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

2. 2 of 25 2 of 58 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/ www-sal.cs.uiuc.edu/~hanj/bk2/ And information on SAS is available at: www.sas.com www.sas.com

3. 3 of 25 3 of 52 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. 4 of 25 4 of 52 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. 5 of 25 5 of 52 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. 6 of 25 6 of 52 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. 7 of 25 7 of 52 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. 8 of 25 8 of 52 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. 9 of 25 9 of 52 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. 10 of 25 10 of 52 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. 11 of 25 11 of 52 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. 12 of 25 12 of 52 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. 13 of 25 13 of 52 OLTP Vs. OLAP OLTPOLAP UsersClerk, IT professionalKnowledge worker FunctionDay to day operationsDecision support DB DesignApplication-orientedSubject-oriented Data Current, up-to-date detailed, flat relational Isolated Historical, summarized, multidimensional, integrated, consolidated UsageRepetitiveAd-hoc Access Read/write Index/hash on prim. Key Lots of scans Unit of WorkShort, simple transactionComplex query # Records AccessedTensMillions # UsersThousandsHundreds DB Size100MB-GB100GB-TB MetricTransaction throughputQuery throughput, response

14. 14 of 25 14 of 52 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. 15 of 25 15 of 52 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. 16 of 25 16 of 52 SAS Rapid Data Warehouse Methodology The SAS Rapid Data Warehouse Methodology facilitates the development of high quality data warehousing environments The methodology is based on experience that was gained from hundreds of warehousing projects

17. 17 of 25 17 of 52 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. 18 of 25 18 of 52 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 Ongoing Maintenance and Administration Assessment Requirements Design Deployment Review Construction Final Test

19. 19 of 25 19 of 52 Assessment Phase Phase 1 – Assessment: Identify the organization’s readiness for undertaking a data warehousing project. Ongoing Maintenance and Administration Assessment Requirements Design Deployment Review ConstructionFinal Test

20. 20 of 25 20 of 52 Requirements Phase Ongoing Maintenance and Administration Assessment Requirements Design Deployment Review ConstructionFinal Test Phase 2 – Requirements: Gather the business requirements and define the acceptance criteria.

21. 21 of 25 21 of 52 Design Phase Ongoing Maintenance and Administration Assessment Requirements Design Deployment Review Construction Final Test Phase 3 – Design: Analyze and design the warehouse system architecture. Confirm the acceptance test criteria.

22. 22 of 25 22 of 52 Construction Phase Ongoing Maintenance and Administration Assessment Requirements Design Deployment Review Construction Final Test 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. 23 of 25 23 of 52 Final Test Phase Ongoing Maintenance and Administration Assessment Requirements Design Deployment Review Construction Final Test Phase 5 – Final Test: Test the warehouse and ensure that it meets the specifications of the requirements document.

24. 24 of 25 24 of 52 Deployment Phase Ongoing Maintenance and Administration Assessment Requirements Design Deployment Review Construction Final Test Phase 6 – Deployment: Roll out to the environment and perform an acceptance test. Ensure knowledge transfer and user access throughout the organization.

25. 25 of 25 25 of 52 Review Phase Ongoing Maintenance and Administration Assessment Requirements Design Deployment Review Construction Final Test Phase 7 – Review: Review the project process, the deployment process, and the impact on the organization.

26. 26 of 25 26 of 52 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. 27 of 25 27 of 52 A Data Cube Of Dollars Sold

28. 28 of 25 28 of 52 Adding A 4 th Dimension

29. 29 of 25 29 of 52 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. 30 of 25 30 of 52 Cube: A Lattice Of Cuboids all timeitemlocationsupplier time,itemtime,location time,supplier item,location item,supplier location,supplier time,item,location time,item,supplier time,location,supplier item,location,supplier time, item, location, supplier 0-D(apex) cuboid 1-D cuboids 2-D cuboids 3-D cuboids 4-D(base) cuboid

31. 31 of 25 31 of 52 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. 32 of 25 32 of 52 Example Star Schema time_key day day_of_the_week month quarter year time location_key street city state_or_province country location Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales Measures item_key item_name brand type supplier_type item branch_key branch_name branch_type branch

33. 33 of 25 33 of 52 Example Snowflake Schema time_key day day_of_the_week month quarter year time location_key street city_key location Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales Measures item_key item_name brand type supplier_key item branch_key branch_name branch_type branch supplier_key supplier_type supplier city_key city state_or_province country city

34. 34 of 25 34 of 52 Example Fact Constellation time_key day day_of_the_week month quarter year time location_key street city province_or_state country location Measures item_key item_name brand type supplier_type item branch_key branch_name branch_type branch Shipping Fact Table time_key item_key shipper_key from_location to_location dollars_cost units_shipped shipper_key shipper_name location_key shipper_type shipper Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales

35. 35 of 25 35 of 52 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. 36 of 25 36 of 52 A Concept Hierarchy: Dimension (Location) All EuropeNorth America MexicoCanadaSpainGermany Vancouver M. WindL. Chan... All Region Office Country TorontoFrankfurt City

37. 37 of 25 37 of 52 Multidimensional Data Sales volume as a function of product, month, and region Product Region Month Dimensions: Product, Location, Time Hierarchical summarization paths Industry Region Year Category Country Quarter Product City Month Week Office Day

38. 38 of 25 38 of 52 A Sample Data Cube Total annual sales of TV in U.S.A. Date Product Country sum TV VCR PC 1Qtr 2Qtr 3Qtr 4Qtr U.S.A Canada Mexico sum

39. 39 of 25 39 of 52 Cuboids Corresponding To The Cube all product date country product,dateproduct,countrydate, country product, date, country 0-D (apex) cuboid 1-D cuboids 2-D cuboids 3-D (base) cuboid

40. 40 of 25 40 of 52 Browsing A Data Cube Visualization OLAP capabilities Interactive manipulation

41. 41 of 25 41 of 52 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. 42 of 25 42 of 52 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)

43. 43 of 25 43 of 52

44. 44 of 25 44 of 52 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. 45 of 25 45 of 52 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. 46 of 25 46 of 52 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. 47 of 25 47 of 52 Data Warehouse Extract Transform Load Refresh OLAP Engine Analysis Query Reports Data mining Monitor & Integrator Metadata Data Sources Front-End Tools Serve Data Marts Operational DBs other sources Data Storage OLAP Server Multi-Tiered Architecture

48. 48 of 25 48 of 52 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. 49 of 25 49 of 52 Data Warehouse Development: A Recommended Approach Define a High-Level Corporate Data Model Data Mart Distributed Data Marts Enterprise Data Warehouse Multi-Tier Data Warehouse Model Refinement

50. 50 of 25 50 of 52 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

51. 51 of 25 51 of 52 OLAP Server Architectures (cont…) Hybrid OLAP (HOLAP) –User flexibility, e.g., low level: relational, high- level: array Specialized SQL Servers –Specialized support for SQL queries over star/snowflake schemas

52. 52 of 25 52 of 52 Summary Today we took an overview of data warehousing We really have barely scratched the surface

53. 53 of 25 53 of 52 Questions ?