CIS 8900 - Knowledge Systems

Prerequisites

CSP 1-8

Course Material

• Required Text: V. Dhar and R. Stein, Seven Methods for Transforming Corporate Data into Business Intelligence, Prentice Hall, 1997.
• Reference Text: Data Mining Techniques, Michael Berry and Gordon Linoff (BL), Wiley 1997 (in Library Reserve). 
• Class handouts / overheads
• Software tools to be discussed in class

Course Description

This course covers the development and use of knowledge intensive systems in business applications. Businesses are becoming increasingly "knowledge intensive". In particular, with the explosion in the amount of data available, there is an increasing need for systems that help people filter, summarize, and interpret large amounts of very disparate kinds of data. At the same time, the enabling technologies such as database systems, networks, desktops, and Artificial Intelligence techniques have reached industrial strength maturity, providing unprecedented opportunities for building powerful decision support systems. Techniques to support knowledge intensive business processes and exploiting the vast amount of data available, especially in the Internet age are explored. Several development environments for the construction of knowledge intensive applications are studied. Various tools and techniques used in the development of knowledge intensive systems will be studied and the tradeoffs involved in choosing from among them will be evaluated.  Case studies of several knowledge intensive systems are used for insight into their motivation, construction, and use. Innovative e-business applications of knowledge intensive systems will be discussed.

Whether you are an information systems professional or a business manager/user, you need to understand the value the new technologies provide and how to recognize when they are useful. This course will give you a broad understanding of these technologies, a methodology that lets you evaluate the pros and cons of each of the technologies in the context of real-world problems, and exposure to business cases where this methodology has been applied.

Objectives

·        Understand different uses of knowledge systems (e.g., data mining systems) in various business domains

·        Understand the steps involved in the development of knowledge systems

·        Acquire working knowledge of several popular knowledge based techniques

·        Apply the various techniques to solve business problems

·        Acquire a working knowledge of some popular tools for knowledge systems design

·        Learn to recognize and overcome the obstacles in knowledge systems development and use

·        Become aware of the emerging tools and techniques to support knowledge systems.

·        Create commonly expected "deliverables" of a knowledge systems project in a group project.

Detailed objectives will be provided before each segment is covered in class.

 Evaluation Policy

General Class Policies

·        Students are expected to attend all classes and group meetings, except when precluded by emergencies, religious holidays or bona fide extenuating circumstances.  If you will be absent from class, be sure to notify a team member from whom you will need to acquire course notes and class assignments.  You will not have the opportunity to make up in-class work.

·        Students who, for non-academic reasons beyond their control, are unable to meet the full requirements of the course should notify the instructor. Refer to GSU catalog for details on Incomplete Grades.

·        Spirited class participation is encouraged and informed discussion in class is expected.  This requires completing readings and assignments before class.

·        All assignments are due at the beginning of the class on the due date.  Assignments may be turned in late with the permission of the instructor!  There will be a 10% penalty deduction for assignments turned in late.  No assignment will be accepted beyond one week late.

·        There are NO MAKE UP EXAMS.

·        Unless specifically stated by the instructor, all exams and lab assignments are to be completed by the student alone.  Collaboration will be considered cheating and the student/students involved will be immediately dropped from the program. 

·        Within group collaboration is allowed on project work.  Collaboration between project groups will be considered cheating unless specifically allowed by an instructor.

Communication

E-mail is the preferred way to communicate with the instructor.  As the e-mail is filtered automatically, each message must have following format in the subject header to receive a quick response:  CIS8900F01-topic, where topic is one of the following: Test-1, Test-2, Project-1, Project-2, Administrative-issues, or the various topics covered in class.

Plan

This course has ambitious objectives and will be only as beneficial to you as you want to make it for yourself. Expect to spend some time on the often-steep learning curve of the some knowledge systems / tools. Broad knowledge about the various aspects of data mining from business, technical and end user perspectives gained in class will be applied in a team project and case analyses. Classes will consist of a combination of lectures, discussions of business cases, and software demonstrations. Experts from the industry will make occasional presentations.

The course syllabus provides a general plan for the course; Deviations may be necessary!