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Dr. Brian Mac Namee ( www.comp.dit.ie/bmacnamee ). Business Systems Intelligence: 3. Data Warehousing. Acknowledgments. These notes are based (heavily) on those provided by the authors to accompany “Data Mining: Concepts & Techniques” by Jiawei Han and Micheline Kamber

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Business systems intelligence 3 data warehousing

Dr. Brian Mac Namee (www.comp.dit.ie/bmacnamee)

Business Systems Intelligence:3. Data Warehousing


Acknowledgments
Acknowledgments

  • These notes are based (heavily) on those provided by the authors to accompany “Data Mining: Concepts & Techniques” by Jiawei Han and Micheline Kamber

  • Some slides are also based on trainer’s kits provided by

More information about the book is available at:www-sal.cs.uiuc.edu/~hanj/bk2/

And information on SAS is available at:www.sas.com


Data warehousing olap technology for data mining
Data Warehousing & OLAP Technology For Data Mining

  • Today we will begin to look at data warehouses, and in particular:

    • What is a data warehouse?

    • A multi-dimensional data model

    • Data warehouse architecture

    • Data warehouse implementation

    • Further development of data cube technology

    • From data warehousing to data mining


What is a data warehouse
What Is A Data Warehouse?

  • Defined in many different ways, but not rigorously

    • A decision support database that is maintained separately from the organization’s operational database

    • Support information processing by providing a solid platform of consolidated, historical data for analysis

“A data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management’s decision-making process”

—Bill Inmon


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

  • 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
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
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
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


Data warehouse vs heterogeneous dbms
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


What is olap
What Is OLAP?

  • Online Analytical Processing (OLAP) is an industry-accepted reporting technology that provides high-performance analysis and easy reporting on large volumes of data

  • The goal of OLAP, also known as multidimensional data analysis, is to provide fast and flexible data summarization, analysis, and reporting capabilities with the ability to view trends over time


What is olap cont
What Is OLAP? (cont…)

  • OLAP applications, also called decision support systems (DSS), have the following features:

    • Enable users to look at different relationships in data by looking beyond traditional two-dimensional row and column data analysis

    • Offer high-performance access to large amounts of presummarized data

    • Give users the power to retrieve answers to multi-dimensional business questions quickly and easily

    • Provide slice-and-dice views of multiple relationships in large quantities of presummarized data


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



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


Some key points regarding data warehousing
Some Key Points Regarding Data Warehousing

  • Data warehousing has these characteristics:

    • Not new; existed for some time

    • An accepted practice

    • Fairly widespread, but not always well done

  • Many organizations now run data warehouse projects

  • Data warehousing is used in multiple industry sectors throughout the world

  • Within organizations, more and more data warehouses and data marts are used

  • Data warehousing is most successful when used as the foundation of an integrated information strategy


Sas rapid data warehouse methodology
SAS Rapid Data Warehouse Methodology

  • The SAS Rapid Data Warehouse Methodology facilitates the development of high quality data warehousing environments

  • The methodology is based onexperience that was gainedfrom hundreds of warehousingprojects


Why use a methodology
Why Use A Methodology?

  • For the first few data warehouse projects, a methodology accomplishes the following:

    • Gives practitioners a roadmap to follow for success

    • Enables practitioners to benefit from the experience (and mistakes!) of others

  • For experienced practitioners, a methodology provides the following:

    • A checklist of tasks, roles, deliverables, and so on

    • An explanation of tasks to others (customer management, users, project staff)


Sas rapid data warehouse methodology1
SAS Rapid Data Warehouse Methodology

  • The SAS Rapid Data Warehouse Methodology has seven phases

  • The phases provide logical work groupings and milestones to verify that the project has a solid foundation

Assessment

Requirements

Design

Review

Construction

Final Test

Deployment

Ongoing Maintenanceand Administration


Assessment phase

Assessment

Requirements

Design

Construction

Final Test

Deployment

Assessment Phase

Phase 1 – Assessment:Identify the organization’sreadiness for undertakinga data warehousing project.

Review

Ongoing Maintenanceand Administration


Requirements phase

Assessment

Requirements

Design

Construction

Final Test

Deployment

Requirements Phase

Phase 2 – Requirements:Gather the businessrequirements and definethe acceptance criteria.

Review

Ongoing Maintenanceand Administration


Design phase

Assessment

Requirements

Design

Review

Construction

Final Test

Deployment

Ongoing Maintenanceand Administration

Design Phase

Phase 3 – Design:Analyze and designthe warehouse systemarchitecture. Confirm the acceptance test criteria.


Construction phase

Assessment

Requirements

Design

Review

Construction

Final Test

Deployment

Ongoing Maintenanceand Administration

Construction Phase

Phase 4 – Construction:Construct and populate the warehouse. Code and test the exploitation applications and processes. Validate types of test. Perform unit and integration tests.


Final test phase

Assessment

Requirements

Design

Review

Construction

Final Test

Deployment

Ongoing Maintenanceand Administration

Final Test Phase

Phase 5 – Final Test:Test the warehouse andensure that it meets thespecifications of therequirements document.


Deployment phase

Assessment

Requirements

Design

Review

Construction

Final Test

Deployment

Ongoing Maintenanceand Administration

Deployment Phase

Phase 6 – Deployment:Roll out to the environmentand perform an acceptance test. Ensure knowledge transfer and user access throughout the organization.


Review phase

Assessment

Requirements

Design

Review

Construction

Final Test

Deployment

Ongoing Maintenanceand Administration

Review Phase

Phase 7 – Review:Review the project process, the deployment process, and the impact on the organization.


From tables spreadsheets to data cubes
From Tables & Spreadsheets to Data Cubes

  • A data warehouse is based on a multi-dimensional 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



Adding a 4 th dimension
Adding A 4th Dimension


N dimensional data cubes
N-Dimensional Data Cubes

  • We can continue to add dimensions indefinitley

  • 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
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

2-D cuboids

time,supplier

item,supplier

time,location,supplier

time,item,location

3-D cuboids

item,location,supplier

time,item,supplier

4-D(base) cuboid

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


Example star schema

time

item

time_key

day

day_of_the_week

month

quarter

year

item_key

item_name

brand

type

supplier_type

location

branch

location_key

street

city

state_or_province

country

branch_key

branch_name

branch_type

Example Star Schema

Sales Fact Table

time_key

item_key

branch_key

location_key

units_sold

dollars_sold

avg_sales

Measures


Example snowflake schema

time

item

time_key

day

day_of_the_week

month

quarter

year

item_key

item_name

brand

type

supplier_key

supplier

supplier_key

supplier_type

location

branch

location_key

street

city_key

branch_key

branch_name

branch_type

city

city_key

city

state_or_province

country

Example Snowflake Schema

Sales Fact Table

time_key

item_key

branch_key

location_key

units_sold

dollars_sold

avg_sales

Measures


Example fact constellation

time

item

time_key

day

day_of_the_week

month

quarter

year

item_key

item_name

brand

type

supplier_type

branch

location

branch_key

branch_name

branch_type

location_key

street

city

province_or_state

country

shipper

shipper_key

shipper_name

location_key

shipper_type

Example Fact Constellation

Shipping Fact Table

time_key

Sales Fact Table

item_key

time_key

shipper_key

item_key

from_location

branch_key

to_location

location_key

dollars_cost

units_sold

units_shipped

dollars_sold

avg_sales

Measures


Measures three categories
Measures: Three Categories

  • Distributive: If the result derived by applying the function to n aggregate values is the same as that derived by applying the function on all the data without partitioning

    • E.g., count(), sum(), min(), max().

  • Algebraic: If the result can be computed by an algebraic function with M arguments, 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 sub-aggregate.

    • E.g., median(), mode(), rank()

  • Measures are usually confined to numerics


  • A concept hierarchy dimension location
    A Concept Hierarchy: Dimension (Location)

    All

    All

    Region

    Europe

    ...

    North America

    Country

    Germany

    ...

    Spain

    Canada

    ...

    Mexico

    City

    Frankfurt

    ...

    Vancouver

    ...

    Toronto

    Office

    L. Chan

    ...

    M. Wind


    Multidimensional data
    Multidimensional Data

    • Sales volume as a function of product, month, and region

    Dimensions: Product, Location, Time

    Hierarchical summarization paths

    Region

    Industry Region Year

    Category Country Quarter

    Product City Month Week

    Office Day

    Product

    Month


    A sample data cube

    Date

    2Qtr

    1Qtr

    sum

    3Qtr

    4Qtr

    TV

    Product

    U.S.A

    PC

    VCR

    sum

    Canada

    Country

    Mexico

    sum

    All, All, All

    A Sample Data Cube

    Total annual sales

    of TV in U.S.A.


    Cuboids corresponding to the cube
    Cuboids Corresponding To The Cube

    all

    0-D (apex) cuboid

    country

    product

    date

    1-D cuboids

    product,date

    product,country

    date, country

    2-D cuboids

    3-D (base) cuboid

    product, date, country


    Browsing a data cube

    Visualization

    OLAP capabilities

    Interactive manipulation

    Browsing A Data Cube


    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


    Typical olap operations cont
    Typical OLAP Operations (cont…)

    • 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)


    Design of a data warehouse a business analysis framework
    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
    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


    Data warehouse design process cont
    Data Warehouse Design Process (cont…)

    • 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

    sources

    Extract

    Transform

    Load

    Refresh

    Operational

    DBs

    Multi-Tiered Architecture

    Monitor

    &

    Integrator

    OLAP Server

    Metadata

    Analysis

    Query

    Reports

    Data mining

    Serve

    Data

    Warehouse

    Data Marts

    Data Sources

    Data Storage

    OLAP Engine

    Front-End Tools


    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

        • 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


    Data warehouse development a recommended approach
    Data Warehouse Development: A Recommended Approach

    Multi-Tier Data Warehouse

    Distributed Data Marts

    Enterprise Data Warehouse

    Data Mart

    Data Mart

    Model Refinement

    Model Refinement

    Define a High-Level Corporate Data Model


    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 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


    Olap server architectures cont
    OLAP Server Architectures (cont…)

    • 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


    Summary
    Summary

    • Today we took an overview of data warehousing

    • We really have barely scratched the surface