Download - What is MIS? (And How We Figured It Out) A definition of the MIS field MIS 696a/797 Fall 1998
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What is MIS?(And How We Figured It Out)
A definition of the MIS field
MIS 696a/797 Fall 1998
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Agenda
The ProcessThe Areas of MISIndividual Paper PresentationsLessons Learned DiscussionDinner
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The Process
Brainstorming Session I via GroupSystemsFaculty Survey and InterviewsResearching areas of MISBrainstorming Session II: MIS areas
From areas to categories Splitting up the work
Which paper to choose?
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Which Paper to Choose?
Backtrack through paper referenceUse overview booksWeb siteswebofscience.comInterviews
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The Seven Pillars of MIS
A Breakdown of the MIS Field
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MIS Foundations & Methodology
Science & Scientific PracticeTheoretical Background
Systems Theory Cognitive Science Social Psychology etc.
Methodologies
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Database Technology
Physical Logical Conceptual Application
Modeling Applications
Operational
Special Applications (MM, web, temporal/ spatial)
Decision Support
Key Researchers• Peter Chen• E. F. Codd• Bill Inmon
• Bhawani Thuraisingham• Arie Segev• S. B. Navathe
Storage Structure
DBMS
Query Processing
DBMS Interoperability
Metadata Management
Transaction Management
Key Researchers• Jennifer Widom• Jeff Ulmann• Hector Garcia Molina
• Benjamin Wah• Richard Snodgrass• Umesh Dayal
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Software Development and Engineering
Models (waterfall, et al)System engineeringWorkflow management/Process modelingBusiness Process ReengineeringCASE tools
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Technical Aspects of MIS
Artificial IntelligenceAlgorithms & Data StructuresGroup Support Systems
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Human-Computer Interaction
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Human-Computer Interaction
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Human-Computer Interaction
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Human-Computer Interaction
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Organizational/Behavioral
System ManagementJudgment and Decision Making (individual
and group)Organizational ChangeEthical, Social and Legal IssuesInternational Issues
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Decision Sciences
Operations Research/ Operations ManagementDecision Support Systems/ Executive Support
SystemsEconomics of Information Systems
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Individual Paper Presentation
A Sample of Selected Papers
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Ethics: Authorship of Papers1) Conception of idea & design of experiment
2) Actual execution of experiment; hands-on lab work
3) Analysis & Interpretation of data
4) Actual writing of manuscript ICMJE: Each author = able to defend work publiclyAlternatives: Credits & ContributorsQuestion of “Guarantee” Important to ensure: Accountability with Credit
[sources: B. J. Culliton, Science Vol. 242 p.658; R. Smith, BMJ Vol. 314 p.992]
Presented by: Faiz Currim
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Usable Knowledge: Social Science and Social Problem Solving C.E.Lindblom & D.K.Cohen, 1979
The problem: the dissatisfaction from the social sciences as an instrument of social problem solving
L&C’s contribution: they discuss issues that social scientists should consider if they wish to be useful for social problem solving. e.g.,
How to define useful, success, or failure Misconceptions that social scientists have about social
science Includes a long bibliography section
Presented by: Irit Askira Gelman
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The Entity-Relationship Model - Toward a Unified View of Data
Who/Where? – Author: Peter Pin-shan Chen – Sources: ACM Transactions on Database Systems (1:1), 1976
What?– A conceptual data model entities + relationships– Commonly used for database design & analysis– ER diagram is used to visually represent data objects
Why?– Unify the network and relational database views– Lead to a proliferation of theoretical extension (e.g. EER)– Map well to the relational model– Simple and easy to understand with a minimum of training
Presented by: Dongwon Lee
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A Comparative Analysis of Methodologies for Database Schema Integration
Who/ Where/ When? Batini C., Lenzerini, M., Navathe S. B. [ACM Computing Survey,
1986]
What? Provide uniform framework for schema integration Comparative review of work done Strengths and weaknesses of existing methodologies General guidelines for future improvement
Why? Ties together various disparate frameworks Paves way for future work
Presented by: Vijay Khatri
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Federated Database Systems for Managing Distributed, Heterogeneous, and Autonomous Databases
Who/ Where / When? Authors: Amit P. Sheth & James A. Larson [ACM Computing
Surveys, 1990]
What? Define a reference architecture for distributed DBMS. Show how various FDBS architectures can be developed. Define a methodology for developing one architecture of an FDBS. Discuss critical issues on developing and operating an FDBS.
Why? Provide a reference architecture. Itself is extensively referenced.
Presented by: Huimin Zhao
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Software Engineering
The Capability Maturity Model for Software
Paulk, M. C., B. Curtis, & et al. (July, 1993). Capability maturity model, version 1.1. IEEE Software, 18-27.
Presented by: Conan Albrecht
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Lessons from a Dozen Years of Group Support Systems Research: A Discussion of Lab and Field Findings
Jay F. Nunamaker Jr., Robert O. Briggs, Daniel D. Mittleman, Douglas R. Vogel
Pierre A. Balthazard
Presented by: Karl Wiers
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Georgakopoulos, D., Hornick, M., & Sheth, A. (1995).An Overview of Workflow Management: From Process Modeling to Workflow Automation Infrastructure. Distributed and Parallel Databases, 3(2), 119-153
•Distributed Object Management•Customized Transaction Management
•Business Process Reengineering•Business Process Modeling
•Increase workflow automation in complex real-world environments involving heterogeneous, autonomous and distributed database systems
Presented by: Jeff Perry
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Process ModelingBill Curtis, Marc I. Kellner and Jim Over
Uses for Process Modeling Four Perspectives in Process Modeling
functional organizational behavioral informational
Comparison of Different Process Modeling Techniques
Presented by: Xiao Fang
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Frameworks for Component-Based Client/Server Computing
Who/ Where? Authors: Scott M. Lewandowski [ACM Computing Survey, 1998]
What? Review of client/server computing and component technologies Comparative study on the use of CORBA, DCOM and Java for
client/server computing Discussion on the frameworks issue, especially on business objects as a
client/server framework and compound documents as a client framework
Why? Provides a comprehensive review on related topics Proposes a new model for client/server computing
Presented by: Yi Shan
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A foundation for the study of group decision support systemsG. Desanctis and R. Brent Gallupe
Management Science, Vol. 33, March 1987What?
Goal of Group Decision Support Systems Measurement Three levels of the systems Taxonomy of systems: group size and member
proximity The role of task Research directions
Presented by: Dongsong Zhang
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Information Visualization for Collaborative Computing
Information structure for visualizationGroupware and collaborative computingTextual analysisA SOM Based Information Visualization Tool for
Groupware
H. Chen, O. Titkova, R. Orwig, J. F. Nunamaker
Presented by: Bin Zhu
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Agents that Reduce Work and Information OverloadPattie Maes
Discuss the basic concepts of autonomous agent
Popular examples: E-mail Agent, Meeting Scheduling Agent
Challenging future research direction Privacy, legal responsibilities
Presented by: Michael Chau
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The Psychology of Human-Computer Interaction (1983)
Cognitive models of human interaction with computers
Used to explain and predict human behaviorGOMS Model, Keystroke Level Model, etc.
Understanding ofhuman information processing
system design, analysis, and training
Presented by: Rosie Hauck
Stuart K. Card, Thomas P. Moran (Xerox Parc) and Allen Newell (CMU)
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The Impact of Sunk Outcomes on Risky Choice Behavior
Applied sunk cost research to sunk gains Integrated two major theoretical concepts
Problem framing• Mental accounting
• Editing rules - Prospect Theory Sunk outcomes
• Effects of prior gains and losses on risky decision making
Presented by: Gary Mahon
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Kling, Rob. (1991). Computerization and Social Transformations. Science, Technology and Human Values, 16(3), 342-367.
RQ: To what extent does the use of computer-based systems transform the social order (and, if so, how)?
Computerization may restructure major social relationships, including interpersonal, intergroup and institutional ones
The social effects of computerization are more complex than many suspect
Different sectors are affected to different degrees and in different ways
Computerization is not always transformative Empirical studies have difficulty identifying substantial
social changesPresented by: Craig Erwin
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Duchessi, P., and O’Keefe, R. A Knowledge-based Approach to Production Planning. J. Opl Res. Soc. 41(5), 1990.
Optimization techniques/heuristic approaches: lack credibility incur high cost of developing and using models require excessive data
A knowledge-based production planning system employs a set of production rules and inference mechanism to model the process of:
building plans, computing decision variable values, selecting combination of values for each period, and incorporating constraints and heuristics into the reasoning process.
Presented by: Poh-Kim Tay
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This paper describes: one company’s experienced-based approach to
production planning and how it was incorporated into a knowledge-based system.
the production planning state-space use of common planning constraints and
heuristic procedures a prototype that develops production plans for
one product family
Duchessi, P., and O’Keefe, R. A Knowledge-based Approach to Production Planning. J. Opl Res. Soc. 41(5), 1990.
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Reengineering the Corporation: A Manifesto for Business Revolution
What it’s aboutWhy I like it
Michael Hammer and James Champy (1993)
Presented by: Wayne Anderson
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Computers and IntractabilityA Guide to the Theory of NP-Completeness
Shows how to recognize NP-Complete problems
Lists over 300 main entries
Michael R. Garey and David S. Johnson
Presented by: Gregory Lousignont
Connect
ConnectC
onnect
Connect
Connect
Connect
Connect
Connect
Connect
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The Seven Areas???
MIS Foundations & MethodologyDatabase TechnologySoftware Development and EngineeringTechnical Aspect of MISHuman-Computer InteractionOrganizational/BehavioralDecision Sciences
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Lessons Learned
Group size (a problem)Organization (a problem)Definition (a problem)U of A MIS = General MIS Field?
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Discussion