Business Intelligence Mining in Web 2.0: Data, Text and Web Mining for Finance, Accounting and ...

244 Pages·2015·4.13 MB·English

by  Ahmed Abbasi

“Business Intelligence Mining in Web 2.0:
Data, Text and Web Mining for Finance,
Accounting and Marketing Applications”

Hsinchun Chen, Ph.D.
Director, Artificial Intelligence Lab
Director, NSF COPLINK and Dark Web Research Centers
University of Arizona

Acknowledgements: NSF, LOC, ITIC/KDD, DHS, DOJ
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My Background
• NCTU  SUNY Buffalo  NYU  U Arizona (MIS #4)
• MS, MIS, Design Science, AI, Search Engine, Digital
Library, Medical Informatics, Intelligence & Security
Informatics, Business Intelligence
• AI Lab, 25+ researchers; $25M funding ($1.5M/year),
180 top SCI papers (20+ papers/year); DL (#1), MIS
(#8); Scientific Advisor: NLC, NLM, Academia Sinica;
Chair, ICADL, IEEE ISI
• AE in ten top SCI journals, IEEE and AAAS Fellow
• DL/SE; GeneScene & BioPortal; COPLINK & Dark Web
(NYT, USA Today, Associated Press, etc.); Knowledge
Computing Corporation ($100M)
• Business Intelligence Mining???
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The Peta Age

The End of Theory
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Outline
• Web 2.0 + Data Mining, Text mining, Web mining

• Intelligence and Security Informatics

• Case Studies, Examples, and Lessons Learned:
Business Intelligence Data, Text and Web mining

• Opportunities and Future Directions: Finance,
Accounting, and Marketing Applications
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Web 2.0, Data Mining, Text
Mining, and Web Mining
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Web 2.0, by O’Reilly
• http://www.oreilly.com, “What is Web 2.0? Design
Patterns and Business Models for the Next
Generation of Software,” by Tim O’Reilly,
9/30/2005 (O’Reilly Media Web 2.0 Conference,
2004)

• Examples of Web 2.0: Google AdSense, Flikr,
Napster, Wikipedia, blogging, search engine
optimization, web services, participation, tagging
(folksonomy), syndication, etc.

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Web 2.0, by O’Reilly
• Strategic positioning: “The Web as Platform”
• User positioning: “You control your own data”

• Core competencies:
– Services, not packageg software
– Architecture of participation
– Cost-effective scalability
– Remixable data sources and data transformations
– Software above the level of a single device
– Harnessing collective intelligence

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Web 2.0 Lessons
• The value of the software is proportional to the scale and
dynamism of the data it helps to manage.
• Leverage customer-self service and algorithmic data
management to reach out to the entire web, to the edges
and not just the center, to the long tail and not just the
head.
• The service automatically gets better the more better use
it.
• Blogging and the wisdom of the crowds.
• Network effects from user participation are the key to
market dominance in the Web 2.0 era.
• We, the media.
• Data is the next Intel inside.
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Web 2.0 Lessons (cont’d)
• Operations must become a core competency.
• The perceptual beta.
• Support lightweight programming models that allow for
loosely coupled systems. (SOAP, REST, AJAX, etc.)
• Think syndication, not coordination.
• Innovation in assembly. The Mashups.
• Design for “hackability” and remixability.
• Some rights reserved.

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Web 2.0, Wikipedia
• “Web 2.0 is a trend in the use of the WWW
technology and web design that aims to facilitate
creativity, information sharing, and collaboration
among users.”

• “Web 2.0 is the business revolution in the
computer industry caused by the move to the
Internet as platform, and an attempt to
understand the rules for success on that new
platform.”

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