Computational Web Intelligence for Wired and Wireless Applications

1. Computational Web Intelligence for Wired and Wireless Applications Yan-Qing Zhang Department of Computer Science Georgia State University Atlanta, GA 30302-4110 yzhang@cs.gsu.edu

2. Outline • Introduction • Computational Intelligence • Web Technology • Computational Web Intelligence (CWI) • Wired and Wireless Applications • Conclusion and Future Work

3. 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),

4. 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?”

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

6. 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.

7. Computational Web Intelligence (Zhang and Lin, 2002) • Uncertainty on the Web (FLINT 2001 at BISC at UC Berkeley http://www-bisc.cs.berkeley.edu/) • CWI = CI + WT (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.

8. Computational Web Intelligence (cont.) • Fuzzy Web Intelligence • Neural Web Intelligence • Evolutionary Web Intelligence • Probabilistic Web Intelligence • Granular Web Intelligence • Rough Web Intelligence • Hybrid Web Intelligence

10. 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

11. 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

12. 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

13. 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).

14. Data file SQL table Java conversion program To implement this stockprediction system, Java Servlets, Java Script and Jdbc are used. SQL is used as the back-end database. Fuzzy Neural Web Agents for Stock Prediction

15. Fig 1. Graph of Predicted and Real values for dow stock using complete data

16. Personalized Wireless Information Agents for Mobile Phones

17. Personalized Weather Agent

18. Local File store 2 search result go Search Agent message with result go dispatch go Search Agent Fuzzy Ranking Display Search Agent store user message with result 1 generate search result Local Agent Local File time out go time out counter=1 counter=2 Search Agent Search Agent go • Mobile Wireless Shopping Agents

19. Web and Data Center User Depot2 Depot1 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

21. Truck to Truck Communication • 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. Truck AppO SyD Listener SyD listener Truck AppO SyD Engine SyD Engine TDB TDB DBS: Database service TDB: Truck database Truck1 Truck2

22. 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.

23. 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.

24. Thank you! • Any Question?