Data warehousing. Han, J. and M. Kamber. Data Mining: Concepts and Techniques Morgan Kaufmann.

Transcription

1 Data warehousing Han, J. and M. Kamber. Data Mining: Concepts and Techniques Morgan Kaufmann.

2 KDD process Application Pattern Evaluation Data Mining Task-relevant Data Data Warehouse Selection Data Cleaning Data Integration Databases

3 Data mining is the process of discovering interesting knowledge from large amounts of data stored in databases, data warehouses and/or other information repositories.

4 Data mining and Business Intelligence Making Decisions End User Data Presentation Visualization Techniques Data Mining Information Discovery Business Analyst Data Analyst Data Exploration Statistical Analysis, Querying and Reporting Data Warehouses / Data Marts OLAP, Multi-dimensional Analysis Data Sources Paper, Files, Information Providers, Database Systems, OLTP DBA

5 Architecture of a typical data mining system Graphical user interface Pattern evaluation Data mining engine Knowledge base Data cleaning Data integration Database or data warehouse server Database Filtering Data warehouse

6 Customer Cust_ID Name Address Age Income Credit_info Item item_id Name Brand Category Type Price Supplier Employee Emp_ID Name Department Group Salary Commission Purchases Trans_ID Cust_id Emp_id Date Time Pay_method amount Items_sold Trans_ID Item_ID Qty A relational database fragment

7 Queries List of items sold in previous quarter Total sales last month, grouped by salesperson Number of sales transactions in December Salesperson with highest amount of sales Data warehouse Integrates data from various sources Data organized on a historical perspective Presents different levels of summarized data Multi-dimensional structure dimension: attribute cell: aggregate measures

8 Data source in Chicago Client Data source in New York Clean Transform Integrate Load Data warehouse Query and analysis tools Data source in Toronto Client Data source in Vancouver Typical data warehouse architecture

9 Chicago New York Toronto Vancouver Q1 Address (city) Time (qtr.) Q2 Q3 Q4 Multi-dimensional data Chicago New York Toronto Vancouver Jan Address (city) Time (month) Feb March Drill down on data for Q1 T1 T2 T3 T4 T5 T6 Item-types T1 T2 T3 T4 T5 T6 Item-types Roll-up on Address USA Canada Address (country) Q1 Q2 Q3 Q4 T1 T2 T3 T4 T5 T6 Item-types

10 Data Warehouse A decision support database that is maintained separately from the organization s operational database Collection of data this is subject-oriented integrated time-variant nonvolatile

11 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.

12 Data Warehouse Integrated Constructed by integrating multiple, heterogeneous data sources relational databases, flat files, on-line transaction records Data cleaning and data integration Ensure consistency in naming conventions, encoding structures, attribute measures, etc. among different data sources e.g., Hotel price: currency, tax, breakfast covered, etc. Data is converted when moved to the warehouse.

13 Data Warehouse Time Variant The time horizon for data warehouse is significantly longer than that of operational systems. Operational database: current value data. Data warehouse: provides 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

14 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.

15 Data Warehouse vs. Heterogeneous DBMS Traditional heterogeneous DB integration Build wrappers/integrators 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 the individual heterogeneous sites involved, and results are integrated into a global answer set Complex information filtering, compete for resources with local processing Data warehouse: update-driven, high performance Information from heterogeneous sources is integrated in advance and stored in warehouses for direct query and analysis

16 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): Users and system orientation: transaction vs. decision support Data contents: current, detailed vs. historical, consolidated Database design: ER + application vs. star schema + subject View: current, local vs. evolutionary, integrated Access patterns: update vs. read-only but complex queries

17 OLTP vs. OLAP OLTP 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 OLAP historical, summarized, multidimensional integrated, consolidated access read/write multiple large scans index on primary 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

18 Why Separate Data Warehouse? Maintain 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 data and function 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

19 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 n-d base cube is called a base cuboid. The lattice of cuboids forms a data cube.

20 Time Multi-dimensional cube Sales by Item, Time, Location, Supplier SALES Item Supplier Location

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

22 yearly data (keep all data) quarterly data (up to 20 years) old quarterly data (archived) old monthly data (archived) retail monthly data (up to 15 years) monthly data (up to 15 years) old monthly data (archived) old weekly data (archived) weekly data (up to 7 years) special event effects (up to 30 years) old detailed data (archived) current detailed data (up to 3 years)

23 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

24 Star Schema Example time 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 province_or_state country

25 Snowflake Schema example time 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_key city province_or_state country city_key city province_or_street country

26 Fact Constellation example time time_key day day_of_the_week month quarter year branch branch_key branch_name branch_type Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales Multiple fact tables, sharing dimensions Collection of stars fact constellation or galaxy schema item item_key item_name brand type supplier_type location location_key street city province_or_street 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

27 Data warehouse vs. data marts Data warehouse Enterprise-wide scope Subjects that span the organization Fact constellation used to model multiple, interrelated subjects Data mart Department-wide scope Departmental subset of data warehouse Star, snowflake schema Star schema is more efficient and thereby popular

28 Computing Measures Measure: numerical value at each point in the data cube e.g. <time= Q1, location= Chicago, item= xyz >: avg-amount Need to be able to efficiently compute measures

29 Measure types Distributive: E.g., count(), sum(), min(), max(). 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: E.g., avg(), min_n(), standard_deviation(). 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. Holistic: E.g., median(), mode(), rank(). There is no constant bound on the storage size needed to describe a sub-aggregate. No constant function with M arguments (constant M) that characterizes the computation. Can be difficult to compute efficiently approximate computation

30 Concept Hierarchy Example: Location dimension all all region Europe... North_America country Germany... Spain Canada... Mexico city Frankfurt... Vancouver... Toronto

31 Concept hierarchies Full or partial ordering Industry Region Year Category Country Quarter Product City Month Week Office Day Set-grouping hierarchy e.g. price ($0..$1000] ($0..$1000] ($0..$1000] ($0..$1000] ($0..$1000] ($0..$1000] ($0..$1000] ($0..$1000] ($0..$1000] ($0..$1000] Multiple hierarchies for an attribute price: {inexpensive, moderately_priced, expensive}

32 OLAP examples Sales volume as a function of product, month, and region Region Dimensions: Product, Location, Time Hierarchical summarization paths Industry Region Year Category Country Quarter Product Product City Month Week Office Day Month

33 A Sample Data Cube TV PC VCR sum Product Date 1Qtr 2Qtr 3Qtr 4Qtr sum Total annual sales of TV in U.S.A. U.S.A Canada Mexico Country sum

34 Chicago New York Toronto Vancouver Q1 Address (city) Time (qtr.) Q2 Q3 Q4 Drill down, Roll up Chicago New York Toronto Vancouver Jan Address (city) Time (month) Feb March Drill down on data for Q1 T1 T2 T3 T4 T5 T6 Item-types T1 T2 T3 T4 T5 T6 Item-types Roll-up on Address USA Canada Address (country) Q1 Q2 Q3 Q4 T1 T2 T3 T4 T5 T6 Item-types

35 Chicago Toronto Q1 Q2 T3 T8 Dice for (location in {Chicago, Toronto} and time in {Q1} And Item in {T3, T8} Slice For Time in {Q1} Chicago New York Toronto Vancouver Time (qtr.) Q1 Q2 Q3 Q4 T1 T2 T3 T4 T5 T6 Item-types Chicago New York Toronto Vancouver T1 T2 T3 T4 T5 T6 Slicing and Dicing Slice: Selection on one dimension Dice; Selection on two or more dimensions

36 Browsing a Data Cube Visualization OLAP Interactive manipulation

37 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)

38 A Star-Net Query Model Shipping Method AIR-EXPRESS Customer Orders CONTRACTS Customer Time TRUCK ANNUALY QTRLY DAILY CITY COUNTRY ORDER PRODUCT LINE Product PRODUCT ITEM PRODUCT GROUP SALES PERSON DISTRICT REGION Location Promotion DIVISION Organization

39 Data Warehouse Design: Four Views Top-down view selection of the relevant information necessary for the data warehouse based on current and future needs Data source view exposes the information being captured, stored, and managed by operational systems (E/R models, CASE, etc) Data warehouse view fact tables and dimension tables, pre-calculated totals, counts, etc. Source information, date, time for historical context Business query view perspectives of data in the warehouse from the view of enduser

40 Data Warehouse Design Process Top-down, bottom-up approaches or a combination 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 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

41 Multi-Tiered DW 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 Data Storage OLAP Engine Front-End Tools

42 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

43 Data Marts Data warehouse designed to meet the needs of a specific group of users Should (but may not) be designed with corporate standards and accessibility in mind incorporate standards for hardware, software, networking, DBMS, naming conventions, etc. vendor s attempt to bypass IT and sell directly to end-users?

44 Data Warehouse Development: A Recommended Approach Distributed Data Marts Multi-Tier Data Warehouse Data Mart Data Mart Enterprise Data Warehouse Model refinement Model refinement Define a high-level corporate data model

45 OLAP Server Types Relational OLAP (ROLAP) Use relational or extended-relational DBMS to store and manage warehouse data and OLAP middleware 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

46 Metadata Repository Meta data is the data defining warehouse objects. It has the following kinds Description of the structure of the 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

47 Data Warehouse Back-End Tools, 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 indices and partitions Refresh propagate the updates from the data sources to the warehouse

48 Advanced examples Exploration of Data Cubes Hypothesis-driven: exploration by user, huge search space Discovery-driven pre-computed measures indicate exceptions, guide user in the data analysis, at all levels of aggregation Exception: significantly different from the value anticipated, based on a statistical model Visual cues such as background color are used to reflect the degree of exception of each cell Computation of exception indicator can be included in cube construction SelfExp: degree of surprise in cell, relative to values at same levels of aggregation InExp: degree of surprise somewhere beneath the cell, if we drill down PathExp: degree of surprise for each drill down path from cell

49 Advanced examples Example: Discovery-driven exploration

50 Advanced examples Complex Aggregation at Multiple Granularities Ex. Total sales in 2000 by Item, Region, Month, with subtotals Ex. Grouping by all subsets of {item, region, month}, find the maximum price in 2000 for each group, and the total sales generated by all maximum-price-sales Ex. Among the max-price-sales, find the min and max shelf life. Find the fraction of the total sales due to cases that have min shelf life.

51 Advances examples Sales #units, $value Sales #units, $value Ordering Sales #units, $value Supplier Product Product Supplier Product Sales %sales Supplier Product Sales volume as a % of total units sold of Product Group sales by contiguous 10-day intervals. 10 day Moving-avg of Sales, by Product Order Products by Sales-$ and group into deciles of decreasing performance.