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DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall COS 236 Day 25.

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Presentation on theme: "DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall COS 236 Day 25."— Presentation transcript:

1 DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall COS 236 Day 25

2 DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall Agenda Assignment Average Posted –2 B’s, 4 C’s & 2 D Lowest Grade posted –1 C, 1 D, 2 F’s and 3 0’s Assignment 11Due Assignment 12 Posted –Due May 9 Quiz 3 is on May 9 –DP Chapters 9,10,11,12,13 & 15 –15 4 points each – 5 8 Points each Today we will discuss –Database Processing for Business Intelligence Systems

3 DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall End of days? (subject to change) May 3 –DP Chap 15 –Assignment 11 Due May 9 –Assignment 12 due –Quiz 3 –10 AM –Capstone presentations

4 DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall David M. Kroenke’s Chapter Fifteen: Database Processing for Business Intelligence Systems Part One Database Processing: Fundamentals, Design, and Implementation

5 DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall Business Intelligence (BI) Systems Business Intelligence (BI) systems are information systems that assist managers and other professionals: –To analyze current and past activities, and –To predict future events. Two broad categories: –Reporting –Data mining

6 DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall The Relationship of Operational and BI Applications

7 DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall Data for BI Systems BI systems obtain data in three ways: –From the operational database: Read and process data only. DO NOT insert, modify or delete operational data! –From extracts from the operational database: Data is in a BI DBMS. May be a different DBMS than the operations DBMS. –From data purchased from data vendors.

8 DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall Reporting Applications Reporting system applications: –Filter –Sort –Group –Make simple calculations –Classify entities (customers, products, employees, etc.) RFM Analysis –Can be performed using standard SQL –Extensions to SQL are sometime used OnLine Analytical Processing (OLAP). –Summarize current business status –Compare current business status to past or future –Deal with critical report delivery

9 DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall Data Mining Applications Data mining applications are used to: –Perform what-if analysis –Make predictions –Facilitate decision making Data mining applications use sophisticated statistical and mathematical techniques. Report delivery is not as critical.

10 DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall Characteristics of BI Applications

11 DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall Data Warehouses and Data Marts: Problems of Using Transaction Data for BI

12 DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall Data Warehouses and Data Marts: Components of a Data Warehouse

13 DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall Data Warehouses and Data Marts: Data Warehouse Compared to Data Marts

14 DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall Reporting Systems: RFM Analysis RFM Analysis analyzes and ranks customers according to purchasing patterns: –R = Recent (most recent order) –F = Frequent (how often an order is made) –M = Money (dollar amount of orders) Customers are sorted into five groups, each containing 20% of the customers. Each group is given a numerical value: –1 = Top 20% –2, 3, 4 = Each 20% in between top and bottom 20% –5 = Bottom 20%

15 DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall Reporting Systems: RFM Analysis (Continued) Ajax ordered recently (1), orders often (1) but does not order the most expensive items (3) – Try to sell Ajax more expensive goods! Bloominghams has not ordered recently (5), but has ordered often (1) and purchased the most expensive items (1). This customer may be looking for a different vendor – better call!

16 DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall Reporting Systems: Producing the RFM Analysis: Tables

17 DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall Reporting Systems: Producing the RFM Analysis: Stored Procedure Calculate_R [SQL Server]

18 DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall Reporting Systems: Producing the RFM Analysis: Stored Procedure RFM_Analysis [SQL Server]

19 DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall Reporting Systems: Producing the RFM Analysis: RFM Results [SQL Server]

20 DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall Reporting Systems: Components of a Reporting System

21 DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall Reporting Systems: Report Characteristics

22 DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall Reporting Systems: Report System Functions Report Authoring: –Connect to data sources. –Create the report structure. –Format the report. Report Management: –Defines who receives what reports when and by what means. Report Delivery: –Push reports or allow them to be pulled.

23 DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall Reporting Systems: OnLine Analytical Processing [OLAP] An OLAP report has measures and dimensions: –Measure — A data item of interest. –Dimension — A characteristic of a measure. OLAP cube — A presentation of a measure with associated dimensions. –An OLAP cube can have any number of axes. –The terms OLAP cube and OLAP report are synonymous. OLAP allows drill-down — a further division of the data into more detail.

24 DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall Reporting Systems: OLAP Drill Down: Product Family by Store Type

25 DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall Reporting Systems: OLAP Drill Down: Product Family and Store Location by Store Type

26 DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall Reporting Systems: OLAP Drill Down: Store Location and Product Family by Store Type

27 DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall Reporting Systems: OLAP Servers and OLAP Databases

28 DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall Data Mining Applications Data mining applications use sophisticated statistical and mathematical techniques to find patterns and relationships that can be used to classify and predict. –Unsupervised data mining — Statistical techniques are used to identify groups of entities with similar characteristics. Cluster Analysis –Supervised data mining: A model is developed. Statistical techniques are used to estimate parameter values of the model. –Regression analysis

29 DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall Data Mining Applications: The Convergence of the Disciplines

30 DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall Data Mining Applications: Three Popular Data Mining Techniques Decision tree analysis — Classifies entities into groups based on past history. Logistic regression — Produces equations that offer probabilities that certain events will occur. Neural Networks — Complex statistical prediction techniques

31 DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall Data Mining Applications: Market Basket Analysis Market Basket Analysis — Determines patterns of associated buying behavior. –Support — The probability that two items will be purchased together. –Confidence — The probability that an item will be purchased given the fact that the customer has already purchased another particular item. –Lift — the ratio of confidence to the basic probability that a particular item will be purchased.

32 DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall Data Mining Applications: Market Basket Analysis Example

33 DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall Data Mining Applications: SQL Statements for Market Basket Analysis CREATE VIEWTwoItemBasket AS SELECTT1.ItemID as FirstItem, T2.ItemID as SecondIem FROMTRANS_DATA T1 JOIN TRANS_DATA T2 ONT1.TransactionID = T2.TransactionID ANDT1.ItemID <> T2.ItemID; CREATE VIEWItemSupport AS SELECTFirstItem, SecondItem, Count(*) AS SupportCount FROMTwoItemBasket GROUP BYFirstItem, SecondItem;

34 DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall David M. Kroenke’s Database Processing Fundamentals, Design, and Implementation (10 th Edition) End of Presentation: Chapter Fifteen Part Two


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