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DSS Database Requirements
• The Database Schema
– Support complex (non-normalized) data
representations
• Aggregated and summarized data
• Queries to extract multidimensional time slices
• Data extraction and loading
• The end-user analytical interface
• Database size
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normalized
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If 20 stores
nationwide
and 10
departments
per store?
If we want
monthly
sales
summaries?
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Data Extraction and Filtering
• Data extraction capabilities
– Allow batch and scheduled data extraction, to minimize
the impact on the operational DB
– Support different data sources
• Solve data-formatting conflicts
• Data filtering capabilities
– Include the ability to check for inconsistent data or data
validation rules
• Advanced data integration, aggregation, and
classification
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End-User Analytical Interface
• Support advanced data modeling and data
presentation tools to define the nature and extent of
business problems
• Generate the queries to retrieve the appropriate data
from the DSS database
• Optimize the queries
– Bitmap index and data partitioning
– Support non-normalized and complex structures
• Evaluate the query results with data analysis tools
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Database Size
• Support very large database
– Use advanced hardware, such as multiple disk
arrays
– Support multiple-processor technologies, such
as symmetric multiprocessors (SMP) or
massively parallel processors (MPP)
• Creation of a new type of data repository:
data warehouse
– Contains data in formats that facilitate data
extraction, data analysis, and decision making
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12.3 The Data Warehouse
• Integrated, subject-oriented, time-variant,
nonvolatile database that provides support for
decision making
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University Lab
usage
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Data Mart
• A small, single-subject data warehouse
subset that provides decision support to a
small group of people, such as within a
department
• Some choose to implement data mart not
only because of the lower cost and shorter
implementation time, but also because of
“people issues”
•
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DSS Architectural Styles (Continued)
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Twelve Rules that Define a Data Warehouse
1. The data warehouse and operational environment 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 are subject oriented
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 is data-driven; the
classical system development is process-driven
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Twelve Rules that Define a Data Warehouse
8. The data warehouse contains data with several levels of
detail: current detail, old detail, lightly summarized,
highly summarized
9. The data warehouse environment is characterized by
read-only transactions to very large data sets
10. The data warehouse environment has a system that
traces data sources, transformations, and storage
11. The data warehouse’s metadata identify and define all
data elements. They provide the source, transformation,
integration, storage, usage, relationships, and history of
each data element
12. The data warehouse contains a chargeback mechanism
for resource usage that enforces optimal use of the data
by end users
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