Download Week 11

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The Data Warehouse
and Data Mining
MIS 304 Winter 2006
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Class Goals
• Be able to understand and apply the concept of
a Data Warehouse to database environments.
• Be able to describe the concept of On Line
Analytical Processing (OLAP) and show how it
might be used.
• Understand the seminaries and differences
between DDS, OLAP, and Data Mining
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Class Goals
• Develop and understanding of the Star Schema
and its importance in the design of the data
warehouse.
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How much data is there?
• Remember the reading from the first
night’s class.
• We are swimming in data.
• What can we do with it?
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The Need for Data Analysis
• Constant pressure from external and internal forces
requires prompt tactical and strategic decisions.
• The decision-making cycle time is reduced, while
problems are increasingly complex with a growing
number of internal and external variables.
• Managers need support systems for facilitating quick
decision making in a complex environment.
• One solution many people use is a Decision support
systems (DSS).
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Data Warehouse Examples
• Center for Disease Control
http://www.cdc.gov/nchs/datawh.htm
• United States Geological Survey
http://orxddwimdn.er.usgs.gov
• Pharmacia – Upjohn
• AT&T
• Meijer
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DSS
• Came about in the 1980’s based on work done
at AT&T, IBM, Boston Consulting Group and
others.
• Bonczek, Holsapple and Winston 1981,
Foundations of Decision Support Systems
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Decision Support Systems
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• Decision Support is a methodology (or a series of
methodologies) designed to extract information from data
and to use such information as a basis for decision making.
• A decision support system (DSS) is an arrangement of
computerized tools used to assist managerial decision
making within a business.
– A DSS usually requires extensive data “massaging” to produce
information.
– The DSS is used at all levels within an organization and is often
tailored to focus on specific business areas or problems.
– The DSS is interactive and provides ad hoc query tools to retrieve
data and to display data in different formats.
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Four Components of a DSS
• The data store component is basically a DSS database.
• The data extraction and filtering component is used to extract and
validate the data taken from the operational database and the
external data sources.
• The end user query tool is used by the data analyst to create the
queries that access the database.
• The end user presentation tool is used by the data analyst to organize
and present the data.
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Main Components Of A Decision Support System (DSS)
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Operational Data vs. Decision Support Data
• Most operational data are stored in a relational database in
which the structures tend to be highly normalized.
• The operational data storage is optimized to support
transactions that represent daily operations.
• Whereas operational data capture daily business
transactions, DSS data give tactical and strategic business
meaning to the operational data.
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Operational Data vs. Decision Support Data
• The design needs of the DSS and the
Supporting data are often at odds with one
another!
• You have two choices:
– Modify the structure of your operational data
– Build a separate system.
What is the deciding factor?
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Operational Data vs. Decision Support
Data
•
Time span
– Operational data represent current (atomic) transactions.
– DSS data tend to cover a longer time frame.
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Granularity
– Operational data represent specific transactions that occur at a
given time.
– DSS data must be presented at different levels of aggregation.
•
Dimensionality
– Operational data focus on representing atomic transactions.
– DSS data can be analyzed from multiple dimensions.
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Contrasting Operational And DSS Data Characteristics
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Decision Support Systems
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The DSS Database Requirements
– Database Schema
• The DSS database schema must support complex (nonnormalized) data representations.
• The queries must be able to extract multidimensional time
slices.
What is the Impact on the Database?
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Impacts on the Database
• Less normalization typically means
more speed.
• Less normalization typically means
bigger tables
• Less normalization typically means
“easier” queries (SQL)
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Data Extraction and Loading
• The DBMS must support advanced data extracting
and filtering tools.
• The data extraction capabilities should support
different data sources and multiple vendors.
• Data filtering capabilities must include the ability to
check for inconsistent data or data validation rules.
• The DBMS must support advanced data integration,
aggregation, and classification capabilities.
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Decision Support Systems
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• End-User Analytical Interface
– The DSS DBMS must support advanced data modeling and
data presentation tools, data analysis tools, and query
generation and optimization components.
– The end user analytical interface is one of the most critical
components.
• Database Size Requirements
– DSS databases tend to be very large.
– The DBMS must be capable of supporting very large
databases (VLDB).
– The DBMS may be required to use advanced hardware, such
as multiple disk arrays and multiple-processor technologies.
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The Data Warehouse
• The Data Warehouse is an integrated, subject-oriented, timevariant, non-volatile database that provides support for
decision making.
– Integrated
• The Data Warehouse is a centralized, consolidated
database that integrates data retrieved from the entire
organization.
– Subject-Oriented
• The Data Warehouse data is arranged and optimized to
provide answers to questions coming from diverse
functional areas within a company.
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The Data Warehouse
– Time Variant
• The Warehouse data represent the flow of data
through time. It can even contain projected
data.
– Non-Volatile
• Once data enter the Data Warehouse, they are
never removed.
• The Data Warehouse is always growing.
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Comparison Of Data Warehouse And Operational
Database Characteristics
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Creating A Data Warehouse
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The Other Option
• Find places where this filtering occurs
naturally
– Accounting systems
– Distribution systems
– Engineering systems
• Extract the data from the “filtering” system
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Data Mart
• A data mart is a small, single-subject data
warehouse subset that provides decision support to
a small group of people.
• Data Marts can serve as a test vehicle for companies
exploring the potential benefits of Data Warehouses.
• Data Marts address local or departmental problems,
while a Data Warehouse involves a company-wide
effort to support decision making at all levels in the
organization.
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Twelve Rules That Define a
Data Warehouse
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1. The Data Warehouse and operational environments are
separated.
2. The Data Warehouse data are integrated.
3. The Data Warehouse contains historical data over a long
time horizon.
4. The Data Warehouse data are snapshot data captured at a
given point in time.
5. The Data Warehouse data are subject-oriented.
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12 Rules cont.
6. The Data Warehouse data are mainly read-only with
periodic batch updates from operational data. No
online updates are allowed.
7. The Data Warehouse development life cycle differs
from classical systems development. The Data
Warehouse development is data driven; the classical
approach is process driven.
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12 Rules cont.
8. The Data Warehouse contains data with several levels of
detail; current detail data, old detail data, lightly summarized,
and highly summarized data.
9. The Data Warehouse environment is characterized by readonly transactions to very large data sets. The operational
environment is characterized by numerous update
transactions to a few data entities at the time.
10. The Data Warehouse environment has a system that traces
data resources, transformation, and storage.
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12 Rules cont.
11. The Data Warehouse’s metadata are a critical
component of this environment. The metadata identify
and define all data elements. The metadata provide
the source, transformation, integration, storage, usage,
relationships, and history of each data element.
12. The Data Warehouse contains a charge-back
mechanism for resource usage that enforces optimal
use of the data by end users.
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10 Mistakes to Avoid
• Mistake 1:Not gathering business requirements
• Mistake 2: Saving time by not creating a subject
area model
• Mistake 3: Delivering normalized tables to drive
out mart design
• Mistake 4: Designing the data staging process for
the ease of the developers at the expense of the
end users
• Mistake 5: Denormalizing without starting with a
fully normalized data model
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10 Mistakes to Avoid
• Mistake 6: Allowing users to drive the level of detail
• Mistake 7: Not having a multi-tiered warehousing
environment
• Mistake 8: Developing a data model from a list of required
data elements
• Mistake 9: Thinking that you must choose between
relational and dimensional models
• Mistake 10: Jumping straight into data mart design
Karolyn Duncan, Steve Hoberman, and Laura Reeves, 10
Mistakes to Avoid When Modeling Data Warehouses
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OnLine Analytical Processing
OLAP
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On-Line Analytical Processing
• On-Line Analytical Processing (OLAP) is an advanced
data analysis environment that supports decision
making, business modeling, and operations research
activities.
• Four Main Characteristics of OLAP
– Use multidimensional data analysis techniques
– Provide advanced database support
– Provide easy-to-use end user interfaces
– Support client/server architecture
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Multidimensional Data Analysis
Techniques
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• The processing of data in which data are viewed as
part of a multidimensional structure.
• Multidimensional view allows end users to
consolidate or aggregate data at different levels.
• Multidimensional view allows a business analyst to
easily switch business perspectives.
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Operational Vs. Multidimensional View Of Sales
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On-Line Analytical Processing
• Additional Functions of Multidimensional Data
Analysis Techniques
– Advanced data presentation functions
– Advanced data aggregation, consolidation, and
classification functions
– Advanced computational functions
– Advanced data modeling functions
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Integration Of OLAP With
A Spreadsheet Program
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Advanced Database Support
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• Access to many different kinds of DBMSs, flat files, and internal
and external data sources.
• Access to aggregated Data Warehouse data as well as to the
detail data found in operational databases.
• Advanced data navigation features such as drill-down and rollup.
• Rapid and consistent query response times.
• The ability to map end user requests, expressed in either
business or model terms, to the appropriate data source and
then to the proper data access language.
• Support for very large databases.
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On-Line Analytical Processing
• Easy-to-Use End User Interface
– Easy-to-use graphical user interfaces make
sophisticated data extraction and analysis tools easily
accepted and readily used.
• Client/Server Architecture
– The client/server environment enables us to divide an
OLAP system into several components that define its
architecture.
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On-Line Analytical Processing
• OLAP Architecture
– Three Main Modules
• OLAP Graphical User Interface (GUI)
• OLAP Analytical Processing Logic
• OLAP Data Processing Logic
– OLAP systems are designed to use both operational
and Data Warehouse data.
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OLAP Server Arrangement
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OLAP Server With Multidimensional
Data Store Arrangement
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OLAP Server With Local Mini Data-Marts
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Relational OLAP
• Relational On-Line Analytical Processing (ROLAP) provides
OLAP functionality by using relational database and familiar
relational query tools.
• Extensions to RDBMS
– Multidimensional data schema support within the RDBMS
– Data access language and query performance optimized for
multidimensional data
– Support for very large databases
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Multidimensional Data Schema Support
• Normalization of tables in relational technology is seen as a
stumbling block to its use in OLAP systems.
• DSS data tend to be non-normalized, duplicated, and preaggregated.
• ROLAP uses a special design technique to enable RDBMS
technology to support multidimensional data representations,
known as star schema.
• Star schema creates the near equivalent of a multidimensional
database schema from the existing relational database.
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On-Line Analytical Processing
• Data Access Language and Query Performance Optimized for
Multidimensional Data
– Most decision support data requests require the use of
multiple-pass SQL queries or multiple nested SQL
statements.
– ROLAP extends SQL so that it can differentiate between
access requirements for data warehouse data and
operational data.
• Support for Very Large Databases
– Decision support data are normally loaded in bulk (batch)
mode from the operational data.
– RDBMS must have the proper tools to import, integrate, and
populate the data warehouse with operational data.
– The speed of the data-loading operations is important.
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A Typical ROLAP Client/Server Architecture
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Multidimensional OLAP
(MOLAP)
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MOLAP extends OLAP functionality to multidimensional databases (MDBMS).
MDBMS end users visualize the stored data as a multidimensional cube known as a
data cube.
Data cubes are created by extracting data from the operational databases or from
the data warehouse.
Data cubes are static and require front-end design work.
To speed data access, data cubes are normally held in memory, called cube cache.
MOLAP is generally faster than their ROLAP counterparts. It is also more resourceintensive.
MDBMS is best suited for small and medium data sets.
Multidimensional data analysis is also affected by how the database system
handles sparsity.
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MOLAP Client/Server Architecture
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Relational Vs. Multidimensional OLAP
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Star Schema
• The star schema is a data-modeling technique used
to map multidimensional decision support into a
relational database.
• Star schemas yield an easily implemented model for
multidimensional data analysis while still preserving
the relational structure of the operational database.
• Four Components:
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Facts
Dimensions
Attributes
Attribute hierarchies
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A Simple Star Schema
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• Facts
Star Schema
– Facts are numeric measurements (values) that represent a specific
business aspect or activity.
– The fact table contains facts that are linked through their
dimensions.
– Facts can be computed or derived at run-time (metrics).
• Dimensions
– Dimensions are qualifying characteristics that provide additional
perspectives to a given fact.
– Dimensions are stored in dimension tables.
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Possible Attributes For Sales Dimensions
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• Attributes
– Each dimension table contains attributes. Attributes
are often used to search, filter, or classify facts.
– Dimensions provide descriptive characteristics about
the facts through their attributes.
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Three Dimensional View Of Sales
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Slice And Dice View Of Sales
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Attribute Hierarchies
• Attributes within dimensions can be ordered in a
well-defined attribute hierarchy.
• The attribute hierarchy provides a top-down data
organization that is used for two main purposes:
– Aggregation
– Drill-down/roll-up data analysis
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A Location Attribute Hierarchy
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Attribute Hierarchies In
Multidimensional Analysis
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Star Schema Representation
• Facts and dimensions are normally represented by physical
tables in the data warehouse database.
• The fact table is related to each dimension table in a manyto-one (M:1) relationship.
• Fact and dimension tables are related by foreign keys and
are subject to the primary/foreign key constraints.
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Star Schema For Sales
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Orders Star Schema
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Example
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Star Schema
• Performance-Improving Techniques
– Normalization of dimensional tables
– Multiple fact tables representing different
aggregation levels
– Denormalization of fact tables
– Table partitioning and replication
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Normalized Dimension Tables
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Multiple
Fact
Tables
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Data Warehouse Implementation
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• The Data Warehouse as an Active Decision Support
Network
– A Data Warehouse is a dynamic support framework.
– Implementation of a Data Warehouse is part of a complete
database-system-development infrastructure for companywide decision support.
– Its design and implementation must be examined in the light
of the entire infrastructure.
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Data Warehouse Implementation
• A Company-Wide Effort that Requires User
Involvement and Commitment at All Levels
– For a successful design and implementation, the designer
must:
• Involve end users in the process.
• Secure end users’ commitment from the beginning.
• Create continuous end user feedback.
• Manage end user expectations.
• Establish procedures for conflict resolution.
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Data Warehouse Implementation
• Satisfy the Trilogy: Data, Analysis, and Users
– For a successful design and implementation, the
designer must satisfy:
• Data integration and loading criteria.
• Data analysis capabilities with acceptable query
performance.
• End user data analysis needs.
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Apply Database Design Procedures
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– Data Warehouse development is a company-wide
effort and requires many resources: people, financial,
and technical.
• The sheer and often mind-boggling quantity of decision
support data is likely to require the latest hardware and
software.
• It is also imperative to have very detailed procedures to
orchestrate the flow of data from the operational
databases to the Dare Warehouse.
• To implement and support the Data Warehouse
architecture, we also need people with advanced
database design, software integration, and management
skills.
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Data Warehouse Implementation Road Map
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Data Mining
• In contrast to the traditional (reactive) DSS tools, the
data mining premise is proactive.
• Data mining tools automatically search the data for
anomalies and possible relationships, thereby
identifying problems that have not yet been identified
by the end user.
• Data mining tools -- based on algorithms that form the
building blocks for artificial intelligence, neural
networks, inductive rules, and predicate logic -- initiate
analysis to create knowledge.
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Data Mining Techniques
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Neural Nets
Regression Analysis
Time Series Analysis
Multidimensional Analysis
Factor Analysis
Nearest Neighbor methods
Cluster Analysis
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Extraction Of Knowledge From Data
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Data Mining
• Four Phases of Data Mining
1. Data Preparation
• Identify and cleanse data sets.
• Data Warehouse is usually used for data mining operations.
2. Data Analysis and Classification
• Identify common data characteristics or patterns using
– Data groupings, classifications, clusters, or sequences.
– Data dependencies, links, or relationships.
– Data patterns, trends, and deviations.
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Data Mining
3. Knowledge Acquisition
• Select the appropriate modeling or knowledge acquisition
algorithms.
• Examples: neural networks, decision trees, rules induction,
genetic algorithms, classification and regression tree, memorybased reasoning, or nearest neighbor and data visualization.
4. Prognosis
• Predict future behavior and forecast business outcomes using the
data mining findings.
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Data-Mining Phases
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Examples
• Credit Card usage (who buys what)
• Airline travel (where do people go, when)
• Social Services (who is not paying the tab)
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