Data Warehousing and Decision Support

1. Data Warehousing and Decision Support Chapter 25

2. Introduction • Increasingly,organizations are analyzing current and historical data to identify useful patterns and support business strategies. • Emphasis is on complex, interactive, exploratory analysis of very large datasets created by integrating data from across all parts of an enterprise; data is fairly static. • Contrast such On-Line Analytic Processing (OLAP) with traditional On-line Transaction Processing (OLTP): mostly long queries, instead of short update Xacts.

3. Three Complementary Trends • Data Warehousing: Consolidate data from many sources in one large repository. • Loading, periodic synchronization of replicas. • Semantic integration. • OLAP: • Complex SQL queries and views. • Queries based on spreadsheet-style operations and “multidimensional” view of data. • Interactive and “online” queries. • Data Mining: Exploratory search for interesting trends and anomalies. (Another lecture!)

4. EXTERNAL DATA SOURCES OLAP Data Warehousing EXTRACT TRANSFORM LOAD REFRESH • Integrated data spanning long time periods, often augmented with summary information. • Several gigabytes to terabytes common. • Interactive response times expected for complex queries; ad-hoc updates uncommon. DATA WAREHOUSE Metadata Repository SUPPORTS DATA MINING

5. Warehousing • Growing industry: $30+ billion industry • Range from desktop to huge: • Walmart: 900-CPU, 2,700 disk, 23TBTeradata system (numbers from earlier part of this decade) • Lots of buzzwords, hype • slice & dice, rollup, MOLAP, pivot, ...

6. Road map • What is a data warehouse? • A multi-dimensional data model • Data warehouse architecture • Data warehouse implementation • From data warehousing to data mining

7. 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 asubject-oriented, integrated, time-variant, and nonvolatilecollection of data in support of management’s decision-making process.”—W. H. Inmon • Data warehousing: • The process of constructing and using data warehouses

8. more What is a Data Warehouse? • Collection of diverse data • subject oriented • aimed at executive, decision maker • often a copy of operational data • with value-added data (e.g., summaries, history) • integrated • time-varying • non-volatile

9. What is a Warehouse? • Collection of tools • gathering data • cleansing, integrating, ... • querying, reporting, analysis • data mining • monitoring, administering warehouse

10. 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

11. 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.

12. 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”

13. Data Warehouse—Nonvolatile • 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

14. Data Warehouse vs. Heterogeneous DBMS • Traditional heterogeneous DB integration: A query driven approach • Build wrappers/mediators on top of heterogeneous databases • 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

15. 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

16. OLTP vs. OLAP

17. Road map • What is a data warehouse? • A multi-dimensional data model • Data warehouse architecture • Data warehouse implementation • From data warehousing to data mining

18. From Tables and Spreadsheets to Data Cubes • A data warehouse is based on a multidimensional 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 • 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. • Each cuboid represents a different degree of summarization, or group by

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

20. 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

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

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

23. item time item_key item_name brand type supplier_type time_key day day_of_the_week month quarter year location location_key street city province_or_state country shipper branch shipper_key shipper_name location_key shipper_type branch_key branch_name branch_type Example of 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

24. Star schema vs. snowflake schema • Major difference: the dimension tables in snowflake model may be kept in normalized form to reduce redundancies • However, this saving is negligible in comparison to the typical magnitude of the fact table • Furthermore, the snowflake structure can reduce the effectiveness of browsing, since more joins will be needed to execute a query. • Hence, star schema is more popular

25. Data warehouse vs. data mart • A data warehouse collects information about subjects that span the entire organization, such as customers, items, sales, assets, and personnel. • Its scope is enterprise-wide • For data warehouses, the fact constellation schema is commonly used, since it can model multiple, interrelated subjects • A data mart is a department subject of the data warehouse that focuses on selected subjects • Its scope is department-wide • Star or snowflake schema are commonly, with the former more popular

26. Measures • A multidimensional point in the data cube space can be defined by a set of dimension-value pairs. • E.g., <time=“Q1”, location=“Vancouver”, item=“computer”> • A data cube measure is a numerical function that can be evaluated at each point in the data cube space • A measure value is computed for a given point by aggregating the data corresponding to the respective dimension-value pairs defining the given point. • Large data cube applications require efficient compuation of measures. • For this purpose, let us examine 3 categories of measures.

27. Measures of Data Cube: 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 it can be computed by an algebraic function with M arguments (where M is a bounded integer), each of which is obtained by applying a distributive aggregate function • E.g.,avg(), min_N(), standard_deviation() • avg() = sum() / coung() • Holistic: if there is no constant bound on the storage size needed to describe a subaggregate. • E.g., median(), mode(), rank() • Distributive and algebraic measures can be computed efficiently. • Holistic measures can be approximated efficiently.

28. A Concept Hierarchy: Dimension (location) all all Europe ... North_America region Germany ... Spain Canada ... Mexico country Vancouver ... city Frankfurt ... Toronto L. Chan ... M. Wind office

29. View of Hierarchies

30. A Concept Hierarchy in partial order: time • In the location example, attributes of a dimension are related by a total order, forming a concept hierarchy • Alternatively, attributes of a dimension can be organized in a partial order, forming a lattice. Year Quarter Month Week Day

31. How are concepts hierarchies useful in OLAP? • In the multidimensional model, data are organized into multiple dimensions, and each dimension contains multiple levels of abstraction defined by concept hierarchies. • This organization provides users with flexibility to view data from different perspectives. • A number of OLAP cube operators exist to materialize these different views, allowing interactive querying and analysis of the data at hand. • Roll up, drill down, slice, dice, pivot … • Thus, OLAP provides a user friendly environment for interactive data analysis

32. 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

33. 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.

34. 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

35. Browsing a Data Cube • Visualization • OLAP capabilities • Interactive manipulation

36. Typical OLAP Operations • Two major operations: • Change resolution: roll up/drill down (zoom out/in) from one dimension • Get a sub-matrix: slice, dice • Roll up (drill-up): summarize data • by climbing up hierarchy or by dimension reduction • E.g., rather than grouping the data by city, the resulting cube groups the data by country • Drill down (roll down): reverse of roll-up • from higher level summary to lower level summary or detailed data, or introducing new dimensions

37. Typical OLAP Operations • Slice:performs a selection on one dimension of the cube, resulting in a subcube. E.g., time = “Q1”; • Another explanation: picking a rectangular subset of a cube by choosing a single value for one of its dimensions, creating a new cube with one fewer dimension. • dice: defines a subcube by performing a selection on two or more dimensions. E.g., (location=“Toronto” or “Vancouver) and (time=“Q1” or “Q2”) and (item=“home entertainment” or “computer”) • Anohter explanation: produces a subcube by allowing the analyst to pick specific values of multiple dimensions.

38. Typical OLAP Operations • Pivot (rotate): • change the dimensional orientation; i.e., rotates the data axes in view in order to provide an alternative presentation of the data • Another explanation: allows an analyst to rotate the cube in space to see its various faces. • reorient the cube • 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)

39. Pivoting pivoting operation example: The whole cube is rotated, giving another perspective on the data.

40. Typical OLAP Operations

41. Road Map • What is a data warehouse? • A multi-dimensional data model • Data warehouse architecture • Data warehouse implementation • From data warehousing to data mining

42. Other sources Extract Transform Load Refresh Operational DBs Data Warehouse: A 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

43. 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, such as marketing data mart • 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

44. Data Warehouse Back-End Tools and Utilities • Data extraction • get data from multiple, heterogeneous, and external sources • Data cleaning • detect errors in the data and rectify them when possible • Data transformation • convert data from legacy or host format to warehouse format • Load • sort, summarize, consolidate, compute views, check integrity, and build indicies and partitions • Refresh • propagate the updates from the data sources to the warehouse

45. OLAP Server Architectures • Relational OLAP (ROLAP) • Use relational or extended-relational DBMS to store and manage warehouse data and OLAP middle ware • Include optimization of DBMS backend, implementation of aggregation navigation logic, and additional tools and services • Greater scalability • Multidimensional OLAP (MOLAP) • Sparse array-based multidimensional storage engine that directly implements multidimensional data and operations • Fast indexing to pre-computed summarized data • Hybrid OLAP (HOLAP)(e.g., Microsoft SQLServer) • Flexibility, e.g., low level: relational, high-level: array • Specialized SQL servers (e.g., Redbricks) • Specialized support for SQL queries over star/snowflake schemas

46. Road Map • What is a data warehouse? • A multi-dimensional data model • Data warehouse architecture • Data warehouse implementation • From data warehousing to data mining

47. () (city) (item) (year) (city, item) (city, year) (item, year) (city, item, year) Number of cuboids • Data cube can be viewed as a lattice of cuboids, each corresponds to a group-by • The bottom-most cuboid is the base cuboid • The top-most cuboid (apex) contains only one cell • How many cuboids in an n-dimensional cube? • If no hierarchy for each dimension, 2^n • Or, let Li be the # of levels associated with dimension I • + 1 to include the virtual top level, i.e., removal of the dimension in roll up

48. Materialization of data cube • No materialization: do not precompute any of the nonbase cuboids. • Leads to expensive multidimensional aggregates on the fly, which can be extremely slow • Full materialization: precompute every (cuboid) • Due to huge number of cuboids, unrealistic • Partial materialization • Selection of which cuboids to materialize, based on size, sharing, access frequency etc • A popular approach is to materialize the set of cuboids on which other frequently referenced cuboids are based • Or alternatively, compute an iceberg cube

49. Iceberg Cube • Computing only the cuboid cells whose count or other aggregates satisfying the condition like HAVING COUNT(*) >= min_sup • Motivation • Only a small portion of cube cells may be “above the water’’ in a sparse cube • Only calculate “interesting” cells—data above certain threshold • Avoid explosive growth of the cube • Efficient cube computation is detailed in chapter 4

50. Indexing OLAP Data: Bitmap Index • Indexing facilitates efficient data accessing • Index on a particular column • Each value in the column has a bit vector: bit-op is fast • The length of the bit vector: # of records in the base table • The i-th bit is set if the i-th row of the base table has the value for the indexed column • not suitable for high cardinality domains Base table Index on Region Index on Type