ACCT 6910, Spring 2003

Building Business Intelligence Systems


Meeting schedule and room              Tuesday Thursday 9:10 – 10:30 am, BUC 108


Instructor                                           Professor Olivia R. Liu Sheng

                                                            Office: KDGB 404, Phone: 801-585-9071, Email: actos@business.utah.edu


TA                                                     Wei Gao, Office: BUC455a, Email: weig@bpa.arizona.edu


Office hours                                      (KDGB404) Tuesday Thursday 10:30 am to noon or by appointment


Listserv/Homepage                          ebis_class-1@lists.business.utah.edu

(tentative)                                             http://e.bis.business.utah.edu/ebis_class/2003s/


Course Description

Today’s businesses must face the challenges of rapidly creating and sustaining enterprise and business intelligence systems (e.bis) in order to operate in e-business contexts.  E.bis are web-enabled, integrated information systems that have become central to e-business by processing e-business transactions and adding business intelligence values. This course will focus on the issues and building of business intelligence systems using data warehouse, Online Analytic Processing (OLAP) and data mining technologies. 



Mastery of basic ER modeling techniques, Relational Database concepts and SQL statements.


Required Texts:

(T1)  Ralph Kimball and Margy Ross. The Data Warehouse Toolkit, 2nd Edition. John Wiley & Sons, 2002

(T2) Jiawei Han & Micheline Kamber. Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, 2001



  1. Database references of your choice for reviewing Entity-Relationship (ER) modeling techniques, Relational Database Model concepts and SQL statements.
  2. There are many references and texts about data mining and data warehousing. All have advantages and disadvantages. You are welcome but are not required to seek your own references for alternative explanations, additional examples and broader or more advanced concepts.
  3. Data Mart Suite and Intelligent Miner references and other useful web resources will be provided via the class website.
  4. Supplemental articles will be provided via UU’s digital library.



Grading and Course Requirements (tentative; curving will be applied when needed):

                                                            Grade %                      Course Grade               Course Score

Midterm exam                                      25%                                         A                     91 - 100

Final exam                                            25%                                         B                      81 - 90

Assignments                                         20%                                         C                     71 - 80

Class Project                                        20%                                         D                     61 – 70

Class Contributions                               10%                                         F                      60 or below


Class Contributions: Include but are not limited to class participations, seeking interesting projects and cases, sharing experiences, and submitting useful questions or information using the listserv.


Labs on Data Warehouse and Data Mining Tools:


During our regular class meetings, students will attend lab sessions to complete hand-on exercises towards completing project milestones utilizing Oracle Data Mart Suite and IBM Intelligent Miner for developing business intelligence systems. Lab participation and performance is counted in class contributions.


Assignments (Problem Analyses, Solution Design and Interpretation):


Students will work on the following four assignments primarily to review and apply concepts and techniques learned.  Assignments 2-4 prepare students for examinations that cover the same material.


1.      (10%) Listserv sign up and account registration (1/16 – 23)

2.      (30%) Logical data warehouse design  (2/11 – 2/25)

3.      (30%) Association rule mining (3/27 – 4/8)

4.      (30%) Classification and clustering (4/8 – 4/17)



Closed books and notes unless otherwise specified in class. Bring your own exam books.


Class Project:

Every student needs to finish five project milestones to build a business intelligence system for an assigned project case during the semester.


(25%) Milestone 1:       DW Logical Design (1/30 – 2/11)

(10%) Milestone 2:       DW Logical Design Using Oracle Data Mart Designer (2/11 – 2/20)

(25%) Milestone 3:     DW Physical Design and Data Staging using Oracle Data Mart

Builder (2/20 – 3/11)

(15%) Milestone 4:       Data Analysis using Oracle Data Mart Discoverer (3/11 – 3/25)

(25%) Milestone 5:       Data Mining Case Study using IBM Intelligent Miner (4/1 – 4/17)


Class Schedule (Dates and Topics are subject to changes that will be announced in class or on the listserv when necessary):






Data Warehouse and Mining & Course Overview



Introduction to Data Warehouse Technology and Lifecycle

T1: Ch.16


Logical Data Warehouse Design: overview of ER, Relational and Dimensional Models


DB reference

T1: Ch.1-2


Logical Data Warehouse Design I-VI: Dimensional Modeling


T1: Ch. 3-15


Lab Session I: Data Mart Designer



Physical Data Warehouse Design: Aggregates and Indexes

T1: Ch.16


Data Staging

T1. Ch.16


Lab Session II – IV: Data Mart Builder



Midterm Exam Review



Midterm Exam



Lab Session V: OLAP Queries and Discoverer

T1: Ch.16


Data Mining Primitives and Concepts

T2: Ch.2-5


Spring Break



Association Rule and Sequential Pattern Mining

T2: Ch.6


Lab Session VI: Intelligent Miner – Association Rule and Sequential Pattern Mining






Lab Session VII: Intelligent Miner - Clustering



Classification I - II

T2: Ch.7


Lab Session VIII: Intelligent Miner - Classification



Business Intelligence in the Real World

Guest lecture


Final Exam review


5/1 8-10AM

Final exam