Computational Web Intelligence for
Wired and Wireless Applications
Yan-Qing Zhang
Department of Computer Science
Georgia State University
Atlanta, GA 30302-4110
[email protected]
Outline
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Introduction
Computational Intelligence
Web Technology
Computational Web Intelligence (CWI)
Wired and Wireless Applications
Conclusion and Future Work
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Introduction
QoI (Quality of Intelligence) of e-Business
 WI = AI + IT
WI (Web Intelligence) exploits Artificial
Intelligence (AI) and advanced Information
Technology (IT) on the Web and Internet .
(Zhong, Liu, Yao and Ohsuga) at Proc. the 24th
IEEE Computer Society International
Computer Software and Applications
Conference (COMPSAC 2000),
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Introduction (cont.)
“CI is a subset of AI”,
 “CI is not a subset of AI, there is an
overlap between AI and CI”.
 In general, CIAI.
crisp logic and rules in AI, and fuzzy
logic and rules in CI (Zadeh).
 Motivation: “Input CI onto Web?”
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Computational Intelligence
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fuzzy computing (FC)
neural computing (NC),
evolutionary computing (EC),
probabilistic computing (PC),
granular computing (GrC)
rough computing (RC).
…
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Web Technology
a hybrid technology including
computer networks, the Internet,
wireless networks, databases, search
engines, client-server, programming
languages, Web-based software,
security, agents, e-business systems,
and other relevant techniques.
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Computational Web Intelligence
(Zhang and Lin, 2002)
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Uncertainty on the Web (FLINT 2001 at BISC
at UC Berkeley http://www-bisc.cs.berkeley.edu/) (Zhang,
et al, 2001 (a), (b) (c))
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CWI = CI + WT (Zhang and Lin, 2002)
CWI is a hybrid technology of Computational
Intelligence (CI) and Web Technology (WT) on
wired and wireless networks.
CWI is dedicating to increasing QoI of eBusiness applications with uncertain data on
the Internet and wireless networks.
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Computational Web Intelligence (cont.) (Zhang
and Lin 2002)
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Fuzzy Web Intelligence
Neural Web Intelligence
Evolutionary Web Intelligence
Probabilistic Web Intelligence
Granular Web Intelligence
Rough Web Intelligence
Hybrid Web Intelligence
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Preface. . . . . . . . . . . . . . . . . . . . . . . . .
v
Introduction to Computational Web Intelligence and Hybrid
Web Intelligence. . .. . . . . . . . . . . . . xviii
Part I: Fuzzy Web Intelligence, Rough Web Intelligence and
Probabilistic Web Intelligence. . . . ... . . . . . . . . . . . . . . . . . . 1
Chapter 1. Recommender Systems Based on Representations. .. . . 3
Chapter 2. Web Intelligence: Concept-based Web Search. . . . . . . 19
Chapter 3. A Fuzzy Logic Approach to Answer Retrieval from the
World-Wide-Web
.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
Chapter 4. Fuzzy Inference Based Server Selection in Content
Distribution Networks. . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .
77
Chapter 5. Recommendation Based on Personal Preference. . . …..101
Chapter 6. Fuzzy Clustering and Intelligent Search for a Web-based
Fabric Database. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
Chapter 7. Web Usage Mining: Comparison of Conventional, Fuzzy and
Rough Set Clustering . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . 133
Chapter 8. Towards Web Search Using Contextual
Probabilistic Independencies. . . . .. . . . . . . . . . . . . . . .. . . . . . . 149
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Part II: Neural Web Intelligence, Evolutionary Web
Intelligence and Granular Web Intelligence
167
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Chapter 9. Neural Expert System for Vehicle Fault Diagnosis
via The WWW. . . .. . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . .169
Chapter 10. Dynamic Documents in The Wired World.. ... . . . .183
Chapter 11. Proximity-based Supervision for Flexible
Web Page Categorization. . . . .. . . . . . .. . . . .. . . . . . . . . . 205
Chapter 12. Web Usage Mining: Business Intelligence From Web Logs.
. . . 229
Chapter 13. Intelligent Content-Based Audio Classification and
Retrieval for Web Application. . . . . . . . . . . . . . . . . . . . . . . . . . . 257
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Part III: Hybrid Web Intelligence and e-Applications
283
Chapter 14. Developing an Intelligent Multi-Regional Chinese Medical Portal.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .285
Chapter 15. Multiplicative Adaptive User Preference Retrieval and Its
Applications to Web Search. . . . . . . . . . . . . . . . . . . . . . . . . . . . .303
Chapter 16. Scalable Learning Method to Extract Biological Information from
Huge Online Biomedical Literature. . . . . . . . . . . . . . . . . . .329
Chapter 17. iMASS: An Intelligent Multi-resolution Agent-based Surveillance
System. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .347
Chapter 18. Networking Support for Neural Network-based Web Monitoring
and Filtering. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369
Chapter 19. Web Intelligence: Web-based BISC Decision Support System
(WBICS-DSS) . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . .391
Chapter 20. Content and Link Structure Analysis for Searching the Web. 431
Chapter 21. Mobile Agent Technology for Web Applications. . . . 453
Chapter 22. Intelligent Virtual Agents and the WEB. . . . . . . . . . .481
Chapter 23. Data Mining in Network Security. . . . . . . . . . . . . . . .501
Chapter 24. Agent-supported WI Infrastructure: Case Studies in Peer-topeer Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515
Chapter 25. Intelligent Technology for Content Monitoring on the Web. .539
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Wired and Wireless Applications
CWI has various applications in intelligent
e-Business on the Internet and on
wireless mobile networks.
1. Neural-Net-based online Stock Agents,
2. Personalized Mobile Phone Agents,
3. Mobile Wireless Shopping Agents,
4. Mobile Wireless Fleet Application
(Yamacraw Research Project).
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Fuzzy Neural Web Agents for Stock Prediction
(Zhang, et al, 2001)
To implement this stock prediction system,
Java Servlets, Java Script and Jdbc are used.
SQL is used as the back-end database.
Data file
Java
conversion
program
SQL
table
Fig 1. Graph of Predicted and Real values for dow stock
using complete data (Zhang, et al, 2001)
Comparision of Predicted and Real values
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Predicted
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Real
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Close($)
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Date
Personalized Wireless Information
Agents for Mobile Phones
Personalized Weather Agent
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Mobile Wireless Shopping Agents
Local File
dispatch
user
Fuzzy Search Agent
Ranking
generate
Display
Local Agent
time out
time out counter=1
counter=2
go
search result
Search Agent
message
with result
go
go
Search Agent
message
with result
search result
Local File
store
2
store
1
go
Search Agent Search Agent
go
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Mobile Fleet Application
(Yamacraw Research Project)
Web and
Data
Center
User
Depot1
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Automated scheduling of
pickups and deliveries
Distributed design
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Emergency Handling:
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On-the-fly scheduling of
package exchanges between
trucks (rendezvous – peer-topeer interaction)
Demo
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Depot2
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Truck to Truck Communication
Truck
AppO
SyD
SyD
Listen
er
listene
r
SyD
Engine
TDB
Truck1
Truck
AppO
SyD
Engine
DBS: Database service
TDB: Truck database
•
A truck (Truck1) sends a
request to the SyD
Listener on a peer truck
using SyD Engine
“invoke” method.
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A selected (Truck2) peer
resolves the request
using Its own SyD
Listener and Engine.
•
Sends the result back to
the calling peer (Truck1).
•
IP address of peers are
resolved using the SyD
directory service
running in a central
location
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Each device is capable of
functioning as client or
server.
TDB
Truck2
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Conclusion
CWI based on CI and WT, a new research
area, is proposed to increase the QoI of eBusiness applications.
CWI has a lot of wired and wireless
applications in intelligent e-Business. FWI,
NWI, EWI, PWI, GWI, RWI, and HWI are
major CWI techniques currently.
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Future Work
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CWI on wired and mobile wireless networks.
Web Data Mining and Knowledge Discovery.
Intelligent wireless mobile PDAs (do smart eBusiness, Homeland Security, etc.)
Intelligent Wireless Mobile Agents (in cars,
houses, offices, etc.)
Intelligent Bioinformatics on the Web
CWI and Grid Computing.
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References
[1] Y.-Q. Zhang, A. Kandel, T.Y. Lin and Y.Y. Yao (eds.), “Computational Web Intelligence:
Intelligent Technology for Web Applications,” Series in Machine Perception and Artificial
Intelligence, volume 58, World Scientific, 2004.
[2] Y.-Q. Zhang and T.Y. Lin, “Computational Web Intelligence (CWI): Synergy of Computational
Intelligence and Web Technology,” Proc. of FUZZ-IEEE2002 of World Congress on Computational
Intelligence 2002: Special Session on Computational Web Intelligence, pp. 1104-1107, Honolulu, May
2002.
[3] M. Atlas and Y.-Q. Zhang, “Fuzzy Neural Web Agents for Efficient NBA Scouting,” Web Intelligence
and Agent Systems: An International Journal, vol. 6, no. 1, pp. 83-91, 2008.
[4] Y.-Q. Zhang, S. Hang, T.Y. Lin and Y.Y. Yao, “Granular Fuzzy Web Search Agents,” Proc. of
FLINT2001, pp. 95-100, UC Berkeley, Aug. 14-18, 2001.
[5] Y.-Q. Zhang, S. Akkaladevi, G. Vachtsevanos and T.Y. Lin, “Fuzzy Neural Web Agents for Stock
Prediction,” Proc. of FLINT2001, pp. 101-105, UC Berkeley, Aug. 14-18, 2001.
[6] Y. Tang and Y.-Q. Zhang, “Personalized Library Search Agents Using Data Mining Techniques,” Proc. of
FLINT2001, pp. 119-124, UC Berkeley, Aug. 14-18, 2001.
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Thank you!
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Any Question?
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