Data warehousing

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Introduction to Data Warehousing

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Data Mining: Concepts and Techniques — Chapter 3 — : Data Mining: Concepts and Techniques — Chapter 3 — March 14, 2009 1 Data Mining: Concepts and Techniques

Chapter 3: Data Warehousing and OLAP Technology: An Overview : 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 March 14, 2009 2 Data Mining: Concepts and Techniques

DATA WARE HOUSING : DATA WARE HOUSING March 14, 2009 3 Data Mining: Concepts and Techniques

What is Data Warehouse? : What is Data Warehouse? Defined in many different ways, but not rigorously. A decision support database that is maintained separately from the organization’s operational database Support information processing by providing a solid platform of consolidated, historical data for analysis. “A data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management’s decision-making process.”—W. H. Inmon Data warehousing: The process of constructing and using data warehouses March 14, 2009 4 Data Mining: Concepts and Techniques

Data Warehouse—Subject-Oriented : 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 March 14, 2009 5 Data Mining: Concepts and Techniques

Data Warehouse—Integrated : Data Warehouse—Integrated Constructed by integrating multiple, heterogeneous data sources relational databases, flat files, on-line transaction records March 14, 2009 6 Data Mining: Concepts and Techniques

Data Warehouse—Time Variant : 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” March 14, 2009 7 Data Mining: Concepts and Techniques

Data Warehouse—Nonvolatile : 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 March 14, 2009 8 Data Mining: Concepts and Techniques

Data Warehouse vs. Heterogeneous DBMS : 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 March 14, 2009 9 Data Mining: Concepts and Techniques

Data Warehouse vs. Operational DBMS : 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 March 14, 2009 10 Data Mining: Concepts and Techniques

OLTP vs. OLAP : OLTP vs. OLAP March 14, 2009 Data Mining: Concepts and Techniques 11

Why Separate Data Warehouse? : Why Separate Data Warehouse? High performance for both systems DBMS— tuned for OLTP: access methods, indexing, concurrency control, recovery Warehouse—tuned for OLAP: complex OLAP queries, multidimensional view, consolidation Different functions and different data: missing data: Decision support requires historical data which operational DBs do not typically maintain data consolidation: DS requires consolidation (aggregation, summarization) of data from heterogeneous sources data quality: different sources typically use inconsistent data representations, codes and formats which have to be reconciled Note: There are more and more systems which perform OLAP analysis directly on relational databases March 14, 2009 12 Data Mining: Concepts and Techniques

3-D data cube : 3-D data cube March 14, 2009 14 Data Mining: Concepts and Techniques

From Tables and Spreadsheets to Data Cubes : 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. March 14, 2009 15 Data Mining: Concepts and Techniques

3-D data cube : 3-D data cube March 14, 2009 16 Data Mining: Concepts and Techniques

4D-DATA CUBE : 4D-DATA CUBE March 14, 2009 17 Data Mining: Concepts and Techniques

Cube: A Lattice of Cuboids : Cube: A Lattice of Cuboids March 14, 2009 18 Data Mining: Concepts and Techniques time,item time,item,location time, item, location, supplier

Conceptual Modeling of Data Warehouses : 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 March 14, 2009 19 Data Mining: Concepts and Techniques

Example of Star Schema : Example of Star Schema March 14, 2009 20 Data Mining: Concepts and Techniques Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales Measures

Example of Snowflake Schema : Example of Snowflake Schema March 14, 2009 21 Data Mining: Concepts and Techniques Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales Measures

Example of Fact Constellation : Example of Fact Constellation March 14, 2009 22 Data Mining: Concepts and Techniques Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales Measures Shipping Fact Table time_key item_key shipper_key from_location to_location dollars_cost units_shipped

Cube Definition Syntax (BNF) in DMQL : Cube Definition Syntax (BNF) in DMQL Cube Definition (Fact Table) define cube []: Dimension Definition (Dimension Table) define dimension as () Special Case (Shared Dimension Tables) First time as “cube definition” define dimension as in cube March 14, 2009 23 Data Mining: Concepts and Techniques

Defining Star Schema in DMQL : 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) March 14, 2009 24 Data Mining: Concepts and Techniques

Defining Snowflake Schema in DMQL : 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)) March 14, 2009 25 Data Mining: Concepts and Techniques

Defining Fact Constellation in DMQL : 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 March 14, 2009 26 Data Mining: Concepts and Techniques

Measures of Data Cube: Three Categories : 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 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() March 14, 2009 27 Data Mining: Concepts and Techniques

A Concept Hierarchy: Dimension (location) : A Concept Hierarchy: Dimension (location) March 14, 2009 28 Data Mining: Concepts and Techniques all Europe North_America Mexico Canada Spain Germany Vancouver M. Wind L. Chan ... ... ... ... ... ... all region office country Toronto Frankfurt city

View of Warehouses and Hierarchies : View of Warehouses and Hierarchies Specification of hierarchies Schema hierarchy day < {month < quarter; week} < year Set_grouping hierarchy {1..10} < inexpensive March 14, 2009 29 Data Mining: Concepts and Techniques

Multidimensional Data : Multidimensional Data Sales volume as a function of product, month, and region March 14, 2009 30 Data Mining: Concepts and Techniques Product Region Month Dimensions: Product, Location, Time Hierarchical summarization paths Industry Region Year Category Country Quarter Product City Month Week Office Day

A Sample Data Cube : A Sample Data Cube March 14, 2009 31 Data Mining: Concepts and Techniques Total annual sales of TV in U.S.A.

Cuboids Corresponding to the Cube : Cuboids Corresponding to the Cube March 14, 2009 32 Data Mining: Concepts and Techniques all product date country product,date product,country date, country product, date, country 0-D(apex) cuboid 1-D cuboids 2-D cuboids 3-D(base) cuboid

Browsing a Data Cube : Browsing a Data Cube Visualization OLAP capabilities Interactive manipulation March 14, 2009 33 Data Mining: Concepts and Techniques

Typical OLAP Operations : 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) March 14, 2009 34 Data Mining: Concepts and Techniques

Slide 35 : March 14, 2009 35 Data Mining: Concepts and Techniques Fig. 3.10 Typical OLAP Operations

A Star-Net Query Model : A Star-Net Query Model March 14, 2009 36 Data Mining: Concepts and Techniques Shipping Method AIR-EXPRESS TRUCK ORDER Customer Orders CONTRACTS Customer Product PRODUCT GROUP PRODUCT LINE PRODUCT ITEM SALES PERSON DISTRICT DIVISION Organization Promotion CITY COUNTRY REGION Location DAILY QTRLY ANNUALY Time Each circle is called a footprint

Design of Data Warehouse: A Business Analysis Framework : 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 March 14, 2009 38 Data Mining: Concepts and Techniques

Data Warehouse Design Process : 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 March 14, 2009 39 Data Mining: Concepts and Techniques

Slide 40 : March 14, 2009 Data Mining: Concepts and Techniques 40 Data Warehouse: A Multi-Tiered Architecture Data Warehouse OLAP Engine Analysis Query Reports Data mining Monitor & Integrator Metadata Data Sources Front-End Tools Serve Data Marts Data Storage OLAP Server

Three Data Warehouse Models : 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 March 14, 2009 41 Data Mining: Concepts and Techniques

Data Warehouse Development: A Recommended Approach : Data Warehouse Development: A Recommended Approach March 14, 2009 42 Data Mining: Concepts and Techniques Define a high-level corporate data model Data Mart Data Mart Distributed Data Marts Multi-Tier Data Warehouse Enterprise Data Warehouse Model refinement Model refinement

Data Warehouse Back-End Tools and Utilities : 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 March 14, 2009 43 Data Mining: Concepts and Techniques

Metadata Repository : 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 March 14, 2009 44 Data Mining: Concepts and Techniques

OLAP Server Architectures : 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 March 14, 2009 45 Data Mining: Concepts and Techniques

Efficient Data Cube Computation : 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? 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. March 14, 2009 47 Data Mining: Concepts and Techniques

Cube Operation : Cube Operation 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 CUBE BY item, city, year Need compute the following Group-Bys (date, product, customer), (date,product),(date, customer), (product, customer), (date), (product), (customer) () March 14, 2009 48 Data Mining: Concepts and Techniques

Iceberg Cube : Iceberg Cube Computing only the cuboid cells whose count or other aggregates satisfying the condition like HAVING COUNT(*) >= minsup March 14, 2009 49 Data Mining: Concepts and Techniques 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 Suppose 100 dimensions, only 1 base cell. How many aggregate cells if count >= 1? What about count >= 2?

Indexing OLAP Data: Bitmap Index : 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 March 14, 2009 50 Data Mining: Concepts and Techniques Base table Index on Region Index on Type

Indexing OLAP Data: Join Indices : 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 March 14, 2009 51 Data Mining: Concepts and Techniques

Efficient Processing OLAP Queries : 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 March 14, 2009 52 Data Mining: Concepts and Techniques

Data Warehouse Usage : 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 March 14, 2009 54 Data Mining: Concepts and Techniques

From On-Line Analytical Processing (OLAP) to On Line Analytical Mining (OLAM) : 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 March 14, 2009 55 Data Mining: Concepts and Techniques

An OLAM System Architecture : An OLAM System Architecture March 14, 2009 56 Data Mining: Concepts and Techniques Data Warehouse Meta Data MDDB OLAM Engine OLAP Engine User GUI API Data Cube API Database API Data cleaning Data integration Layer3 OLAP/OLAM Layer2 MDDB Layer1 Data Repository Layer4 User Interface Filtering&Integration Filtering Databases Mining query Mining result

Chapter 3: Data Warehousing and OLAP Technology: An Overview : 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 March 14, 2009 57 Data Mining: Concepts and Techniques

Summary: Data Warehouse and OLAP Technology : 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) March 14, 2009 58 Data Mining: Concepts and Techniques

Slide 59 : March 14, 2009 59 Data Mining: Concepts and Techniques