Business Systems Intelligence: 3. Data Warehousing

1. Dr. Brian Mac Namee (www.comp.dit.ie/bmacnamee) Business Systems Intelligence:3. Data Warehousing

2. Acknowledgments • These notes are based (heavily) on those provided by the authors to accompany “Data Mining: Concepts & Techniques” by Jiawei Han and Micheline Kamber • Some slides are also based on trainer’s kits provided by More information about the book is available at:www-sal.cs.uiuc.edu/~hanj/bk2/ And information on SAS is available at:www.sas.com

3. Data Warehousing & OLAP Technology For Data Mining • Today we will begin to look at data warehouses, and in particular: • What is a data warehouse? • A multi-dimensional data model • Data warehouse architecture • Data warehouse implementation • Further development of data cube technology • From data warehousing to data mining

4. What Is A Data Warehouse? • Defined in many different ways, but not rigorously • A decision support database that is maintained separately from the organization’s operational database • Support information processing by providing a solid platform of consolidated, historical data for analysis “A data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management’s decision-making process” —Bill Inmon

5. Data Warehouse - Subject-Oriented • Organized around major subjects, such as customer, product, sales • Focusing on the modeling and analysis of data for decision makers, not on daily operations or transaction processing • Provide a simple and concise view around particular subject issues by excluding data that are not useful in the decision support process

6. Data Warehouse - Integrated • Constructed by integrating multiple, heterogeneous data sources • Relational databases, flat files, on-line transaction records • Data cleaning and data integration techniques are applied • Ensure consistency in naming conventions, encoding structures, attribute measures, etc. among different data sources • E.g., Hotel price: currency, tax, breakfast covered, etc. • When data is moved to the warehouse, it is converted

7. Data Warehouse - Time Variant • The time horizon for the data warehouse is significantly longer than that of operational systems • Operational database: current value data • Data warehouse data: provide information from a historical perspective (e.g., past 5-10 years) • Every key structure in the data warehouse • Contains an element of time, explicitly or implicitly • But the key of operational data may or may not contain “time element”

8. Data Warehouse - Non-Volatile • A physically separate store of data transformed from the operational environment • Operational update of data does not occur in the data warehouse environment • Does not require transaction processing, recovery, and concurrency control mechanisms • Requires only two operations in data accessing: • Initial loading of data and access of data

9. Data Warehouse Vs. Heterogeneous DBMS • Traditional heterogeneous DB integration: • Build wrappers/mediators on top of heterogeneous databases • Query driven approach • When a query is posed to a client site, a meta-dictionary is used to translate the query into queries appropriate for individual heterogeneous sites involved, and the results are integrated into a global answer set • Complex information filtering, compete for resources • Data warehouse: update-driven, high performance • Information from heterogeneous sources is integrated in advance and stored in warehouses for direct query and analysis

10. What Is OLAP? • Online Analytical Processing (OLAP) is an industry-accepted reporting technology that provides high-performance analysis and easy reporting on large volumes of data • The goal of OLAP, also known as multidimensional data analysis, is to provide fast and flexible data summarization, analysis, and reporting capabilities with the ability to view trends over time

11. What Is OLAP? (cont…) • OLAP applications, also called decision support systems (DSS), have the following features: • Enable users to look at different relationships in data by looking beyond traditional two-dimensional row and column data analysis • Offer high-performance access to large amounts of presummarized data • Give users the power to retrieve answers to multi-dimensional business questions quickly and easily • Provide slice-and-dice views of multiple relationships in large quantities of presummarized data

12. Data Warehouse Vs. Operational DBMS • OLTP (on-line transaction processing) • Major task of traditional relational DBMS • Day-to-day operations: purchasing, inventory, banking, manufacturing, payroll, registration, accounting, etc. • OLAP (on-line analytical processing) • Major task of data warehouse system • Data analysis and decision making • Distinct features (OLTP vs. OLAP): • User and system orientation: customer vs. market • Data contents: current, detailed vs. historical, consolidated • Database design: ER + application vs. star + subject • View: current, local vs. evolutionary, integrated • Access patterns: update vs. read-only but complex queries

13. OLTP Vs. OLAP

14. Why Separate Data Warehouse? • High performance for both systems • DBMS - tuned for OLTP: access methods, indexing, concurrency control, recovery • Warehouse - tuned for OLAP: complex OLAP queries, multidimensional view, consolidation. • Different functions and different data: • Missing data: Decision support requires historical data which operational DBs do not typically maintain • Data consolidation: DS requires consolidation (aggregation, summarization) of data from heterogeneous sources • Data quality: different sources typically use inconsistent data representations, codes and formats which have to be reconciled

15. Some Key Points Regarding Data Warehousing • Data warehousing has these characteristics: • Not new; existed for some time • An accepted practice • Fairly widespread, but not always well done • Many organizations now run data warehouse projects • Data warehousing is used in multiple industry sectors throughout the world • Within organizations, more and more data warehouses and data marts are used • Data warehousing is most successful when used as the foundation of an integrated information strategy

16. SAS Rapid Data Warehouse Methodology • The SAS Rapid Data Warehouse Methodology facilitates the development of high quality data warehousing environments • The methodology is based onexperience that was gainedfrom hundreds of warehousingprojects

17. Why Use A Methodology? • For the first few data warehouse projects, a methodology accomplishes the following: • Gives practitioners a roadmap to follow for success • Enables practitioners to benefit from the experience (and mistakes!) of others • For experienced practitioners, a methodology provides the following: • A checklist of tasks, roles, deliverables, and so on • An explanation of tasks to others (customer management, users, project staff)

18. SAS Rapid Data Warehouse Methodology • The SAS Rapid Data Warehouse Methodology has seven phases • The phases provide logical work groupings and milestones to verify that the project has a solid foundation Assessment Requirements Design Review Construction Final Test Deployment Ongoing Maintenanceand Administration

19. Assessment Requirements Design Construction Final Test Deployment Assessment Phase Phase 1 – Assessment:Identify the organization’sreadiness for undertakinga data warehousing project. Review Ongoing Maintenanceand Administration

20. Assessment Requirements Design Construction Final Test Deployment Requirements Phase Phase 2 – Requirements:Gather the businessrequirements and definethe acceptance criteria. Review Ongoing Maintenanceand Administration

21. Assessment Requirements Design Review Construction Final Test Deployment Ongoing Maintenanceand Administration Design Phase Phase 3 – Design:Analyze and designthe warehouse systemarchitecture. Confirm the acceptance test criteria.

22. Assessment Requirements Design Review Construction Final Test Deployment Ongoing Maintenanceand Administration Construction Phase Phase 4 – Construction:Construct and populate the warehouse. Code and test the exploitation applications and processes. Validate types of test. Perform unit and integration tests.

23. Assessment Requirements Design Review Construction Final Test Deployment Ongoing Maintenanceand Administration Final Test Phase Phase 5 – Final Test:Test the warehouse andensure that it meets thespecifications of therequirements document.

24. Assessment Requirements Design Review Construction Final Test Deployment Ongoing Maintenanceand Administration Deployment Phase Phase 6 – Deployment:Roll out to the environmentand perform an acceptance test. Ensure knowledge transfer and user access throughout the organization.

25. Assessment Requirements Design Review Construction Final Test Deployment Ongoing Maintenanceand Administration Review Phase Phase 7 – Review:Review the project process, the deployment process, and the impact on the organization.

26. From Tables & Spreadsheets to Data Cubes • A data warehouse is based on a multi-dimensional data model which views data in the form of a data cube • A data cube, such as sales, allows data to be modeled and viewed in multiple dimensions • Dimension tables, such as item (item_name, brand, type), or time (day, week, month, quarter, year) • Fact table contains measures (such as dollars_sold) and keys to each of the related dimension tables

27. A Data Cube Of Dollars Sold

28. Adding A 4th Dimension

29. N-Dimensional Data Cubes • We can continue to add dimensions indefinitley • In data warehousing literature, an n-D base cube is called a base cuboid • The top most 0-D cuboid, which holds the highest-level of summarization, is called the apex cuboid • The lattice of cuboids forms a data cube

30. Cube: A Lattice Of Cuboids all 0-D(apex) cuboid time item location supplier 1-D cuboids time,item time,location item,location location,supplier 2-D cuboids time,supplier item,supplier time,location,supplier time,item,location 3-D cuboids item,location,supplier time,item,supplier 4-D(base) cuboid time, item, location, supplier

31. Conceptual Modeling of Data Warehouses • Modeling data warehouses: dimensions & measures • Star schema: A fact table in the middle connected to a set of dimension tables • Snowflake schema: A refinement of star schema where some dimensional hierarchy is normalized into a set of smaller dimension tables, forming a shape similar to snowflake • Fact constellations: Multiple fact tables share dimension tables, viewed as a collection of stars, therefore called galaxy schema or fact constellation

32. time item time_key day day_of_the_week month quarter year item_key item_name brand type supplier_type location branch location_key street city state_or_province country branch_key branch_name branch_type Example Star Schema Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales Measures

33. time item time_key day day_of_the_week month quarter year item_key item_name brand type supplier_key supplier supplier_key supplier_type location branch location_key street city_key branch_key branch_name branch_type city city_key city state_or_province country Example Snowflake Schema Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales Measures

34. time item time_key day day_of_the_week month quarter year item_key item_name brand type supplier_type branch location branch_key branch_name branch_type location_key street city province_or_state country shipper shipper_key shipper_name location_key shipper_type Example Fact Constellation Shipping Fact Table time_key Sales Fact Table item_key time_key shipper_key item_key from_location branch_key to_location location_key dollars_cost units_sold units_shipped dollars_sold avg_sales Measures

35. Measures: Three Categories • Distributive: If the result derived by applying the function to n aggregate values is the same as that derived by applying the function on all the data without partitioning • E.g., count(), sum(), min(), max(). • Algebraic: If the result can be computed by an algebraic function with M arguments, each of which is obtained by applying a distributive aggregate function • E.g., avg(), min_N(), standard_deviation() • Holistic: If there is no constant bound on the storage size needed to describe a sub-aggregate. • E.g., median(), mode(), rank() • Measures are usually confined to numerics

36. A Concept Hierarchy: Dimension (Location) All All Region Europe ... North America Country Germany ... Spain Canada ... Mexico City Frankfurt ... Vancouver ... Toronto Office L. Chan ... M. Wind

37. Multidimensional Data • Sales volume as a function of product, month, and region Dimensions: Product, Location, Time Hierarchical summarization paths Region Industry Region Year Category Country Quarter Product City Month Week Office Day Product Month

38. Date 2Qtr 1Qtr sum 3Qtr 4Qtr TV Product U.S.A PC VCR sum Canada Country Mexico sum All, All, All A Sample Data Cube Total annual sales of TV in U.S.A.

39. Cuboids Corresponding To The Cube all 0-D (apex) cuboid country product date 1-D cuboids product,date product,country date, country 2-D cuboids 3-D (base) cuboid product, date, country

40. Visualization OLAP capabilities Interactive manipulation Browsing A Data Cube

41. Typical OLAP Operations • Roll up (drill-up): Summarize data • By climbing up hierarchy or by dimension reduction • Drill down (roll down): Reverse of roll-up • From higher level summary to lower level summary or detailed data, or introducing new dimensions • Slice and dice: • Project and select • Pivot (rotate): • Reorient the cube, visualization, 3D to series of 2D planes

42. Typical OLAP Operations (cont…) • Other operations: • Drill across: Involving (across) more than one fact table • Drill through: Through the bottom level of the cube to its back-end relational tables (using SQL)

44. Design Of A Data Warehouse: A Business Analysis Framework • Four views regarding the design of a data warehouse • Top-down view • Allows selection of the relevant information necessary for the data warehouse • Data source view • Exposes the information being captured, stored, and managed by operational systems • Data warehouse view • Consists of fact tables and dimension tables • Business query view • Sees the perspectives of data in the warehouse from the view of end-user

45. Data Warehouse Design Process • Top-down, bottom-up approaches or a combination of both • Top-down: • Starts with overall design and planning (mature) • Bottom-up: • Starts with experiments and prototypes (rapid) • From software engineering point of view • Waterfall: • Structured and systematic analysis at each step before proceeding to the next • Spiral: • Rapid generation of increasingly functional systems, short turn around time, quick turn around

46. Data Warehouse Design Process (cont…) • Typical data warehouse design process • Choose a business process to model • E.g. orders, invoices, etc. • Choose the grain (atomic level of data) of the business process • Choose the dimensions that will apply to each fact table record • Choose the measure that will populate each fact table record

47. other sources Extract Transform Load Refresh Operational DBs Multi-Tiered Architecture Monitor & Integrator OLAP Server Metadata Analysis Query Reports Data mining Serve Data Warehouse Data Marts Data Sources Data Storage OLAP Engine Front-End Tools

48. Three Data Warehouse Models • Enterprise Warehouse • Collects all of the information about subjects spanning the entire organization • Data Mart • A subset of corporate-wide data that is of value to a specific groups of users • Its scope is confined to specific, selected groups • Independent vs. dependent (directly from warehouse) data mart • Virtual Warehouse • A set of views over operational databases • Only some of the possible summary views may be materialized

49. Data Warehouse Development: A Recommended Approach Multi-Tier Data Warehouse Distributed Data Marts Enterprise Data Warehouse Data Mart Data Mart Model Refinement Model Refinement Define a High-Level Corporate Data Model

50. OLAP Server Architectures • Relational OLAP (ROLAP) • Use relational or extended-relational DBMS to store and manage warehouse data and OLAP middle ware to support missing pieces • Include optimization of DBMS backend, implementation of aggregation navigation logic, and additional tools and services • Greater scalability • Multidimensional OLAP (MOLAP) • Array-based multidimensional storage engine (sparse matrix techniques) • Fast indexing to pre-computed summarized data