![Page 1: Decision Support System BUS 782. Decision supports Systems Components Data management function –Decision Support Database Data warehouse Model management](https://reader035.vdocument.in/reader035/viewer/2022062409/56649f435503460f94c6458d/html5/thumbnails/1.jpg)
Decision Support System
BUS 782
![Page 2: Decision Support System BUS 782. Decision supports Systems Components Data management function –Decision Support Database Data warehouse Model management](https://reader035.vdocument.in/reader035/viewer/2022062409/56649f435503460f94c6458d/html5/thumbnails/2.jpg)
Decision supports Systems Components
• Data management function– Decision Support Database
• Data warehouse
• Model management function– Analytical models:
• Statistical model, management science model
• Data Mining
• User interface– Data visualization
![Page 3: Decision Support System BUS 782. Decision supports Systems Components Data management function –Decision Support Database Data warehouse Model management](https://reader035.vdocument.in/reader035/viewer/2022062409/56649f435503460f94c6458d/html5/thumbnails/3.jpg)
New Developments in Decision Support Systems
• Data visualization: Representing data in graphical/multimedia formats for analysis.
• Data Warehouse– Data Mart: A data warehouse that is limited in scope
• Data Mining
• Geological Information System, GIS
• What-if scenarios
![Page 4: Decision Support System BUS 782. Decision supports Systems Components Data management function –Decision Support Database Data warehouse Model management](https://reader035.vdocument.in/reader035/viewer/2022062409/56649f435503460f94c6458d/html5/thumbnails/4.jpg)
Data Warehouse
• Data warehouse is a repository of an organization's electronically stored data.
• A data warehouse houses a standardized, consistent, clean and integrated form of data that:– sourced from various operational systems in
use in the organization, – structured in a way to specifically address
the reporting and analytic requirements.
![Page 5: Decision Support System BUS 782. Decision supports Systems Components Data management function –Decision Support Database Data warehouse Model management](https://reader035.vdocument.in/reader035/viewer/2022062409/56649f435503460f94c6458d/html5/thumbnails/5.jpg)
Example:Transaction Database
Customer Order
Product
Has
Has
1 M
M
M
CID Cname City OID ODate
PIDPname
Price
RatingSalesPerson
Qty
![Page 6: Decision Support System BUS 782. Decision supports Systems Components Data management function –Decision Support Database Data warehouse Model management](https://reader035.vdocument.in/reader035/viewer/2022062409/56649f435503460f94c6458d/html5/thumbnails/6.jpg)
Analyze Sales DataDetailed Business Data
• Total sales:– by product:
• Qty*Price of each detail line• Sum (Qty*Price)• Detailed business data: qty*price
• Total quantity sold:– By product:
• Sum(Qty)• Detailed business data: Qty
![Page 7: Decision Support System BUS 782. Decision supports Systems Components Data management function –Decision Support Database Data warehouse Model management](https://reader035.vdocument.in/reader035/viewer/2022062409/56649f435503460f94c6458d/html5/thumbnails/7.jpg)
Dimensions for Data Analysis:Factors relevant to the business data
• Analyze sales by Product
• Analyze sales related to Customer:– Location: Sales by City– Customer type: Sales by Rating
• Analyze sales related to Time:– Quarterly, monthly, yearly Sales
• Analyze sales related to Employee:– Sales by SalesPerson
![Page 8: Decision Support System BUS 782. Decision supports Systems Components Data management function –Decision Support Database Data warehouse Model management](https://reader035.vdocument.in/reader035/viewer/2022062409/56649f435503460f94c6458d/html5/thumbnails/8.jpg)
Data Warehouse Design- Star Schema -
• Dimension tables– contain descriptions about the subjects of the
business such as customers, employees, locations, products, time periods, etc.
• Fact table– contain detailed business data with links to
dimension tables.
![Page 9: Decision Support System BUS 782. Decision supports Systems Components Data management function –Decision Support Database Data warehouse Model management](https://reader035.vdocument.in/reader035/viewer/2022062409/56649f435503460f94c6458d/html5/thumbnails/9.jpg)
Star Schema
FactTableLocationCodePeriodCode
RatingPIDQty
Amount
LocationDimension
LocationCodeStateCity
CustomerRatingDimension
RatingDescription
ProductDimension
PIDPname
Category
PeriodDimensionPeriodCode
YearQuarter
Can group by State, City
![Page 10: Decision Support System BUS 782. Decision supports Systems Components Data management function –Decision Support Database Data warehouse Model management](https://reader035.vdocument.in/reader035/viewer/2022062409/56649f435503460f94c6458d/html5/thumbnails/10.jpg)
Define Location Dimension
• Location:– In the transaction database: City– In the data warehouse we define Location to
be State, City• San Francisco -> California, San Francisco• Los Angeles -> California, Los Angeles
– Define Location Code: • California, San Francisco -> L1• California, Los Angeles -> L2
![Page 11: Decision Support System BUS 782. Decision supports Systems Components Data management function –Decision Support Database Data warehouse Model management](https://reader035.vdocument.in/reader035/viewer/2022062409/56649f435503460f94c6458d/html5/thumbnails/11.jpg)
Define Period Dimension
• Period:– In the transaction database: Odate– In the data warehouse we define Period to be:
Year, Quarter• Odate: 11/2/2003 -> 2003, 4• Odate: 2/28/2003 -> 2003, 1
– Define Period Code:• 2003, 4 -> 20034• 2003, 1 -> 20031
![Page 12: Decision Support System BUS 782. Decision supports Systems Components Data management function –Decision Support Database Data warehouse Model management](https://reader035.vdocument.in/reader035/viewer/2022062409/56649f435503460f94c6458d/html5/thumbnails/12.jpg)
The ETL Process
E
T
LOne, company-wide warehouse
Periodic extraction data is not completely current in warehouse
![Page 13: Decision Support System BUS 782. Decision supports Systems Components Data management function –Decision Support Database Data warehouse Model management](https://reader035.vdocument.in/reader035/viewer/2022062409/56649f435503460f94c6458d/html5/thumbnails/13.jpg)
The ETL Process
• Capture/Extract• Transform
– Scrub(data cleansing),derive– Example:
• City -> LocationCode, State, City• OrderDate -> PeriodCode, Year, Quarter
• Load and Index
ETL = Extract, transform, and load
![Page 14: Decision Support System BUS 782. Decision supports Systems Components Data management function –Decision Support Database Data warehouse Model management](https://reader035.vdocument.in/reader035/viewer/2022062409/56649f435503460f94c6458d/html5/thumbnails/14.jpg)
From SalesDB to MyDataWarehouse
• Extract data from SalesDB:– Create query to get the fact data
• FactData
– Download to MyDataWareHouse
• Transform:– Transform City to Location– Transform Odate to Period
• Query FactDataScrubing
• Load data to FactTable
![Page 15: Decision Support System BUS 782. Decision supports Systems Components Data management function –Decision Support Database Data warehouse Model management](https://reader035.vdocument.in/reader035/viewer/2022062409/56649f435503460f94c6458d/html5/thumbnails/15.jpg)
Performing Analysis
• Analyze sales:– by Location– By Location and Customer Type– By Location and Period– By Period and Product
• Pivot Table:– Drill down, roll up, reaggregation
![Page 16: Decision Support System BUS 782. Decision supports Systems Components Data management function –Decision Support Database Data warehouse Model management](https://reader035.vdocument.in/reader035/viewer/2022062409/56649f435503460f94c6458d/html5/thumbnails/16.jpg)
Star schema example
Fact table provides statistics for sales broken down by product, period and store dimensions
Dimension tables contain descriptions about the subjects of the business
![Page 17: Decision Support System BUS 782. Decision supports Systems Components Data management function –Decision Support Database Data warehouse Model management](https://reader035.vdocument.in/reader035/viewer/2022062409/56649f435503460f94c6458d/html5/thumbnails/17.jpg)
Star schema with sample data
![Page 18: Decision Support System BUS 782. Decision supports Systems Components Data management function –Decision Support Database Data warehouse Model management](https://reader035.vdocument.in/reader035/viewer/2022062409/56649f435503460f94c6458d/html5/thumbnails/18.jpg)
Snowflake Schema
FactTableLocationCodePeriodCode
RatingPIDQty
Amount
LocationDimension
LocationCodeStateCity
CustomerRatingDimension
RatingDescription
ProductDimension
PIDPname
CategoryID
ProductCategory
CategoryIDDescription
PeriodDimensionPeriodCode
YearQuarter
Can group by State, City
![Page 19: Decision Support System BUS 782. Decision supports Systems Components Data management function –Decision Support Database Data warehouse Model management](https://reader035.vdocument.in/reader035/viewer/2022062409/56649f435503460f94c6458d/html5/thumbnails/19.jpg)
Data Mining• Knowledge discovery using a blend of
statistical, Artificial Intelligence, and computer graphics techniques
• Goals:– Explain observed events or conditions– Explore data for new or unexpected relationships
![Page 20: Decision Support System BUS 782. Decision supports Systems Components Data management function –Decision Support Database Data warehouse Model management](https://reader035.vdocument.in/reader035/viewer/2022062409/56649f435503460f94c6458d/html5/thumbnails/20.jpg)
History in the Development of Data Mining.Evolutionary Step Business Question Enabling
TechnologiesCharacteristics
Data Collection(1960s)
"What was my total revenue in the last five years?"
Computers, tapes, disks
Retrospective, static data delivery
Data Access(1980s)
"What were unit sales in New England last March?"
Relational databases (RDBMS), Structured Query Language (SQL), ODBC
Retrospective, dynamic data delivery at record level
Data Warehousing &Decision Support(1990s)
"What were unit sales in New England last March? Drill down to Boston."
On-line analytic processing (OLAP), multidimensional databases, data warehouses
Retrospective, dynamic data delivery at multiple levels
Data Mining(Emerging Today)
"What’s likely to happen to Boston unit sales next month? Why?"
Advanced algorithms, multiprocessor computers, massive databases
Prospective, proactive information delivery
![Page 21: Decision Support System BUS 782. Decision supports Systems Components Data management function –Decision Support Database Data warehouse Model management](https://reader035.vdocument.in/reader035/viewer/2022062409/56649f435503460f94c6458d/html5/thumbnails/21.jpg)
Typical Data Mining Techniques
• Statistical regression• Decision tree induction• Clustering – discover subgroups• Affinity – discover things with strong mutual
relationships• Sequence association – discover cycles of
evens and behaviors• Rule discovery – search for patterns and
correlations
![Page 22: Decision Support System BUS 782. Decision supports Systems Components Data management function –Decision Support Database Data warehouse Model management](https://reader035.vdocument.in/reader035/viewer/2022062409/56649f435503460f94c6458d/html5/thumbnails/22.jpg)
Typical Data Mining Applications
• Profiling populations– High-value customers, credit risks, credit card fraud
• Analysis of business trends• Target marketing• Campaign effectiveness• Product affinity
– Identifying products that are purchased concurrently• Up-selling
– Identifying new products and services to sell to a customer based on critical events
![Page 23: Decision Support System BUS 782. Decision supports Systems Components Data management function –Decision Support Database Data warehouse Model management](https://reader035.vdocument.in/reader035/viewer/2022062409/56649f435503460f94c6458d/html5/thumbnails/23.jpg)
Affinity Analysis:Market Basket Analysis
• Market Basket Analysis is a modeling technique based upon the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items.
• The set of items a customer buys is referred to as an itemset, and market basket analysis seeks to find relationships between purchases.
• Typically the relationship will be in the form of a rule: Example:– IF {beer, no bar meal} THEN {chips}.
![Page 24: Decision Support System BUS 782. Decision supports Systems Components Data management function –Decision Support Database Data warehouse Model management](https://reader035.vdocument.in/reader035/viewer/2022062409/56649f435503460f94c6458d/html5/thumbnails/24.jpg)
Basket Analysis and Cross- Selling
• For instance, customers are very likely to purchase shampoo and conditioner together, so a retailer would not put both items on promotion at the same time. The promotion of one would likely drive sales of the other.
• A widely used example of cross selling on the internet with market basket analysis is Amazon.com's use of suggestions of the type:– "Customers who bought book A also bought book B",
e.g.
![Page 25: Decision Support System BUS 782. Decision supports Systems Components Data management function –Decision Support Database Data warehouse Model management](https://reader035.vdocument.in/reader035/viewer/2022062409/56649f435503460f94c6458d/html5/thumbnails/25.jpg)
Geological Information SystemGIS
• GIS is a computer-based tool for mapping and analyzing things that exist and events that happen on earth. GIS technology integrates common database operations such as query and statistical analysis with the unique visualization and geographic analysis benefits offered by maps.
![Page 26: Decision Support System BUS 782. Decision supports Systems Components Data management function –Decision Support Database Data warehouse Model management](https://reader035.vdocument.in/reader035/viewer/2022062409/56649f435503460f94c6458d/html5/thumbnails/26.jpg)
Data of GIS • Geodatabase:
– A geodatabase is a database that is in some way referenced to locations on the earth.
• Longitude, latitude
• Attribute data: – Attribute data generally defined as additional
information, which can then be tied to spatial data.
• Example: – Google Earth– GeoCode service:
http://developer.yahoo.com/maps/rest/V1/geocode.html
![Page 27: Decision Support System BUS 782. Decision supports Systems Components Data management function –Decision Support Database Data warehouse Model management](https://reader035.vdocument.in/reader035/viewer/2022062409/56649f435503460f94c6458d/html5/thumbnails/27.jpg)
Scenario
• A scenario is an assumption about input variables.• Excel’s Scenarios is a what-if-analysis tool. A
scenario is a set of values that Microsoft Excel saves and can substitute automatically in your worksheet.
• You can use scenarios to forecast the outcome of a worksheet model. You can create and save different groups of values on a worksheet and then switch to any of these new scenarios to view different results.
• Data/What If analysis/Scenario
![Page 28: Decision Support System BUS 782. Decision supports Systems Components Data management function –Decision Support Database Data warehouse Model management](https://reader035.vdocument.in/reader035/viewer/2022062409/56649f435503460f94c6458d/html5/thumbnails/28.jpg)
Creating a Scenario
– Add scenario• Changing cells• Resulting cells
• Demo: benefit.xls