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Transcript
13
Chapter 13
The Data Warehouse
1
13
The Need for Data Analysis
• Managers must be able to track daily
transactions to evaluate how the business is
performing
• By tapping into the operational database,
management can develop strategies to meet
organizational goals
• Data analysis can provide information about
short-term tactical evaluations and strategies
2
13
Solving Business Problems and Adding
Value with Data Warehouse-Based Solutions
3
13
Solving Business Problems and Adding Value
with Data Warehouse-Based Solutions (continued)
4
13
Decision Support Systems
• Methodology (or series of methodologies)
designed to extract information from data and to
use such information as a basis for decision
making
• Decision support system (DSS):
– Arrangement of computerized tools used to assist
managerial decision making within a business
– Usually requires extensive data “massaging” to
produce information
– Used at all levels within an organization
– Often tailored to focus on specific business areas
– Provides ad hoc query tools to retrieve data and
to display data in different formats
5
13
Decision Support Systems (continued)
• Composed of four main components:
– Data store component
• Basically a DSS database
– Data extraction and filtering component
• Used to extract and validate data taken from
operational database and external data sources
– End-user query tool
• Used to create queries that access database
– End-user presentation tool
• Used to organize and present data
6
13
Main Components of a
Decision Support System (DSS)
7
13
Transforming Operational Data Into
Decision Support Data
8
13
Contrasting Operational and DSS Data
Characteristics
9
13
The Data Warehouse
• Integrated, subject-oriented, time-variant,
nonvolatile database that provides support for
decision making
10
13
A Comparison of Data Warehouse and
Operational Database Characteristics
11
13
Creating a Data Warehouse
12
13
The ETL Process
•
•
•
•
Capture/Extract
Scrub or data cleansing
Transform
Load and Index
ETL = Extract, transform, and load
13
Capture/Extract…obtaining a snapshot of a chosen subset
of the source data for loading into the data warehouse
13
Figure 11-10:
Steps in data
reconciliation
Static extract = capturing
a snapshot of the source
data at a point in time
Incremental extract =
capturing changes that
have occurred since the last
static extract
14
Scrub/Cleanse…uses pattern recognition and AI
techniques to upgrade data quality
13
Figure 11-10:
Steps in data
reconciliation
(cont.)
Fixing errors: misspellings,
erroneous dates, incorrect field
usage, mismatched addresses,
missing data, duplicate data,
inconsistencies
Also: decoding, reformatting,
time stamping, conversion, key
generation, merging, error
detection/logging, locating
missing data
15
13
Cleanse:
• Process to identify erroneous data, not to fix
them
• Fixes are made at the source
Scrubbing:
A technique using pattern recognition and other
AI techniques to upgrade the quality of data
16
Transform = convert data from format of operational
system to format of data warehouse
13
Figure 11-10:
Steps in data
reconciliation
(cont.)
Record-level:
Selection–data partitioning
Joining–data combining
Aggregation–data summarization
Field-level:
single-field–from one field to one field
multi-field–from many fields to one, or
one field to many
17
Load/Index= place transformed data
into the warehouse and create indexes
13
Figure 11-10:
Steps in data
reconciliation
(cont.)
Refresh mode: bulk rewriting
of target data at periodic intervals
Update mode: only changes
in source data are written to data
warehouse
18
Figure 11-11: Single-field transformation
13
In general–some transformation
function translates data from old
form to new form
Algorithmic transformation uses
a formula or logical expression
Table lookup–another
approach, uses a separate
table keyed by source
record code
19
Figure 11-12: Multifield transformation
13
M:1–from many source
fields to one target field
1:M–from one
source field to
many target fields
20
Derived Data
13
• Objectives
–
–
–
–
–
Ease of use for decision support applications
Fast response to predefined user queries
Customized data for particular target audiences
Ad-hoc query support
Data mining capabilities
• Characteristics
– Detailed (mostly periodic) data
– Aggregate (for summary)
– Distributed (to departmental servers)
Most common data model = star schema
(also called “dimensional model”)
21
13
Star Schemas
• Data modeling technique used to map
multidimensional decision support data into a
relational database
• Creates the near equivalent of a
multidimensional database schema from the
existing relational database
• Yield an easily implemented model for
multidimensional data analysis, while still
preserving the relational structures on which
the operational database is built
• Has four components: facts, dimensions,
attributes, and attribute hierarchies
22
Figure 11-13 Components of a star schema
13
Fact tables contain factual
or quantitative data
1:N relationship between
dimension tables and fact tables
Dimension tables are denormalized to
maximize performance
Dimension tables contain descriptions
about the subjects of the business
Excellent for ad-hoc queries, but bad for online transaction processing
23
Figure 11-14 Star schema example
13
Fact table provides statistics for sales
broken down by product, period and
store dimensions
24
Figure 11-15 Star schema with sample data
13
25
13
Size of fact table
assume:
Total number of stores=1000
Total number of products=10,000
Total number of periods= 24 (two years)
Assume 50% of products record sales
Then total rows in fact table
1000stores* 5000 active products)*24 months
=120,000,000 rows
26
13
Size of “fact” table
Assume there are 6 fields each 4 bytes long,
then total size
120,00,000* 6 fields* 4bytes/field
=2,880,000,000 (2.88 gigabytes)
If instead of monthly, you record daily data
Multiply above by 30 (30 days/per month)
27
13
Online Analytical Processing
• Advanced data analysis environment that
supports decision making, business
modeling, and operations research
• OLAP systems share four main
characteristics:
– Use multidimensional data analysis
techniques
– Provide advanced database support
– Provide easy-to-use end-user interfaces
– Support client/server architecture
28
13
Operational vs. Multidimensional
View of Sales
29
13
Another example: Simple Star Schema
30
13
Possible Attributes for Sales Dimensions
31
13
Three-Dimensional View of Sales
32
13
Slice and Dice View of Sales
33
13
Location Attribute Hierarchy
34
13
Star Schema for Sales
35
13
Orders Star Schema
36
13
Normalized Dimension tables
37
13
Multiple Fact Tables
38
On-Line Analytical Processing (OLAP) Tools
13
• The use of a set of graphical tools that provides users
with multidimensional views of their data and allows
them to analyze the data using simple windowing
techniques
• Relational OLAP (ROLAP)
– Traditional relational representation
• Multidimensional OLAP (MOLAP)
– Cube structure
• OLAP Operations
– Cube slicing–come up with 2-D view of data
– Drill-down–going from summary to more detailed
views
39
Figure 11-23 Slicing a data cube
13
40
Figure 11-24
Example of drill-down
Starting with summary
data, users can obtain
details for particular
cells
Summary report
13
Drill-down with
color added
41
13
Implementing a Data Warehouse
• Numerous constraints:
– Available funding
– Management’s view of the role played by an
IS department and of the extent and depth of
the information requirements
– Corporate culture
• No single formula can describe perfect data
warehouse development
42
13
Factors Common to Data Warehousing
• Data warehouse is not a static database
• Dynamic framework for decision support that
is always a work in progress
• Data warehouse data cross departmental
lines and geographical boundaries
• Must satisfy:
– Data integration and loading criteria
– Data analysis capabilities with acceptable
query performance
– End-user data analysis needs
• Apply database design procedures
43
13
Data Mining
• Tools that:
– analyze data
– uncover problems or opportunities hidden in
data relationships,
– form computer models based on their findings,
and then
– use the models to predict business behavior
• Require minimal end-user intervention
44
13
Extraction of Knowledge From Data
45
13
Data-Mining Phases
46
13
A Sample of Current Data Warehousing
and Data-Mining Vendors
47
Data Mining and Visualization
•
•
13
Knowledge discovery using a blend of statistical, AI, and computer
graphics techniques
Goals:
– Explain observed events or conditions
– Confirm hypotheses
– Explore data for new or unexpected relationships
•
Techniques
–
–
–
–
–
–
–
–
–
•
Statistical regression
Decision tree induction
Clustering and signal processing
Affinity
Sequence association
Case-based reasoning
Rule discovery
Neural nets
Fractals
Data visualization–representing data in graphical/multimedia formats
for analysis
48
13
Summary
• Data analysis is used to derive and interpret
information from data
• Decision support is a methodology designed to
extract information from data and to use such
information as a basis for decision making
• Decision support system is an arrangement of
computerized tools used to assist managerial
decision making within a business
• Data warehouse is an integrated, subjectoriented, time-variant, nonvolatile database that
provides support for decision making
49
13
Summary (continued)
• Online analytical processing is an advanced
data analysis environment that supports
decision making, business modeling, and
operations research
• Star schema is a data-modeling technique used
to map multidimensional decision support data
into a relational database
• The implementation of any company-wide
information system is subject to conflicting
organizational and behavioral factors
50
13
Summary (continued)
• Data mining automates analysis of operational
data with the intention of finding previously
unknown data characteristics, relationships,
dependencies, and/or trends
• Data warehouse is storage location for decision
support data
51