CS 440 Decision Support

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CS 440 Decision Support Intelligent Systems

• Fen Wang
• fen.wang_at_enc.edu
• Jan. 25, 2006

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Agenda

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

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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

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My contact information

• Old colony campus, 2nd floor, Room 4
• Phone [phone]
• E-mail fen.wang_at_enc.edu
• Office hour Tuesdays 930-1030am Wednesdays
  930-1030am (other time by appointment) ? Any
  problem??

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Introductory exercise

• Name?
• Background?
• Major/training?
• Answer a lucky question

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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

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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

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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.

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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

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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

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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

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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
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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.

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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)

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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.

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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

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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.

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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.

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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.

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• Best project award!

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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.

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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

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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.

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Any questions before we move on?
Lets move on to start the DSS thread
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A big picture of the course content
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The decision making process
How a Decision Is Made?
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The decision making process (cont.)
Intelligence
Design
Choice
Implement
Computer aided DSS
Decision Maker
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Conceptual DSS Architecture
Output feedback
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Decision Making
Why not easy?
Problem

• Information
• Uncertainty
• Scarce Resources
• Psychological factors (fear, power, anxiety)

Decision
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Dos

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

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Donts

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

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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