04OLAP[1].pdf

Data Mining: Concepts and Techniques (3rd ed.) — Chapter 4 — Jiawei Han, Micheline Kamber, and Jian Pei University of Illinois at Urbana-Champaign & Simon Fraser University ©2011 Han, Kamber & Pei. All rights reserved....

Data Mining: Concepts and Techniques (3rd ed.) — Chapter 4 — Jiawei Han, Micheline Kamber, and Jian Pei University of Illinois at Urbana-Champaign & Simon Fraser University ©2011 Han, Kamber & Pei. All rights reserved. 1  Chapter 4: Data Warehousing and On-line Analytical Processing Data Warehouse: Basic Concepts Data Warehouse Modeling: Data Cube and OLAP Data Warehouse Design and Usage Data Warehouse Implementation Data Generalization by Attribute-Oriented Induction Summary 2  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.”—W. H. Inmon Data warehousing: The process of constructing and using data warehouses 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 4  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. 5  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” 6  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 7  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 historical, detailed, flat relational summarized, multidimensional isolated integrated, consolidated usage repetitive ad-hoc 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 8  Why a 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 9 Data Warehouse: A Multi-Tiered Architecture Monitor & OLAP Server Other Metadata sources Integrator Analysis Operational Extract Query DBs Transform Data Serve Reports Load Refresh Warehouse Data mining Data Marts Data Sources Data Storage OLAP Engine Front-End Tools 10  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 11 Extraction, Transformation, and Loading (ETL) 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 12  Metadata Repository Meta data is the data defining warehouse objects. It stores: Description of the structure of the data warehouse schema, view, dimensions, hierarchies, derived data defn, data mart locations and contents Operational meta-data data lineage (history of migrated data and transformation path), currency of data (active, archived, or purged), monitoring information (warehouse usage statistics, error reports, audit trails) The algorithms used for summarization The mapping from operational environment to the data warehouse Data related to system performance warehouse schema, view and derived data definitions Business data business terms and definitions, ownership of data, charging policies 13  Chapter 4: Data Warehousing and On-line Analytical Processing Data Warehouse: Basic Concepts Data Warehouse Modeling: Data Cube and OLAP Data Warehouse Design and Usage Data Warehouse Implementation Data Generalization by Attribute-Oriented Induction Summary 14  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. 15  Cube: A Lattice of Cuboids all 0-D (apex) cuboid time item location supplier 1-D cuboids time,location item,location location,supplier time,item 2-D cuboids time,supplier item,supplier time,location,supplier 3-D cuboids time,item,location time,item,supplier item,location,supplier 4-D (base) cuboid time, item, location, supplier 16  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 17  Example of Star Schema time time_key item day item_key day_of_the_week Sales Fact Table item_name month brand quarter time_key type year supplier_type item_key branch_key branch location location_key branch_key location_key branch_name units_sold street branch_type city dollars_sold state_or_province country avg_sales Measures 18  Example of Snowflake Schema time time_key item day item_key supplier day_of_the_week Sales Fact Table item_name supplier_key month brand supplier_type quarter time_key type year item_key supplier_key branch_key branch location location_key location_key branch_key units_sold street branch_name city_key branch_type dollars_sold city city_key avg_sales city state_or_province Measures country 19  Example of Fact Constellation time time_key item Shipping Fact Table day item_key day_of_the_week Sales Fact Table item_name time_key month brand quarter time_key type item_key year supplier_type shipper_key item_key branch_key from_location branch location_key location to_location branch_key location_key dollars_cost branch_name units_sold street branch_type dollars_sold city units_shipped province_or_state avg_sales country shipper Measures shipper_key shipper_name location_key shipper_type 20  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 21  Data Cube 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 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 View of Warehouses and Hierarchies Specification of hierarchies Schema hierarchy day < {month < quarter; week} < year Set_grouping hierarchy {1..10} < inexpensive 23  Multidimensional Data Sales volume as a function of product, month, and region Dimensions: Product, Location, Time Hierarchical summarization paths Industry Region Year Category Country Quarter Product Product City Month Week Office Day Month 24  A Sample Data Cube Total annual sales Date of TVs in U.S.A. 1Qtr 2Qtr 3Qtr 4Qtr sum TV PC U.S.A VCR Country sum Canada Mexico sum 25  Cuboids Corresponding to the Cube all 0-D (apex) cuboid product date country 1-D cuboids product,date product,country date, country 2-D cuboids 3-D (base) cuboid product, date, country 26  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) 27 Fig. 3.10 Typical OLAP Operations 28  A Star-Net Query Model Customer Orders Shipping Method Customer CONTRACTS AIR-EXPRESS ORDER TRUCK PRODUCT LINE Time Product ANNUALY QTRLY DAILY PRODUCT ITEM PRODUCT GROUP CITY SALES PERSON COUNTRY DISTRICT REGION DIVISION Location Each circle is called a footprint Promotion Organization 29 Browsing a Data Cube Visualization OLAP capabilities Interactive manipulation 30  Chapter 4: Data Warehousing and On-line Analytical Processing Data Warehouse: Basic Concepts Data Warehouse Modeling: Data Cube and OLAP Data Warehouse Design and Usage Data Warehouse Implementation Data Generalization by Attribute-Oriented Induction Summary 31  Design of 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 32  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 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 33  Data Warehouse Development: A Recommended Approach Multi-Tier Data Warehouse Distributed Data Marts Enterprise Data Data Data Mart Mart Warehouse Model refinement Model refinement Define a high-level corporate data model 34  Data Warehouse Usage Three kinds of data warehouse applications Information processing supports querying, basic statistical analysis, and reporting using crosstabs, tables, charts and graphs Analytical processing multidimensional analysis of data warehouse data supports basic OLAP operations, slice-dice, drilling, pivoting Data mining knowledge discovery from hidden patterns supports associations, constructing analytical models, performing classification and prediction, and presenting the mining results using visualization tools 35 From On-Line Analytical Processing (OLAP) to On Line Analytical Mining (OLAM) Why online analytical mining? High quality of data in data warehouses DW contains integrated, consistent, cleaned data Available information processing structure surrounding data warehouses ODBC, OLEDB, Web accessing, service facilities, reporting and OLAP tools OLAP-based exploratory data analysis Mining with drilling, dicing, pivoting, etc. On-line selection of data mining functions Integration and swapping of multiple mining functions, algorithms, and tasks 36  Chapter 4: Data Warehousing and On-line Analytical Processing Data Warehouse: Basic Concepts Data Warehouse Modeling: Data Cube and OLAP Data Warehouse Design and Usage Data Warehouse Implementation Data Generalization by Attribute-Oriented Induction Summary 37  Efficient Data Cube Computation Data cube can be viewed as a lattice of cuboids 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 with L levels? n T   ( Li 1) i 1 Materialization of data cube Materialize every (cuboid) (full materialization), none (no materialization), or some (partial materialization) Selection of which cuboids to materialize Based on size, sharing, access frequency, etc. 38  The “Compute Cube” Operator Cube definition and computation in DMQL define cube sales [item, city, year]: sum (sales_in_dollars) compute cube sales Transform it into a SQL-like language (with a new operator cube by, introduced by Gray et al.’96) () SELECT item, city, year, SUM (amount) FROM SALES (city) (item) (year) CUBE BY item, city, year Need compute the following Group-Bys (city, item) (city, year) (item, year) (date, product, customer), (date,product),(date, customer), (product, customer), (date), (product), (customer) (city, item, year) () 39  Indexing OLAP Data: Bitmap Index 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 A recent bit compression technique, Word-Aligned Hybrid (WAH), makes it work for high cardinality domain as well [Wu, et al. TODS’06] Base table Index on Region Index on Type Cust Region Type RecIDAsia Europe America RecID Retail Dealer C1 Asia Retail 1 1 0 0 1 1 0 C2 Europe Dealer 2 0 1 0 2 0 1 C3 Asia Dealer 3 1 0 0 3 0 1 C4 America Retail 4 0 0 1 4 1 0 C5 Europe Dealer 5 0 1 0 5 0 1 40  Indexing OLAP Data: Join Indices Join index: JI(R-id, S-id) where R (R-id, …)  S (S-id, …) Traditional indices map the values to a list of record ids It materializes relational join in JI file and speeds up relational join In data warehouses, join index relates the values of the dimensions of a start schema to rows in the fact table. E.g. fact table: Sales and two dimensions city and product A join index on city maintains for each distinct city a list of R-IDs of the tuples recording the Sales in the city Join indices can span multiple dimensions 41  Efficient Processing OLAP Queries Determine which operations should be performed on the available cuboids Transform drill, roll, etc. into corresponding SQL and/or OLAP operations, e.g., dice = selection + projection Determine which materialized cuboid(s) should be selected for OLAP op. Let the query to be processed be on {brand, province_or_state} with the condition “year = 2004”, and there are 4 materialized cuboids available: 1) {year, item_name, city} 2) {year, brand, country} 3) {year, brand, province_or_state} 4) {item_name, province_or_state} where year = 2004 Which should be selected to process the query? Explore indexing structures and compressed vs. dense array structs in MOLAP 42  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 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 43  Chapter 4: Data Warehousing and On-line Analytical Processing Data Warehouse: Basic Concepts Data Warehouse Modeling: Data Cube and OLAP Data Warehouse Design and Usage Data Warehouse Implementation Data Generalization by Attribute-Oriented Induction Summary 44  Attribute-Oriented Induction Proposed in 1989 (KDD ‘89 workshop) Not confined to categorical data nor particular measures How it is done? Collect the task-relevant data (initial relation) using a relational database query Perform generalization by attribute removal or attribute generalization Apply aggregation by merging identical, generalized tuples and accumulating their respective counts Interaction with users for knowledge presentation 45 Attribute-Oriented Induction: An Example Example: Describe general characteristics of graduate students in the University database Step 1. Fetch relevant set of data using an SQL statement, e.g., Select * (i.e., name, gender, major, birth_place, birth_date, residence, phone#, gpa) from student where student_status in {“Msc”, “MBA”, “PhD” } Step 2. Perform attribute-oriented induction Step 3. Present results in generalized relation, cross-tab, or rule forms 46  Class Characterization: An Example Name Gender Major Birth-Place Birth_date Residence Phone # GPA Initial Jim M CS Vancouver,BC, 8-12-76 3511 Main St., 687-4598 3.67 Woodman Canada Richmond Relation Scott M CS Montreal, Que, 28-7-75 345 1st Ave., 253-9106 3.70 Lachance Canada Richmond Laura Lee F Physics Seattle, WA, USA 25-8-70 125 Austin Ave., 420-5232 3.83 … … … … … Burnaby … … … Removed Retained Sci,Eng, Country Age range City Removed Excl, Bus VG,.. Gender Major Birth_region Age_range Residence GPA Count Prime M Science Canada 20-25 Richmond Very-good 16 Generalized F Science Foreign 25-30 Burnaby Excellent 22 Relation … … … … … … … Birth_Region Canada Foreign Total Gender M 16 14 30 F 10 22 32 Total 26 36 62 47 Basic Principles of Attribute-Oriented Induction Data focusing: task-relevant data, including dimensions, and the result is the initial relation Attribute-removal: remove attribute A if there is a large set of distinct values for A but (1) there is no generalization operator on A, or (2) A’s higher level concepts are expressed in terms of other attributes Attribute-generalization: If there is a large set of distinct values for A, and there exists a set of generalization operators on A, then select an operator and generalize A Attribute-threshold control: typical 2-8, specified/default Generalized relation threshold control: control the final relation/rule size 48  Attribute-Oriented Induction: Basic Algorithm InitialRel: Query processing of task-relevant data, deriving the initial relation. PreGen: Based on the analysis of the number of distinct values in each attribute, determine generalization plan for each attribute: removal? or how high to generalize? PrimeGen: Based on the PreGen plan, perform generalization to the right level to derive a “prime generalized relation”, accumulating the counts. Presentation: User interaction: (1) adjust levels by drilling, (2) pivoting, (3) mapping into rules, cross tabs, visualization presentations. 49  Presentation of Generalized Results Generalized relation: Relations where some or all attributes are generalized, with counts or other aggregation values accumulated. Cross tabulation: Mapping results into cross tabulation form (similar to contingency tables). Visualization techniques: Pie charts, bar charts, curves, cubes, and other visual forms. Quantitative characteristic rules: Mapping generalized result into characteristic rules with quantitative information associated with it, e.g., grad( x)  male( x)  birth_ region( x) "Canada"[t :53%] birth_ region( x) " foreign"[t : 47%]. 50  Mining Class Comparisons Comparison: Comparing two or more classes Method: Partition the set of relevant data into the target class and the contrasting class(es) Generalize both classes to the same high level concepts Compare tuples with the same high level descriptions Present for every tuple its description and two measures support - distribution within single class comparison - distribution between classes Highlight the tuples with strong discriminant features Relevance Analysis: Find attributes (features) which best distinguish different classes 51 Concept Description vs. Cube-Based OLAP Similarity: Data generalization Presentation of data summarization at multiple levels of abstraction Interactive drilling, pivoting, slicing and dicing Differences: OLAP has systematic preprocessing, query independent, and can drill down to rather low level AOI has automated desired level allocation, and may perform dimension relevance analysis/ranking when there are many relevant dimensions AOI works on the data which are not in relational forms 52  Chapter 4: Data Warehousing and On-line Analytical Processing Data Warehouse: Basic Concepts Data Warehouse Modeling: Data Cube and OLAP Data Warehouse Design and Usage Data Warehouse Implementation Data Generalization by Attribute-Oriented Induction Summary 53  Summary Data warehousing: A multi-dimensional model of a data warehouse A data cube consists of dimensions & measures Star schema, snowflake schema, fact constellations OLAP operations: drilling, rolling, slicing, dicing and pivoting Data Warehouse Architecture, Design, and Usage Multi-tiered architecture Business analysis design framework Information processing, analytical processing, data mining, OLAM (Online Analytical Mining) Implementation: Efficient computation of data cubes Partial vs. full vs. no materialization Indexing OALP data: Bitmap index and join index OLAP query processing OLAP servers: ROLAP, MOLAP, HOLAP Data generalization: Attribute-oriented induction 54  References (I) S. 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Answering queries with aggregation using views. VLDB'96 P. Valduriez. Join indices. ACM Trans. Database Systems, 12:218-246, 1987. J. Widom. Research problems in data warehousing. CIKM’95 K. Wu, E. Otoo, and A. Shoshani, Optimal Bitmap Indices with Efficient Compression, ACM Trans. on Database Systems (TODS), 31(1): 1-38, 2006 56 Surplus Slides 57  Compression of Bitmap Indices Bitmap indexes must be compressed to reduce I/O costs and minimize CPU usage—majority of the bits are 0’s Two compression schemes: Byte-aligned Bitmap Code (BBC) Word-Aligned Hybrid (WAH) code Time and space required to operate on compressed bitmap is proportional to the total size of the bitmap Optimal on attributes of low cardinality as well as those of high cardinality. WAH out performs BBC by about a factor of two 58