Data Warehousing & OLAP

Transcription

1 Data Warehousing & OLAP Data Mining: Concepts and Techniques Chapter 3 Jiawei Han and An Introduction to Database Systems C.J.Date, Eighth Eddition, Addidon Wesley,

2 What is Data Warehousing? What is OLAP? What is a Data Cube, what is a Cuboid? What is ROLAP, MOLAP, HOLAP What is 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. W. H. Inmon Data warehousing: The process of constructing and using data warehouses 2

3 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 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. 3

4 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 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 4

5 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 OLTP vs. OLAP OLTP OLAP users clerk, IT professional knowledge worker function day to day operations decision support DB design application-oriented subject-oriented data current, up-to-date detailed, flat relational isolated usage repetitive ad-hoc historical, summarized, multidimensional integrated, consolidated access read/write lots of scans index/hash on prim. key unit of work short, simple transaction complex query # records accessed tens millions #users thousands hundreds DB size 100MB-GB 100GB-TB metric transaction throughput query throughput, response 5

6 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 Note: There are more and more systems which perform OLAP analysis directly on relational databases What is OLAP? The term OLAP ( online analytical processing ) was coined in a white paper written for Arbor Software Corp. in 1993 Interactive process of creating, managing, analyzing and reporting on data Analyzing large quantities of data in realtime 6

7 OLAP Data is perceived and manipulated as though it were stored in a multidimensional array Ideas are explained in terms of conventional SQL-styled tables Data aggregation Data aggregation (agregação) in many different ways The number of possible groupings quickly becomes large The user has to consider all groupings Analytical processing problem 7

8 For two dimensions Spreadsheet (Excel) with spreadsheet formulas calculations For more than two dimensions We will require several spreadsheet tables -> Data explosion Queries for supplier-and-parts database 1) Get the total shipment quantity 2) Get total shipment quantities by supplier 3) Get total shipment quantities by part 4) Get the shipment by supplier and part 8

9 SP S# P# QTY S1 P1 300 S1 P2 200 S2 P1 300 S2 P2 400 S3 P2 200 S4 P SELECT SUM(QTY) AS TOTQTY FROM SP GROUP BY () ; TOTQTY

10 2. SELECT S#, SUM(QTY) AS TOTQTY FROM SP GROUP BY (S#) ; S# S1 S2 S3 S4 TOTQTY SELECT P#, SUM(QTY) AS TOTQTY FROM SP GROUP BY (P#) ; P# P1 P2 TOTQTY

11 4. SELECT S#, P#, SUM(QTY) AS TOTQTY FROM SP GROUP BY (S#,P#), S# P# TOTQTY S1 P1 300 S1 P2 200 S2 P1 300 S2 P2 400 S3 P2 200 S4 P2 200 Drawbacks Formulation so many similar but distinct queries is tedious Executing the queries is expensive Make life easier, more efficient computation Single query GROUPING SETS, ROLLUP, CUBE options Added to SQL standard

12 GROUPING SETS Execute several queries simultaneously SELECT S#, P#, SUM (QTY) AS TOTQTY FROM SP GROUP BY GROUPING SETS ( (S#), (P#) ) ; Single results table Not a relation!! null missing information S# P# TOTQTY S1 null 500 S2 null 700 S3 null 200 S4 null 200 null P1 600 null P SELECT CASE GROUPING ( S# ) WHEN 1 THEN?? S# P# TOTQTY ELSE S# S1!! 500 AS S#, S2!! 700 CASE GROUPING ( P# ) S3!! 200 WHEN 1 THEN!! S4!! 200 ELSE P#?? P1 600 AS P#, SUM ( QTY ) AS TOTQTY FROM SP?? P GROUP BY GROUPING SETS ( ( S# ), ( P# ) ); 12

13 ROLLUP SELECT S#,P#, SUM ( QTY ) AS TOTQTY FROM SP GROUP BY ROLLUP (S#, P#) ; S# P# TOTQTY S1 P1 300 S1 P2 200 S2 P1 300 S2 P2 400 S3 P2 200 S4 P2 200 S1 null 500 S2 null 700 S3 null 200 S4 null 200 null null 1600 GROUP BY GROUPING SETS ( ( S#, P# ), ( S# ), ( ) ) ROLLUP The quantities have been roll up (estender) for each supplier Rolled up along supplier dimension GROUP BY ROLLUP (A,B,...,Z) (A,B,...,Z) (A,B,...) (A,B) (A) () GROUP BY ROLLUP (A,B) is not symmetric in A and B! 13

14 CUBE SELECT S#, P#, SUM ( QTY ) AS TOTQTY FROM SP GROUP BY CUBE ( S#, P#) ; S# P# TOTQTY S1 P1 300 S1 P2 200 S2 P1 300 S2 P2 400 S3 P2 200 S4 P2 200 S1 null 500 S2 null 700 S3 null 200 S4 null 200 null P1 600 null P null null 1600 GROUP BY GROUPING SETS ( (S#, P#), ( S# ), ( P# ), ( ) ) CUBE Confusing term CUBE (?) Derived from the fact that in multidimensional terminology,data values are stored in cells of a multidimensional array or a hypercube The actual physical storage my differ In our example cube has just two dimensions (supplier, part) The two dimensions are unequal (no square rectangle..) Means group by all possible subsets of the set {A, B,..., Z } 14

15 CUBE Means group by all possible subsets of the set {A, B,..., Z } M={A, B,..., Z }, M =N Power Set (Algebra) P(M):={U U M}, P(M) =2 N..proof by induction Subset represent different grade of summarization Data Mining: such a subset is called a Cuboid Cross Tabulations Display query results as cross tabulations More readable way Formatted as a simple array Example: two dimensions (supplier and parts) P1 P2 Total S S S S

16 What is a Data Cube? Data Mining definition 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) time(day, week, month, quarter, year)...hierarchy Fact table contains measures (numerical values, such as dollars_sold) and keys to each of the related dimension tables Cuboid (Data Mining Definition) Names in data warehousing literature: The n-d cuboid, which holds the lowest level of summarization, is called a base cuboid.. {{A},{B},..} 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 16

17 Cube: A Lattice of Cuboids...(Power Set) all time item location supplier D(apex) cuboid D cuboids time,item time,location item,location location,supplier D cuboids time,supplier item,supplier time,location,supplier time,item,location time,item,supplier item,location,supplier time, item, location, supplier 2 4 =16 D cuboids D(base) cuboid Conceptual Modeling of Data Warehouses Modeling data warehouses: dimensions & measures instead of relational model Subject, facilitates on-line data analysis oriented Most popular model is the multidimensional model Most common modeling paradigm: Star schema Data warehouse contains a large central table (fact table) Contains the data without redundancy A set of dimension tables (each for each dimension) 17

18 time Example of Star Schema time_key day day_of_the_week month quarter year branch branch_key branch_name branch_type Measures Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales item item_key item_name brand type supplier_type location location_key street city state_or_province country Snowflake schema 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 18

19 time Example of Snowflake Schema time_key day day_of_the_week month quarter year branch branch_key branch_name branch_type Measures Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales item item_key item_name brand type supplier_key location location_key street city_key supplier supplier_key supplier_type city city_key city state_or_province country Fact constellations Fact constellations: Multiple fact tables share dimension tables, viewed as a collection of stars, therefore called galaxy schema or fact constellation 19

20 time time_key day day_of_the_week month quarter year branch branch_key branch_name branch_type Example of Fact Constellation Measures Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales item item_key item_name brand type supplier_type location location_key street city province_or_state country Shipping Fact Table time_key item_key shipper_key from_location to_location dollars_cost units_shipped shipper shipper_key shipper_name location_key shipper_type Hierarchies Independent variables are often related in hierarchies (taxonomy) Determine ways in which dependent data can be aggregated Temporal hierarchy Seconds, minutes, hours, days, weeks, months, years Same data can be aggregated in many different ways Same independent variable can belong to different hierarchies 20

21 all Hierarchy - Location all region Europe... North_America country Germany... Spain Canada... Mexico city Frankfurt... Vancouver... Toronto office L. Chan... M. Wind View of Warehouses and Hierarchies Specification of hierarchies Schema hierarchy day < {month < quarter; week} < year Set_grouping hierarchy {1..10} < inexpensive 21

22 Multidimensional Data Sales volume as a function of product, month, and region Dimensions: Product, Location, Time Hierarchical summarization paths Product Industry Region Year Category Country Quarter Product City Month Week Month Office Day Measures of Data Cube: Three Categories (Depending on the aggregate functions) 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() Holistic: if there is no constant bound on the storage size needed to describe a subaggregate. E.g., median(), mode(), rank() 22

23 Drill up and down Drill up: going from a lower level of aggregation to a higher Drill down: means the opposite Difference between drill up and roll up Roll up: creating the desired groupings or aggregations Drill up: accessing the aggregations Example for drill down: Given the total shipment quantity, get the total quantities for each individual supplier A Sample Data Cube TV PC VCR sum Product Date 1Qtr 2Qtr 3Qtr 4Qtr sum Total annual sales of TV in Portugal Portugal Spain Germany Country sum 23

24 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 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) Fig Typical OLAP Operations 24

25 Multi-dimensional query Language No standard yet.. DMQL, MDX,.. MDX was introduced by Microsoft with Microsoft SQL Server OLAP Services in around 1998, as the language component of the OLE DB for OLAP API. More recently, MDX has appeared as part of the XML for Analysis API. Microsoft proposed that the MDX is a standard, and its adoption among application writers and other OLAP providers is steadily increasing. No normalization theory that could serve as a scientific basis for designing multi-dimensional databases What Is MDX? MDX is Multi Dimensional EXpressions MDX is the syntax for querying an Analysis Services database MDX is part of the OLE DB for OLAP spec MDX is the key for all advanced analytical capabilities of Analysis Services 25

26 Comparison To SQL SQL Construct SELECT CREATE OLAP construct SELECT (MDX) DSO object model DROP INSERT DELETE MDX Basics MDX allows easy navigation in the multi dimensional space It understands the MD concepts of cube, dimension, level, member and cell It is used for Queries full statements (SELECT FROM) Business modeling defining calculated members using MDX Expressions not a full statement 26

27 MDX Queries vs. MDX Expressions MDX Queries Full statements (SELECT FROM) Usually generated by a query tools and applications such as Excel MDX Sample App deals in queries MDX Expressions Partial MDX statements Define a calculated member, or a set, or member properties, etc. Returns a single value (which may be a set) MDX Constructs Members: an item in a hierarchy [John Doe] [2001] [2001].[Q1].[Jan] Tuple: an intersection of 2 or more members ([Product].[Drink].[Beverages], [Customers].[USA]) ([Product].[Non-Consumable], [2001]) Sets: a group of tuples or members {[John Doe], [Jane Doe]} { ( [Non-Consumable], USA ), ( Beverages, Mexico ) } [2001].Children TopCount(Store.[Store Name]. Members, 10, Sales) 27

28 Background Select on axis (x), on axis (y), on axis (z) From [cubename] Every Cell Has A Name... 28

29 Every Cell Has A Name... Every Cell Has A Name... 29

30 Every Cell Has A Name... Basic MDX Query [WITH MEMBER SET] SELECT [<axis_specification> [, <axis_specification>...]] FROM [<cube_specification>] [WHERE [<slicer_specification>]] 30

31 A query has one or more dimensions. The query above has two. (The first three dimensions (=axes) that are found in MDX queries are known as rows, columns and pages) SELECT{[Time].[1997].[Q1],[Time].[1997].[Q2]}ON COLUMNS,{[Warehouse].[All Warehouses].[USA]} ON ROWSFROM WarehouseWHERE ([Measures].[Warehouse Sales]) Curled brackets "{}" are used in MDX to represent a set of members of a dimension or group of dimensions Sections of a Basic MDX Query Cube source cube for query Axes Collection of members from different dimensions organized as a set of tuples For example, an axis that includes Time and Product dimensions will result in aggregation along these dimensions (cell will include members of these two dimensions) Members selected dimension attributes that are included in output cube Measures (facts) of a fact table are treated as members of a Measures dimension Slice filter that selects cells in output cube 31

32 Example MDX Query SELECT {[Measures].[StoreSales]} on columns, {[Time].[2002].[Q3], [Time].[2002].[Q4]} on rows FROM SalesCube WHERE ([Store].[States].[USA]) 32

33 MDX Notation for schema Periods separate schema parts Ex. [Products].[Food].[Dairy] No spaces in names unless square brackets are used Examples Dimension: [Time] Hierarchy: [Time].[Fiscal] Level: [Time].[Fiscal].[2000].[Q3] Member: Time.Fiscal.2000.Q2.May WITH Operator Use to: Specify a calculated member Define named sets Example WITH MEMBER [Measures].[DaysWorked] AS [Measures].[EndDate] [Measures].[StartDate] SELECT { [Measures].[StartDate], [Measures].[EndDate], [Measures]. [DaysWorked] } ON COLUMNS, { [Employee].[Department] } ON ROWS FROM workcube 33

34 WHERE (slice) IMPORTANT NOTE: If a dimension does not appear on a filter axis, the default member of that dimension is used Usually, the default is the ALL member Common Operators and Functions Comma construct a set by enumerating tuples Ex. {[Time].[2001].[Jan], [Time].[2001].[Feb]. } Colon construct a set by specifying a range Ex. { [Time].[2001].[Jan] : [Time].[2001].[Nov] }.Members returns set of all members Ex. [Customers].Members Comments /* */ - can be multi-line // - to end of line -- - to end of line 34

35 Common Operators and Functions (cont d) CrossJoin() cross-product of members or tuples in two different sets Product all possible combinations Ex. Construct a set Ex. CrossJoin ( { [Time].[2000].[Jan] : [Time].[2000].[Dec] }, { [Product].[Brand].Members } )... Common Operators and Functions (cont d) Filter() reduce a set by including only those elements that satisfy some criteria Arguments Boolean Expression Set Returns subset Ex. Filter ( { [Product].[Brand].Members }, [Measures].[Sales] >= 500 ) 35

36 Common Operators and Functions (cont d) Order() order a set Arguments Ordering criterion Set Flag option (ex. Ascending, descending) Hierarchical order can be complex Ex. Order ( { [Product].[Brand].Members }, ([Measures].[Sales], [Time].[2000]), BDESC ) Boolean Operators = Equal < Less than <= Less than or equal > Greater than >= Greater than or equal <> Not equal IsEmpty(Expression) If expression is an empty set, return true AND OR NOT 36

37 Example SELECT {[Time].[2001].[Aug], [Product].[Bandaids]} ON COLUMNS FROM medsupplycube WHERE { [Measures].[Costs] } /analysis-services.aspx All BI WebCasts - /bi.aspx?tab=webcasts&id=all MDX References msdn.microsoft.com/en-us/library /ms aspx 37

38 Summary: Data Warehouse and OLAP Technology Why data warehousing? A multi-dimensional model of a data warehouse Star schema, snowflake schema, fact constellations A data cube consists of dimensions & measures OLAP operations: drilling, rolling, slicing, dicing and pivoting Implementation & Computation of DW and Data Cube 38