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MIS 696A Final Presentation
Victor Benjamin, Joey Buckman, Xiaobo Cao, Weifeng Li, Zirun Qi, Lee Spitzley, Yun Wang, Rich Yueh
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Agenda
• Introduction•Literature review•Research gaps•Data collection•Results•Conclusion
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Introduction
•What is MIS?•Past work vs. current work•Conference papers•Lower submission-to-acceptance time
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MIS
Decision Support/Communication Organizational Behavior Electrical
Engineering/Healthcare Economics/Accounting
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Decision Support Behavioral Economics Healthcare and Engineering
-Information Retrieval/Artificial Intelligence
- Collaboration-Human-Computer Interaction-Social Issues and Ethics
-Economics/Decision Science-E-commerce
-Databases-Software Development and Engineering-Telecommunications
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Timeline of Greatness
• Artificial Intelligence and Information Retrieval• 1967: The successful knowledge-
based program Dendral is built• 1995: Bayesian method developed
for determining atomic positions• 1999: EcoCyc is built to query and
explore the genetics of E.Coli
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Timeline of Greatness
• Collaboration• 1971: Delphi method proposed as
group communication structure• 1987: Foundation for the study of
GSS• 1991: Benefits and drawbacks of
GSS established• 1996: Groupware Grid proposed
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Timeline of Greatness
• Database• 1961: IDS developed• 1970: E.F. Codd published article on
relational technology• 1973: Michael Stonebraker developed
Ingres/IBM with System R• 1976: Peter Chen and ER Model• 1980: First database build on Oracle
SQL
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Timeline of Greatness
•Decision Science•1980: Framework for DSS developed by Ralph Sprague•1987: Foundation of GDSS•2000: Proof of 2-Player Zero Sum Game Equilibrium
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Timeline of Greatness
• Economics• 1985: Pricing of computer services• 1988: Switching costs and lock-in
theories• 1993: Productivity paradox of IT• 1996: Emergence of E-Commerce• 1999: Economics of global IT
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Timeline of Greatness
• Human-Computer Interaction• 1952: Englebart begins defining information
manipulation problems• 1962: Licklider outlines “Man-Computer
Symbiosis” goals• 1965: First “computer mouse” unveiled (SRI)• 1977: Xerox PARC explores WYSIWYG
displays• 1977: “ZOG: A Man-Machine Communication
Philosophy” (CMU)
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Timeline of Greatness
• Social Issues and Informatics• 1988: Adaptive technology required for
team differences• 1994: Fair use and digital data• 1998: Trust in global virtual teams• 2000: Framework to study technology in
organizations• 2001: Intellectual property in an open
information environment
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Timeline of Greatness
• Systems Analysis and Design• 1968: Systems Analysis became a formal
discipline• 1972: Information hiding was promoted• 1979: “Structured Design” was published by
Edward Yourdon• 1980: Workflow emerges• 1986: Introduction of object-oriented
development• 1997: UML 1.1 was submitted
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MIS Hall of Fame
Hsinchun Chen
University of Arizona
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MIS Hall of Fame
Andrew B. Whinston
University of Texas at Austin
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MIS Hall of Fame
Ronald E. Rice
University of California Santa Barbara
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MIS Hall of Fame
Izak Benbasat
University of British Columbia
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MIS Hall of Fame
Jay F. Nunamaker
University of Arizona
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Research Gaps
• Previous groups examined journal papers• Journal papers have a lag time of
1 – 2 years•Good indicator of long-term
trends and historical information
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Our Focus
•Conference papers•A good indicator of where the field is today•A predictor of the near future•Larger quantity of papers
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Data Collection
•Conference papers are not easy to collect (not in a single database)•Some conferences cannot be collected online
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Data Collection
•After a search of each database we decided to collect: ICIS, AMCIS, CSCW, HICSS, KDD, SIGIR, WWW •Time Frame: 2008-2012
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Data Collection
•Data:•Title, Abstract, Keywords of total 6,036 papers.
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Data Collection
•Different conferences have different website patterns
(HTML tags, structures etc.)•We programmed text scrapers in Python and PHP for different website patterns
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Data Collection
•Data Sources• Microsoft Academic Search• ACM Proceedings• Official Conference Websites
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Data Collection
•Source Comparison
Microsoft Academic
ACM Proceedings
Official Websites
Data Amount Most conferences missing recent years’ paper
Cover all ACM conferences
One conference for each site
Data Quality Detailed info Missing key words
Detailed info
Data Access API + Json parsing
HTML Crawler + Information Extraction
HTML Crawler + Information Extraction
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Data Collection
•Microsoft Academic – JSON Parsing
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Data Collection
• ACM Proceedings IE – Regular Expression
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Data Collection
•Results
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What is LDA?
• Latent Dirichlet Allocation•Algorithm used for topic modeling•Originates from computer science in
2002• Some open source tools exist to help
researchers employ LDA
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How LDA works
• Computes chance of certain words appearing together• Also looks for word groups that appear exclusive from
one another• Assumes each document can be a mixture of various
topics• Returns clusters of words to user and topical make-up
of each document
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An example of LDA
• Suppose you have the following set of sentences:• I like to eat broccoli and bananas.• I ate a banana and spinach smoothie for breakfast.• Chinchillas and kittens are cute.• My sister adopted a kitten yesterday.• Look at this cute hamster munching on a piece of broccoli.
• According to LDA• Sentences 1 and 2: 100% Topic A• Sentences 3 and 4: 100% Topic B• Sentence 5: 60% Topic A, 40% Topic B• Topic A: 30% broccoli, 15% bananas, 10% breakfast, 10% munching, … (at
which point, you could interpret topic A to be about food)• Topic B: 20% chinchillas, 20% kittens, 20% cute, 15% hamster, … (at which
point, you could interpret topic B to be about cute animals)
Example source: http://blog.echen.me/2011/08/22/introduction-to-latent-dirichlet-allocation/
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An example of LDA
• Suppose you have the following set of sentences:• I like to eat broccoli and bananas.• I ate a banana and spinach smoothie for breakfast.• Chinchillas and kittens are cute.• My sister adopted a kitten yesterday.• Look at this cute hamster munching on a piece of broccoli.
• According to LDA• Sentences 1 and 2: 100% Topic A• Sentences 3 and 4: 100% Topic B• Sentence 5: 60% Topic A, 40% Topic B• Topic A: 30% broccoli, 15% bananas, 10% breakfast, 10% munching, … (at
which point, you could interpret topic A to be about food)• Topic B: 20% chinchillas, 20% kittens, 20% cute, 15% hamster, … (at which
point, you could interpret topic B to be about cute animals)
Example source: http://blog.echen.me/2011/08/22/introduction-to-latent-dirichlet-allocation/
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Conclusion
• Trending toward technical research•Away from behavioral research•No more TAM• Future work: broaden range of
conferences