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ITCS 6163 Data Warehousing Xintao Wu History 60s C. Bachman GE network data model Late 60s IBM IMS hierarchical data model 70 E.Codd relational model 80s SQL IBM R transaction J. Gray Late 80s-90s DB2, Oracle, informix, sybase 90sDW, internet Turing award and Turing test? Evolution of Database Technology (See Fig. 1.1) 1960s: Data collection, database creation, IMS and network DBMS 1970s: Relational data model, relational DBMS implementation 1980s: RDBMS, advanced data models (extended-relational, OO, deductive, etc.) and application-oriented DBMS (spatial, scientific, engineering, etc.) 1990s—2000s: Data mining and data warehousing, multimedia databases, and Web databases Can You Easily Answer These Questions? What is the correlation between expenditures and collection of delinquent taxes? What is the impact on revenues and expenditures of changing the operating hours of the Dept. of Motor Vehicles? What are Personnel Services costs across all departments for all funding sources? What are the effects of outsourcing specific services? What is the economic impact of the small business initiative in our district? Overview: Data Warehousing and OLAP Technology for Data Mining What is a data warehouse? Why a data warehouse? A multi-dimensional data model Data warehouse architecture Data warehouse implementation From data warehousing to data mining What is a 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 more What is a Warehouse? Collection of tools gathering data cleansing, integrating, ... querying, reporting, analysis data mining monitoring, administering warehouse What is a 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 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. 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—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. Warehouse is specialized DB Standard DB Mostly updates Many small transactions Mb-Tb of data Current snapshot Raw data Clerical users Warehouse Mostly reads Queries are long, complex Gb-Tb of data History Summarized, consolidated data Decision-makers, analysts as users 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 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 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 complex query Overview: Data Warehousing and OLAP Technology for Data Mining What a data warehouse? Why a data warehouse? A multi-dimensional data model Data warehouse architecture Data warehouse implementation From data warehousing to data mining 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 Warehouse Architecture Client Client Query & Analysis Metadata Warehouse Integration Source Source Source Why a Warehouse? Two Approaches: Query-Driven (Eager) Warehouse (Lazy) ? Source Source Query-Driven Approach Client Client Mediator Wrapper Source Wrapper Wrapper Source Source Advantages of Warehousing High query performance Queries not visible outside warehouse Local processing at sources unaffected Can operate when sources unavailable Can query data not stored in a DBMS Extra information at warehouse Modify, summarize (store aggregates) Add historical information Advantages of Query-Driven No need to copy data less storage no need to purchase data More up-to-date data Query needs can be unknown Only query interface needed at sources Overview: Data Warehousing and OLAP Technology for Data Mining What a data warehouse? Why a data warehouse? A multi-dimensional data model Data warehouse architecture Data warehouse implementation From data warehousing to data mining Modeling OLTP Systems Goal -- Update as many transactions as possible in the shortest period of time Approach Model to 3rd Normal Form (3NF) Minimize redundancy to optimize update Result Create many (hundreds) of tables Difficult for business users to understand and use Retrieval requires many JOINs = lousy performance Modeling the Data Warehouse Tuning the relational model Denormalize – Reduces the number of tables – Improves usability – Improves performance Add aggregate data (typically separate tables) – Improves performance – Degrades usability Modeling the Data Warehouse “Entity relation data models are a disaster for querying because they cannot be understood by users and they cannot be navigated usefully by DBMS software. Entity relation models cannot be used as the basis for enterprise data warehouses.” Ralph Kimball, The Data Warehouse Toolkit, 1996, John Wiley & Sons, Inc., ISBN 0-471-15337-0 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. Cube: A Lattice of Cuboids all time time,item 0-D(apex) cuboid item time,location location item,location time,supplier time,item,location supplier 1-D cuboids location,supplier 2-D cuboids item,supplier time,location,supplier 3-D cuboids time,item,supplier item,location,supplier 4-D(base) cuboid time, item, location, supplier 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 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 location_key branch_key branch_name branch_type units_sold dollars_sold avg_sales Measures item_key item_name brand type supplier_type location location_key street city province_or_street country 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 item_key item_name brand type supplier_key supplier supplier_key supplier_type location location_key street city_key city city_key city province_or_street country Example of Fact Constellation time time_key day day_of_the_week month quarter year item Sales Fact Table time_key item_key item_name brand type supplier_type item_key location_key branch_key branch_name branch_type units_sold dollars_sold avg_sales Measures time_key item_key shipper_key from_location branch_key branch Shipping Fact Table location to_location location_key street city province_or_street country dollars_cost units_shipped shipper shipper_key shipper_name location_key shipper_type Multidimensional Data Sales volume as a function of product, month, and region Dimensions: Product, Location, Time Hierarchical summarization paths Industry Region Year Product Category Country Quarter Product City Office Month Month Week Day A Sample Data Cube 2Qtr 3Qtr 4Qtr sum U.S.A Canada Mexico sum Country TV PC VCR sum 1Qtr Date Total annual sales of TV in U.S.A. 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 3-D(base) cuboid product, date, country 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 backend relational tables (using SQL) Relational Operators Select Project Join Aggregates • Add up amounts by day • In SQL: SELECT date, sum(amt) FROM SALE GROUP BY date sale prodId storeId p1 c1 p2 c1 p1 c3 p2 c2 p1 c1 p1 c2 date 1 1 1 1 2 2 amt 12 11 50 8 44 4 ans date 1 2 sum 81 48 Another Example • Add up amounts by day, product • In SQL: SELECT date, sum(amt) FROM SALE GROUP BY date, prodId sale prodId storeId p1 c1 p2 c1 p1 c3 p2 c2 p1 c1 p1 c2 date 1 1 1 1 2 2 amt 12 11 50 8 44 4 rollup drill-down sale prodId p1 p2 p1 date 1 1 2 amt 62 19 48 Aggregates Operators: sum, count, max, min, median, avg Type Distributive Algebraic holistic “Having” clause Using dimension hierarchy average by region (within store) maximum by month (within date) Cube Aggregation day 2 day 1 p1 p2 c1 p1 12 p2 11 p1 p2 c1 56 11 c1 44 c2 4 c2 c3 Example: computing sums ... c3 50 8 c2 4 8 rollup drill-down c3 50 p1 c1 67 c2 12 c3 50 129 p1 p2 c1 110 19 Aggregation Using Hierarchies day 2 day 1 p1 p2 c1 p1 12 p2 11 c1 44 c2 4 c2 c3 c3 50 customer region 8 country p1 p2 region A region B 56 54 11 8 (customer c1 in Region A; customers c2, c3 in Region B) Pivoting Fact table view: sale prodId storeId p1 c1 p2 c1 p1 c3 p2 c2 p1 c1 p1 c2 Multi-dimensional cube: date 1 1 1 1 2 2 amt 12 11 50 8 44 4 day 2 day 1 p1 p2 c1 p1 12 p2 11 p1 p2 c1 56 11 c1 44 c2 4 c2 c3 c3 50 8 c2 4 8 c3 50 Overview: Data Warehousing and OLAP Technology for Data Mining What a data warehouse? Why a data warehouse? A multi-dimensional data model Data warehouse architecture Data warehouse implementation From data warehousing to data mining 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 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 Multi-Tiered Architecture other Metadata sources Operational DBs Extract Transform Load Refresh Monitor & Integrator Data Warehouse OLAP Server Serve Analysis Query Reports Data mining Data Marts Data Sources Data Storage OLAP Engine Front-End Tools 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 What is a Data Mart ? A data mart is a small-scale data warehouse that is focused on a single department or single subject area to provide a subset of data warehouse data to address specific reporting and analysis requirements. Smaller warehouses Spans part of organization Finance Do not require enterprise-wide consensus but long term integration problems? HR Budget Purchasing Asset Mgmt. Info. Tech. 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 OLAP Server Architectures Relational OLAP (ROLAP) ROLAP - provides a Multi-dimensional view of a relational DB (e.g. MicroStrategy) 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 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 MOLAP Databases Data is stored using a proprietary format(MOLAP) Accessible only through the DB vendor’s tools Suitable only for summarized data Data may be summarized in advance or real-time Examples: PowerPlay Holos Essbase RDBMS: Indexing Strategies Select columns to be indexed: Choose combinations of columns most often used to constrain queries (“where …” clause) Queries must use constraining columns in the same order as the columns in the index Unique more efficient than non-unique. More indexes means faster query performance, but also longer transformation/load times. Types of Indexes: B-tree -- many possible values (e.g., invoice number) Bitmap -- few possible values (e.g., M/F, S/M/D/W) MOLAP versus ROLAP MOLAP ROLAP Multidimensional OLAP Relational OLAP Data stored in multidimensional cube Transformation required Data retrieved directly from cube for analysis Faster analytical processing Cube size limitations Data stored in relational database as virtual cube No transformation needed Data retrieved via SQL from database for analysis Slower analytical processing No size limitations 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. Differences among the three tasks Data Mining & Forecasting Mining the Warehouse Choose data population Select mining technique Segment data into groups Identify data patterns Forecasting Data Select trend data Choose forecast model Run forecast Display predictions Accessing & Analyzing Data Query & Reporting … retrieving data directly from the warehouse and preparing it for presentation Online Analytical Processing (OLAP) … analyzing aggregated data from a variety of perspectives Data Mining & Forecasting … analyzing and predicting data using mathematical models Query & Reporting Query the Data ... Select & filter data Retrieve results Report the Results ... Sort & group data Format & present data Save or Export Data Save queries & reports Export to other tools Publish HTML pages Query & Reporting Tools Cognos Impromptu Business Objects Crystal Info BrioQuery IQ GQL SAS Online Analytical Processing Slice and Dice ... Select dimensions Choose measures Filter by dimensions Drill Down ... Drill down hierarchies Drill through to details Present the Results Present as spreadsheet Display graphically OLAP Tools Cognos PowerPlay Business Analyzer Holos BrioAnalyzer Microstrategy Oracle Express SAS Arbor Essbase Data Mining & Forecasting Mining the Warehouse Choose data population Select mining technique Segment data into groups Identify data patterns Forecasting Data Select trend data Choose forecast model Run forecast Display predictions Data Mining sales records: tran1 tran2 tran3 tran4 tran5 tran6 cust33 cust45 cust12 cust40 cust12 cust12 p2, p5, p1, p5, p2, p9 p5, p8 p8, p11 p9 p8, p11 p9 • Trend: Products p5, p8 often bough together • Trend: Customer 12 likes product p9 Mining and Forecasting Tools Scenario 4Thought Business Miner Clementine Darwin Holos SAS 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 Data Cleaning • Of primordial interest in the warehouse • • • • creation One of the biggest problems Difficult to achieve Probability of one or many of the sources containing “dirty data” is high. Lots of manual intervention Data Cleaning Problems Data quality problems Single Source Schema level Instance level (poor schema design) (data entry errors) .Uniqueness .Misspellings Multi-source Schema level Instance level (heterogeneity) (overlapping contradicting data) Naming conflicts Inconsistent aggregation Multisource problems All the previous problems + Schema differences (translation and integration) E.g.: EmpID, CID, Sex= M/F, Sex=0/1 Instance level conflicts Duplicate records, contradicting records Different measures ($, Euros) Different aggregation levels (weeks, quarters) Overview: Data Warehousing and OLAP Technology for Data Mining What a data warehouse? Why a data warehouse? A multi-dimensional data model Data warehouse architecture Data warehouse implementation From data warehouse to data mining Data Mining: A KDD Process Pattern Evaluation Data mining: the core of knowledge discovery Data Mining process. Task-relevant Data Data Warehouse Data Cleaning Data Integration Databases Selection Steps of a KDD Process Learning the application domain: relevant prior knowledge and goals of application Creating a target data set: data selection Data cleaning and preprocessing: (may take 60% of effort!) Data reduction and transformation: Find useful features, dimensionality/variable reduction, invariant representation. Choosing functions of data mining summarization, classification, regression, association, clustering. Choosing the mining algorithm(s) Data mining: search for patterns of interest Pattern evaluation and knowledge presentation visualization, transformation, removing redundant patterns, etc. Use of discovered knowledge Data Mining and Business Intelligence Increasing potential to support business decisions Making Decisions Data Presentation Visualization Techniques Data Mining Information Discovery End User Business Analyst Data Analyst Data Exploration Statistical Analysis, Querying and Reporting Data Warehouses / Data Marts OLAP, MDA Data Sources Paper, Files, Information Providers, Database Systems, OLTP DBA From 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. Architecture of OLAM An OLAM Architecture Mining query Mining result Layer4 User Interface User GUI API OLAM Engine OLAP Engine Layer3 OLAP/OLAM Data Cube API Layer2 MDDB MDDB Meta Data Filtering&Integration Database API Filtering Layer1 Data cleaning Databases Data Data integration Warehouse Data Repository Summary Data warehouse A subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management’s decision-making process 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 OLAP servers: ROLAP, MOLAP, HOLAP From OLAP to OLAM