for each level in a single denormalized table Include all information for each level in a single denormalized table Normalize the dimension into a nested set of 1:M table relationships Normalize the dimension into a nested set of 1:M table relationships 26

27. Chapter 11 © 2009 Pearson Education, Inc. Publishing as Prentice Hall Slowly Changing Dimensions (SCD) Need to maintain knowledge of the past Need to maintain knowledge of the past One option: for each changing attribute, create a current value field and many old- valued fields (multivalued) One option: for each changing attribute, create a current value field and many old- valued fields (multivalued) Better option: create a new dimension table row each time the dimension object changes, with all dimension characteristics at the time of change Better option: create a new dimension table row each time the dimension object changes, with all dimension characteristics at the time of change 27

28. Chapter 11 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 28 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

29. Chapter 11 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 29 Online Analytical Processing (OLAP) Tools The use of a set of graphical tools that provides users with multidimensional views of their data and allows them to analyze the data using simple windowing techniques The use of a set of graphical tools that provides users with multidimensional views of their data and allows them to analyze the data using simple windowing techniques Relational OLAP (ROLAP) Relational OLAP (ROLAP) Traditional relational representation Traditional relational representation Multidimensional OLAP (MOLAP) Multidimensional OLAP (MOLAP) Cube structure Cube structure OLAP Operations OLAP Operations Cube slicing–come up with 2-D view of data Cube slicing–come up with 2-D view of data Drill-down–going from summary to more detailed views Drill-down–going from summary to more detailed views

30. Chapter 11 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 30 Figure 11-21 Slicing a data cube

31. Chapter 11 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 31 Figure 11-22 Example of drill-down Summary report Drill-down with color added Starting with summary data, users can obtain details for particular cells

32. Chapter 11 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 32 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