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CS 440 Decision Support

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Experience Survey (in class) Information about Myself ... Intelligent Systems, 7th Edition by Efraim Turban, Jay E. Aronson, and Ting-peng ... – PowerPoint PPT presentation

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Title: CS 440 Decision Support


1
CS 440 Decision Support Intelligent Systems
  • Fen Wang
  • fen.wang_at_enc.edu
  • Jan. 25, 2006

2
Agenda
  • Introduction to the class
  • Syllabus overview
  • Introduction to DSS
  • Experience Survey (in class)

3
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

4
My contact information
  • Old colony campus, 2nd floor, Room 4
  • Phone (617) 847-5807
  • E-mail fen.wang_at_enc.edu
  • Office hour Tuesdays 930-1030am Wednesdays
    930-1030am (other time by appointment) ? Any
    problem??

5
Introductory exercise
  • Name?
  • Background?
  • Major/training?
  • Answer a lucky question

6
Syllabus overview
  • Course website
  • http//moodle.enc.edu/course/view.php?id8
  • Course description objectives
  • Text book development tools
  • Grading policy
  • Academic integrity
  • Tentative schedule

7
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

8
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.

9
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

10
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

11
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

12
Syllabus overview
  • Grading policy

5
5 points
Attendance (active learning exercises)
15
15 points
Assignments (asst 1 3)
25
25 points
Team Project (including 2 presentations)
Mid-term Exam (3/3/06 in class)
25 points
25
30
30 points
Final Exam (TBA in class)
100
100 points
TOTAL
Extra Credit Point
lt5 points
lt5
13
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 students 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.

14
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)

15
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.

16
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

17
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.

18
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.

19
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.

20
  • Best project award!

21
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
    745am, Feb. 6
  • A two-page project proposal is due 745am, Mar.
    1. After that, each group is expected to share
    updates of your project progress on a weekly
    basis.

22
Team Presentations
  • Final project presentations on May. 3 during
    regular class time. Every student should attend
    your groups presentation 15 min 5 min QA
  • 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 500pm, 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 QA

23
Project Deliverables
  • 745am, Feb. 6 group info, tentative project
    topic, class presentation topic via
    website/email
  • 745am, Mar. 1 Project proposal collected in
    class
  • May. 3 Project presentation
  • 500pm, 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.

24
Any questions before we move on?
Lets move on to start the DSS thread
25
A big picture of the course content
26
The decision making process
How a Decision Is Made?
27
The decision making process (cont.)
Intelligence
Design
Choice
Implement
Computer aided DSS
Decision Maker
28
Conceptual DSS Architecture
Output feedback
29
Decision Making
Why not easy?
Problem
  • Information
  • Uncertainty
  • Scarce Resources
  • Psychological factors (fear, power, anxiety)

Decision
30
Dos
  • Begin project and assignments early.
  • Think deeply and practice more.
  • Seek help and references.
  • Follow advice.
  • Have high self esteem.

31
Donts
  • Begin project deliverables near due dates
  • Begin assignments on weekends.
  • Miss lectures.
  • Feel dumb.
  • Ignore advice.
  • Panic during exams.

32
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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