Computational Web Intelligence for Wired and Wireless Applications

Yan-Qing ZhangDepartment of Computer Science

Georgia State University Atlanta, GA 30302-4110

yzhang@cs.gsu.edu

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Outline

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 + ITWI (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, CIAI. 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

fuzzy computing (FC) neural computing (NC), evolutionary computing (EC), probabilistic computing (PC), granular computing (GrC) rough computing (RC). …

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Web Technologya 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)

Uncertainty on the Web (FLINT 2001 at BISC at UC Berkeley http://www-bisc.cs.berkeley.edu/) (Zhang, et al, 2001 (a), (b) (c))

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 e-Business applications with uncertain data on the Internet and wireless networks.

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Computational Web Intelligence (cont.) (Zhang and Lin 2002)

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   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-

to-peer 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).

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.

Java conversionprogram

Data file 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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25

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45

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Date

Clo

se($

)

Predicted

Real

Personalized Wireless Information Agents for Mobile Phones

Personalized Weather Agent

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Search Agent

dispatch

user 1store

2store

Mobile Wireless Shopping Agents

go

Local Agent

generate result Local File

search messagewith result

go

result

messagewith result

Fuzzy Ranking Display

go

Search Agent

time outcounter=1

Search Agent

time outcounter=2

go Search Agent

search

Local File

go

Search Agent

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Mobile Fleet Application(Yamacraw Research Project)

Automated scheduling of pickups and deliveries

Distributed design Emergency Handling:

On-the-fly scheduling of package exchanges between trucks (rendezvous – peer-to-peer interaction)

Demo

Depot1 Depot2

 

Web and Data Center 

User

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SyD

listener

TDB

SyD

Listener

• A truck (Truck1) sends a request to the SyD Listener on a peer truck using SyD Engine “invoke” method.

• 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

• Each device is capable of functioning as client or server.

Truck1 Truck2

DBS: Database service TDB: Truck database

TDB

TruckAppO

TruckAppO

SyD Engine

SyD Engine

Truck to Truck Communication

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Conclusion

CWI based on CI and WT, a new research area, is proposed to increase the QoI of e-Business 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 CWI on wired and mobile wireless networks. Web Data Mining and Knowledge Discovery. Intelligent wireless mobile PDAs (do smart e-

Business, 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!

Any Question?