1. MIS 696A Final Presentation Victor Benjamin, Joey Buckman, Xiaobo Cao, Weifeng Li, Zirun Qi, Lee Spitzley, Yun Wang, Rich Yueh

2. Agenda Introduction Literature review Research gaps Data collection Results Conclusion

3. Introduction What is MIS? Past work vs. current work Conference papers Lower submission-to- acceptance time

4. MIS Decision Support/Communication Organizational Behavior Electrical Engineering/Healthcare Economics/Accounting

5. Decision SupportBehavioralEconomicsHealthcare and Engineering -Information Retrieval/Artificial Intelligence - Collaboration -Human-Computer Interaction -Social Issues and Ethics -Economics/Decisio n Science -E-commerce -Databases -Software Development and Engineering -Telecommunicatio ns

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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)

12. 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

13. 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

14. MIS Hall of Fame Hsinchun Chen University of Arizona

15. MIS Hall of Fame Andrew B. Whinston University of Texas at Austin

16. MIS Hall of Fame Ronald E. Rice University of California Santa Barbara

17. MIS Hall of Fame Izak Benbasat University of British Columbia

18. MIS Hall of Fame Jay F. Nunamaker University of Arizona

19. 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

20. Our Focus Conference papers A good indicator of where the field is today A predictor of the near future Larger quantity of papers

21. Data Collection Conference papers are not easy to collect (not in a single database) Some conferences cannot be collected online

22. Data Collection After a search of each database we decided to collect: ICIS, AMCIS, CSCW, HICSS, KDD, SIGIR, WWW Time Frame: 2008-2012

23. Data Collection Data: Title, Abstract, Keywords of total 6,036 papers.

24. Data Collection Different conferences have different website patterns (HTML tags, structures etc.) We programmed text scrapers in Python and PHP for different website patterns

25. Data Collection Data Sources Microsoft Academic Search ACM Proceedings Official Conference Websites

26. 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 QualityDetailed info Missing key words Detailed info Data AccessAPI + Json parsing HTML Crawler + Information Extraction

27. Data Collection Microsoft Academic – JSON Parsing

28. Data Collection ACM Proceedings IE – Regular Expression

29. Data Collection Results

30. 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

31. 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

32. 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/

33. 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/

36. Conclusion Trending toward technical research Away from behavioral research No more TAM Future work: broaden range of conferences