Data Mining: Concepts and Techniques — Chapter 3 — Jiawei Han

Data Mining: Concepts and Techniques — Chapter 3 — Jiawei Han Department of Computer Science University of Illinois at Urbana-Champaign www.cs.uiuc.edu/ hanj 2006 Jiawei Han and Micheline Kamber, All rights reserved January 2, 2023 Data Mining: Concepts and Techniq 1

January 2, 2023 Data Mining: Concepts and Techniq 2

Chapter 3: Data Warehousing and OLAP Technology: An Overview What is a data warehouse? A multi-dimensional data model Data warehouse architecture Data warehouse implementation From data warehousing to data mining January 2, 2023 Data Mining: Concepts and Techniq 3

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, timevariant, 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 January 2, 2023 Data Mining: Concepts and Techniq 4

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 January 2, 2023 Data Mining: Concepts and Techniq 5

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. January 2, 2023 Data Mining: Concepts and Techniq 6

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” January 2, 2023 Data Mining: Concepts and Techniq 7

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: January 2, 2023 initial loading of data and access of data Data Mining: Concepts and Techniq 8

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 January 2, 2023 Data Mining: Concepts and Techniq 9

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 January 2, 2023 Data Mining: Concepts and Techniq 10

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 repetitive historical, summarized, multidimensional integrated, consolidated ad-hoc lots of scans unit of work read/write index/hash on prim. key short, simple transaction # records accessed tens millions #users thousands hundreds DB size 100MB-GB 100GB-TB metric transaction throughput query throughput, response usage access January 2, 2023 complex query Data Mining: Concepts and Techniq 11

Why Separate Data Warehouse? High performance for both systems Warehouse—tuned for OLAP: complex OLAP queries, multidimensional view, consolidation Different functions and different data: DBMS— tuned for OLTP: access methods, indexing, concurrency control, recovery 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 January 2, 2023 Data Mining: Concepts and Techniq 12

Chapter 3: Data Warehousing and OLAP Technology: An Overview What is a data warehouse? A multi-dimensional data model Data warehouse architecture Data warehouse implementation From data warehousing to data mining January 2, 2023 Data Mining: Concepts and Techniq 13

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. January 2, 2023 Data Mining: Concepts and Techniq 14

Cube: A Lattice of Cuboids all time 0-D(apex) cuboid item time,location time,item location supplier item,location time,supplier location,supplier item,supplier time,location,supplier time,item,location time,item,supplier 1-D cuboids 2-D cuboids 3-D cuboids item,location,supplier 4-D(base) cuboid time, item, location, supplier January 2, 2023 Data Mining: Concepts and Techniq 15

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 January 2, 2023 Data Mining: Concepts and Techniq 16

Example of Star Schema time item time key day day of the week month quarter year Sales Fact Table time key item key branch key branch branch key branch name branch type location key units sold dollars sold avg sales item key item name brand type supplier type location location key street city state or province country Measures January 2, 2023 Data Mining: Concepts and Techniq 17

Example of Snowflake Schema time time key day day of the week month quarter year item Sales Fact Table time key item key branch key branch location key branch key branch name branch type units sold dollars sold avg sales Measures January 2, 2023 Data Mining: Concepts and Techniq item key item name brand type supplier key supplier supplier key supplier type location location key street city key city city key city state or province country 18

Example of Fact Constellation time time key day day of the week month quarter year item Sales Fact Table time key item key item key item name brand type supplier type location key branch key branch name branch type units sold dollars sold avg sales item key shipper key location to location location key street city province or state country dollars cost Measures January 2, 2023 time key from location branch key branch Shipping Fact Table Data Mining: Concepts and Techniq units shipped shipper shipper key shipper name location key shipper type 19

Cube Definition Syntax (BNF) in DMQL Cube Definition (Fact Table) define cube cube name [ dimension list ]: measure list Dimension Definition (Dimension Table) define dimension dimension name as ( attribute or subdimension list ) Special Case (Shared Dimension Tables) First time as “cube definition” define dimension dimension name as dimension name first time in cube cube name first time January 2, 2023 Data Mining: Concepts and Techniq 20

Defining Star Schema in DMQL define cube sales star [time, item, branch, location]: dollars sold sum(sales in dollars), avg sales avg(sales in dollars), units sold count(*) define dimension time as (time key, day, day of week, month, quarter, year) define dimension item as (item key, item name, brand, type, supplier type) define dimension branch as (branch key, branch name, branch type) define dimension location as (location key, street, city, province or state, country) January 2, 2023 Data Mining: Concepts and Techniq 21

Defining Snowflake Schema in DMQL define cube sales snowflake [time, item, branch, location]: dollars sold sum(sales in dollars), avg sales avg(sales in dollars), units sold count(*) define dimension time as (time key, day, day of week, month, quarter, year) define dimension item as (item key, item name, brand, type, supplier(supplier key, supplier type)) define dimension branch as (branch key, branch name, branch type) define dimension location as (location key, street, city(city key, province or state, country)) January 2, 2023 Data Mining: Concepts and Techniq 22

Defining Fact Constellation in DMQL define cube sales [time, item, branch, location]: dollars sold sum(sales in dollars), avg sales avg(sales in dollars), units sold count(*) define dimension time as (time key, day, day of week, month, quarter, year) define dimension item as (item key, item name, brand, type, supplier type) define dimension branch as (branch key, branch name, branch type) define dimension location as (location key, street, city, province or state, country) define cube shipping [time, item, shipper, from location, to location]: dollar cost sum(cost in dollars), unit shipped count(*) define dimension time as time in cube sales define dimension item as item in cube sales define dimension shipper as (shipper key, shipper name, location as location in cube sales, shipper type) define dimension from location as location in cube sales define dimension to location as location in cube sales January 2, 2023 Data Mining: Concepts and Techniq 23

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 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., count(), sum(), min(), max() 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() January 2, 2023 Data Mining: Concepts and Techniq 24

A Concept Hierarchy: Dimension (location) all all Europe region country city office January 2, 2023 Germany Frankfurt . . . Spain North America Canada Vancouver . L. Chan . Mexico Toronto . M. Wind Data Mining: Concepts and Techniq 25

View of Warehouses and Hierarchies Specification of hierarchies Schema hierarchy day {month quarter; week} year Set grouping hierarchy {1.10} inexpensive January 2, 2023 Data Mining: Concepts and Techniq 26

Multidimensional Data Sales volume as a function of product, month, and region Dimensions: Product, Location, Time Hierarchical summarization paths Re gi on Industry Region Year Product Category Country Quarter Product City Office Month Week Day Month January 2, 2023 Data Mining: Concepts and Techniq 27

TV PC VCR sum 1Qtr 2Qtr Date 3Qtr 4Qtr sum Total annual sales of TV in U.S.A. U.S.A Canada Mexico Country Pr od uc t A Sample Data Cube sum January 2, 2023 Data Mining: Concepts and Techniq 28

Cuboids Corresponding to the Cube all 0-D(apex) cuboid product product,date date country product,country 1-D cuboids date, country 2-D cuboids product, date, country January 2, 2023 Data Mining: Concepts and Techniq 3-D(base) cuboid 29

Browsing a Data Cube January 2, 2023 Visualization OLAP capabilities Interactive manipulation Data Mining: Concepts and Techniq 30

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) January 2, 2023 Data Mining: Concepts and Techniq 31

Fig. 3.10 Typical OLAP Operations January 2, 2023 Data Mining: Concepts and Techniq 32

A Star-Net Query Model Customer Orders Shipping Method Customer CONTRACTS AIR-EXPRESS TRUCK Time ORDER PRODUCT LINE ANNUALY QTRLY CITY DAILY Product PRODUCT ITEM PRODUCT GROUP SALES PERSON COUNTRY DISTRICT REGION Location January 2, 2023 Each circle is Promotion called a Data Mining: Concepts and Techniq footprint DIVISION Organization 33

Chapter 3: Data Warehousing and OLAP Technology: An Overview What is a data warehouse? A multi-dimensional data model Data warehouse architecture Data warehouse implementation From data warehousing to data mining January 2, 2023 Data Mining: Concepts and Techniq 34

Design of Data Warehouse: A Business Analysis Framework Four views regarding the design of a data warehouse Top-down view Data source view exposes the information being captured, stored, and managed by operational systems Data warehouse view allows selection of the relevant information necessary for the data warehouse consists of fact tables and dimension tables Business query view January 2, 2023 sees the perspectives of data in the warehouse from the view of end-user Data Mining: Concepts and Techniq 35

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 January 2, 2023 Data Mining: Concepts and Techniq 36

Data Warehouse: A Multi-Tiered Architecture Other sources Operational DBs Metadata Extract Transform Load Refresh Monitor & Integrator Data Warehouse OLAP Server Serve Analysis Query Reports Data mining Data Marts Data Sources January 2, 2023 Data Storage OLAP Engine Front-End Tools Data Mining: Concepts and Techniq 37

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 January 2, 2023 Data Mining: Concepts and Techniq 38

Data Warehouse Development: A Recommended Approach Multi-Tier Data Warehouse Distributed Data Marts Data Mart Data Mart Model refinement Enterprise Data Warehouse Model refinement Define a high-level corporate data model January 2, 2023 Data Mining: Concepts and Techniq 39

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 January 2, 2023 Data Mining: Concepts and Techniq 40

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 January 2, 2023 Data Mining: Concepts and Techniq 41

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 Multidimensional OLAP (MOLAP) Sparse array-based multidimensional storage engine Fast indexing to pre-computed summarized data Hybrid OLAP (HOLAP) (e.g., Microsoft SQLServer) Greater scalability Flexibility, e.g., low level: relational, high-level: array Specialized SQL servers (e.g., Redbricks) Specialized support for SQL queries over star/snowflake schemas January 2, 2023 Data Mining: Concepts and Techniq 42

Chapter 3: Data Warehousing and OLAP Technology: An Overview What is a data warehouse? A multi-dimensional data model Data warehouse architecture Data warehouse implementation From data warehousing to data mining January 2, 2023 Data Mining: Concepts and Techniq 43

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 January 2, 2023 Based on size, sharing, access frequency, etc. Data Mining: Concepts and Techniq 44

Cube Operation Cube definition and computation in DMQL define cube sales[item, sum(sales in dollars) city, year]: 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) (city) FROM SALES (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) () January 2, 2023 Data Mining: Concepts and Techniq 45

Iceberg Cube Computing only the cuboid cells whose count or other aggregates satisfying the condition like HAVING COUNT(*) minsup 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 January 2, 2023 Suppose 100 dimensions, only 1 base cell. How many aggregate cells if count 1? What about count 2? Data Mining: Concepts and Techniq 46

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 Base table Cust C1 C2 C3 C4 C5 Region Asia Europe Asia America Europe January 2, 2023 Index on Region Index on Type Type RecID Asia Europe Am erica RecID Retail Dealer Retail 1 1 0 0 1 1 0 Dealer 2 0 1 0 2 0 1 Dealer 3 1 0 0 3 0 1 Retail 4 0 0 1 4 1 0 0 1 0 5 0 1 Dealer 5 Data Mining: Concepts and Techniq 47

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 January 2, 2023 Data Mining: Concepts and Techniq 48

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 January 2, 2023 Data Mining: Concepts and Techniq 49

Chapter 3: Data Warehousing and OLAP Technology: An Overview What is a data warehouse? A multi-dimensional data model Data warehouse architecture Data warehouse implementation From data warehousing to data mining January 2, 2023 Data Mining: Concepts and Techniq 50

Data Warehouse Usage Three kinds of data warehouse applications Information processing Analytical processing supports querying, basic statistical analysis, and reporting using crosstabs, tables, charts and graphs multidimensional analysis of data warehouse data supports basic OLAP operations, slice-dice, drilling, pivoting Data mining January 2, 2023 knowledge discovery from hidden patterns supports associations, constructing analytical models, performing classification and prediction, and presenting the mining results using visualization tools Data Mining: Concepts and Techniq 51

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 January 2, 2023 Data Mining: Concepts and Techniq 52

An OLAM System Architecture Mining query Mining result Layer4 User Interface User GUI API OLAM Engine OLAP Engine Layer3 OLAP/OLAM Data Cube API Layer2 MDDB Filtering&Integration Database API MDDB Meta Data Filtering Layer1 Databases January 2, 2023 Data cleaning Data Data integration Warehouse Data Mining: Concepts and Techniq Data Repository 53

Chapter 3: Data Warehousing and OLAP Technology: An Overview What is a data warehouse? A multi-dimensional data model Data warehouse architecture Data warehouse implementation From data warehousing to data mining Summary January 2, 2023 Data Mining: Concepts and Techniq 54

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 Data warehouse architecture OLAP servers: ROLAP, MOLAP, HOLAP Efficient computation of data cubes Partial vs. full vs. no materialization Indexing OALP data: Bitmap index and join index OLAP query processing From OLAP to OLAM (on-line analytical mining) January 2, 2023 Data Mining: Concepts and Techniq 55

References (I) S. Agarwal, R. Agrawal, P. M. Deshpande, A. Gupta, J. F. Naughton, R. Ramakrishnan, and S. Sarawagi. On the computation of multidimensional aggregates. VLDB’96 D. Agrawal, A. E. Abbadi, A. Singh, and T. Yurek. Efficient view maintenance in data warehouses. SIGMOD’97 R. Agrawal, A. Gupta, and S. Sarawagi. Modeling multidimensional databases. ICDE’97 S. Chaudhuri and U. Dayal. An overview of data warehousing and OLAP technology. ACM SIGMOD Record, 26:65-74, 1997 E. F. Codd, S. B. Codd, and C. T. Salley. Beyond decision support. Computer World, 27, July 1993. J. Gray, et al. Data cube: A relational aggregation operator generalizing group-by, cross-tab and sub-totals. Data Mining and Knowledge Discovery, 1:29-54, 1997. A. Gupta and I. S. Mumick. Materialized Views: Techniques, Implementations, and Applications. MIT Press, 1999. J. Han. Towards on-line analytical mining in large databases. ACM SIGMOD Record, 27:97-107, 1998. V. Harinarayan, A. Rajaraman, and J. D. Ullman. Implementing data cubes efficiently. SIGMOD’96 January 2, 2023 Data Mining: Concepts and Techniq 56

References (II) C. Imhoff, N. Galemmo, and J. G. Geiger. Mastering Data Warehouse Design: Relational and Dimensional Techniques. John Wiley, 2003 W. H. Inmon. Building the Data Warehouse. John Wiley, 1996 R. Kimball and M. Ross. The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling. 2ed. John Wiley, 2002 P. O'Neil and D. Quass. Improved query performance with variant indexes. SIGMOD'97 Microsoft. OLEDB for OLAP programmer's reference version 1.0. In http://www.microsoft.com/data/oledb/olap, 1998 A. Shoshani. OLAP and statistical databases: Similarities and differences. PODS’00. S. Sarawagi and M. Stonebraker. Efficient organization of large multidimensional arrays. ICDE'94 OLAP council. MDAPI specification version 2.0. In http://www.olapcouncil.org/research/apily.htm, 1998 E. Thomsen. OLAP Solutions: Building Multidimensional Information Systems. John Wiley, 1997 P. Valduriez. Join indices. ACM Trans. Database Systems, 12:218-246, 1987. J. Widom. Research problems in data warehousing. CIKM’95 . January 2, 2023 Data Mining: Concepts and Techniq 57

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