CS 440 – Decision Support _ Intelligent Systems by lifemate

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									CS 440 – Decision Support &
    Intelligent Systems

          Fen Wang
       fen.wang@enc.edu
          Jan. 25, 2006
               Agenda
Introduction to the class
Syllabus overview
Introduction to DSS
Experience Survey (in class)
Information about Myself …
B.S. in Management Information Systems
B.A. in Science & Technology English
M.S. in Information Systems
A.B.D in Information Systems
Research areas:
Decision support systems, strategic e-
business management, web-based
services, e-supply chain management
    My contact information

Old colony campus, 2nd floor, Room 4
Phone: (617) 847-5807
E-mail: fen.wang@enc.edu
Office hour: Tuesdays 9:30-10:30am;
Wednesdays 9:30-10:30am (other time by
appointment)  Any problem??
     Introductory exercise
Name?
Background?
Major/training?
Answer a “lucky” question…
         Syllabus overview
Course website
http://moodle.enc.edu/course/view.php?id=8
Course description & objectives
Text book & development tools
Grading policy
Academic integrity
Tentative schedule
        Syllabus overview
Course website
– A brief walk-through (who wants to be the
  test user now?)…
– You need to login and enroll in the course on
  the moodle website ASAP
– The enrollment key is “440”
         Syllabus overview
Lecture notes
– All the lecture presentation slides should be
  available on the course website before the
  class, in each week/topic section
– Please check course website and your ENC
  email account regularly.
            Syllabus overview
Course description
The course aims to provide a broad review of decision
making concepts, technologies, and systems that are
developed to support the process. It covers the
fundamental concepts for decision making process,
decision models, a variety of decision support technologies
and applications, including expert systems, information
retrieval, data warehouse and data mining, group decision
support systems, as well as issues in system design and
integration in support of decision making.
Your are expected to actively participate in the discussion
and practice of materials covered in each class
         Syllabus overview
Course objectives: the primary objective of the
course is to overview DSS theories and related
technologies. Specific goals will be to study:
– Fundamental concepts of decision making process
– Basic components of a decision support system
– Technologies that have been used to support decision
  making
– Types of application systems designed to support
  decision making
– System design and implementation issues
         Syllabus overview
Required Textbook:
George M. Marakas. Decision Support
Systems in the 21st Century. Second edition.
Upper Saddle River, NJ ISBN 0-13-092206-4
Reference Textbook:
Decision Support Systems and Intelligent Systems,
7th Edition by Efraim Turban, Jay E. Aronson, and
Ting-peng Liang. Prentice-Hall, Inc., 2005.
Other readings/materials will be handed
out in class
             Syllabus overview
Grading policy
 Attendance (active learning exercises)     5 points    5%
 Assignments (asst 1 ~ 3)                   15 points   15%
 Team Project (including 2 presentations)   25 points   25%
 Mid-term Exam (3/3/06; in class)           25 points   25%

 Final Exam (TBA; in class)                 30 points   30%
 TOTAL                                      100 points 100%

 Extra Credit Point                         <=5 points <=5%
            Syllabus overview
Attendance Policy
– Attending class, participating in classroom discussions, and other
  similar activities are considered normal and expected
  contributions to the class. Attendance will be taken regularly in
  classes in the form of “active learning exercises”, which are
  designed to encourage class participation in classroom activities”
  and will correspond to 5% of the total grade.
– Please be advised that the maximum allowance of missed
  classes is 3. Absences above these 3 classes will result in the
  student’s name being reported to the Center for Academic
  Services. Excessive absenteeism may result in a student needing
  to meet with the academic dean in order to continue in the
  course. You are also expected to check your e-mail and the
  course website on a regular basis for updated course
  announcements and materials.
           Course Exams
Mid-term and Final exams
– Mid-term exam will be taken in the regular
  classroom during the class time and the final will
  be scheduled later by the Registrar's office
– The best way to study for the exams is to fulfill
  the assignments, understand the concepts and
  examples in class well, and make sure you gained
  skills to practically apply the class knowledge in
  practice
– Make-up exams are NOT given except under
  extreme circumstances, and only when the
  instructor gives permission IN ADVANCE (as for
  the final exam, you also need to get prior
  permission from the Committee on Admissions
  and Academic Standing)
          Course Project (1)
  You are required to complete a team project during
  the semester. Ideally, each team has two students.
  There will be two types of projects:

Type I: DSS overview paper.
  You are expected to conduct a relatively thorough
  research on a particular type of decision support
  systems (such as group DSS or web-based DSS). At
  the end of the semester, each team should submit a
  research paper on a selected topic, with about 15
  pages, with double line spacing and 12 font size. The
  paper should clearly describe the problems, the
  evolution of technologies in decision support
  systems, applications (e.g., case studies), and the
  challenges and possible solutions, etc.
  Examples of Type-I Project

– Data mining in support of enterprise decision
  making
– Web-based decision support systems
– Effectively managing knowledge within
  decision support systems
– Cross-cultural effect in group decision support
  systems
– Decision support systems for healthcare
            Course Project (2)
Type II: A Prototype of DSS
You are required to design and implement a prototype decision
support system. Your DSS can be developed in Microsoft Excel
using VB/VBA (Visual Basic for Applications) or other software and
programming language that you have prior knowledge about.
Your DSS will contain three primary components:
 – A model that will essentially replicate the decision model or could be
   used by the decision maker;
 – A data store that interacts will the model to assist decision making;
   and
 – An interface that should be easy for users to use and work with and
   helps to integrate the model and data store.
At the end of the semester, you are expected to demonstrate a
workable prototype system and write a brief technical report
about the system.
           Course Project (2)
Type II: A Prototype of DSS

All DSS require at lease one decision model. The models may be
mathematical models such as LP models and the Expected Value
models that will be discussed in class. The models may also be
based on qualitative judgment and can be coded as rules such as
"If GPA is greater than 3.0 and GRE is greater than 1200 then
admit the student into the graduate program".

For example, your DSS helps a user decide on the purchase of a
computer. It would evaluate the users performance requirements
based on usage, as well as cost limitations and provide a
recommended computer. This system would be implemented by a
supplier who wants to eliminate the need for personnel to be
directly involved in the process. This would produce cost and time
savings as well as consistent results.
Examples of Type-II Projects
 Whether to rent or buy property (house) based on
 the following criteria: (a) salary/income; (b) cash
 on hand; and (c) length of stay, expected.
 A loan company needs a DSS to help evaluate loan
 applications and make a decision based on
 specified loan applicant criteria.
 You need an investment DSS that help you make
 better investment decisions (Expected Value Model)
 Bill Gates has 1 billion dollars in raise money to
 distribute within his company based on employee
 performance. The system will allow the gathering
 of all the evaluations and will recommend how to
 distribute the budgeted money based on employee
 performance and a number of identified
 constraints.
Best project award!
  Creation of Project Teams
Create project groups by your own and select a
project topic ASAP
Each group has two members
Once a group is formed, each team member should
actively involve in the project and collaborate with
the other teammate
Inform the instructor of your group members (names
& emails) and presentation topic by 7:45am, Feb. 6
A two-page project proposal is due 7:45am, Mar. 1.
After that, each group is expected to share updates
of your project progress on a weekly basis.
      Team Presentations
Final project presentations on May. 3 during regular
class time. Every student should attend your groups’
presentation – 15 min + 5 min Q&A
The presentation will be a part of the project grade.
Your individual project grade also depends on the
team peer-evaluation
The final project paper/report is due 5:00pm, May 8.
Detailed project paper requirement will be handed out
next class.
Besides the final project presentation, each team will
present on a class topic (see the suggested class
presentation date) – 10 min + 5 min Q&A
         Project Deliverables

7:45am, Feb. 6: group info, tentative project topic,
& class presentation topic – via website/email
7:45am, Mar. 1: Project proposal collected in class
May. 3: Project presentation
5:00pm, May 8: Final project report

– Check the syllabus for more details
– I advise you to stay well ahead of each due date for the
  deliverables so that the final report and integration of project
  components runs smoothly.
Any move on before the
Let’s questions to start we
        move on?
       DSS thread…
  A big picture of the course content
                                                  DSS Context:
                                            Management & decision making


                                                          DSS Fundamentals:
                                                          Basic concepts of DSS


DSS Component:                                             DSS Technology:                                DSS Development:
Modeling & analysis                                      Tech/applications of DSS                         Design & development




                                                                                                                 Neural Network & IR
                               Artificial intelligence
          Executive IS & SCM




                                                                                            Data Mining & IV
                                                               Expert Systems




                                                                                Group DSS
The decision making process




       How a Decision Is Made?
The decision making process (cont.)


 Intelligence          Design             Choice   Implement




                         Computer aided
                             DSS

      Decision Maker
Conceptual DSS Architecture
                     Input feedback



   Data Base                           Organize problem               Status Report
   Decision Data;                      parameters

                                                                      Parameter and
                                       Structure decision             Outcome forecasts
   Knowledge Base                      problem
   Problem
   Knowledge                                                          Recommended
                                       Simulate policies              actions
                                       and events
   Model Base
   Decision Model                                                     Outcome
                                       Determine best                 Explanations and
   Solution Method
                                       problem solutions              advice




                                          Computer
                                         Technology/
                                          Interface




                                                            Output feedback
                                        Decision Maker




     INPUTS                           PROCESSING                      OUTPUTS
       Decision Making

Problem     Why not easy?
             Information
             Uncertainty
             Scarce Resources
             Psychological factors
Decision     (fear, power, anxiety)
                 Do’s
Begin project and assignments early.
Think deeply and practice more.
Seek help and references.
Follow advice.
Have high self esteem.
                Don’ts
Begin project deliverables near due dates
Begin assignments on weekends.
Miss lectures.
Feel dumb.
Ignore advice.
Panic during exams.
  To do list after first class
Ensure that you are on class mailing list
Browse the course website and Syllabus
Form project teams and select presentation
topics (project & class presentation)
Get the Textbook and Check useful web
resources (see the course website)
Read textbook Chapter 2 and try the
review questions

								
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