1 / 21

Fuzzy Mobile Agents for Distributed e-Shopping Data Mining

This presentation by Lin Lu explores the architecture, design issues, and implementations of FMADeSDM, a mobile agent system for efficient and prompt e-shopping data mining. It also discusses fuzzy logic and algorithms used in the project.

hoskinsr
Download Presentation

Fuzzy Mobile Agents for Distributed e-Shopping Data Mining

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Fuzzy Mobile Agents for Distributed e-Shopping Data Mining Presented by Lin Lu

  2. Acknowledgement • First of all, thanks to Dr. Zhang for guidance, encouragement and patience throughout the length of the project • Thanks also go to my committee member, Dr. Sunderraman, for his continuous support over the time of my stay at GSU

  3. Overview • Introduction • Architecture of KAARIBOGA Mobile Agents • Design Issues of FMADeSDM • Implementations of FMADeSDM • Concluding Remarks • Demo

  4. Introduction • Background and purpose • Explosive growth of World Wide Web (WWW) makes retrieving information of interest dramatically more challenging • Currently-used smart commercial search engines always fall short in providing prompt and efficient results • Mobile agent paradigm has been recently developed, with high demands in e-commerce applications • Demands for Intelligent mobile agent

  5. Introduction • What is mobile agent? A mobile agent is an autonomous program that can migrate through a heterogeneous network searching for and interacting with services on user's behalf. • What is fuzzy logic? Fuzzy logic is a superset of conventional (Boolean) logic that has been extended to handle the concept of partial truth -- truth values between "completely true" and "completely false".

  6. Introduction (cont.) • Why fuzzy logic? • Fuzzy logic uses soft linguistic variables to represent the range of numerical values and allow these linguistic values to overlap. • Fuzzy logic can be used to deal with uncertain information to come up with decisions, which is ideal for solving real-world problems. • Algorithms used in the project • Fuzzy-user-preference-based ranking algorithm • Distributed data mining algorithm and centralized data-mining algorithm

  7. Architecture of KAARIBOGA Mobile Agents • Life-cycle Model – Creation – Start – Destroy – Dispatch – Arrival – Sleep – Awake – Message handling

  8. Pack agent intomessage Send Kaariboga Message Unpack agent Kaariboga Base Kaariboga Base Architecture of KAARIBOGA Mobile Agents (cont.) • Navigation Model Transfer agent between Kaariboga bases

  9. Kaariboga Base Kaariboga Base Architecture of KAARIBOGA Mobile Agents (cont.) • Communication Model Message exchange between agents and/or bases

  10. Kaariboga Domain Kaariboga Base Kaariboga Base Architecture of KAARIBOGA Mobile Agents (cont.) • Architecture of Kaariboga System Architecture of Kaariboga system

  11. PriceDistance Low Medium Short Very High High Medium   Medium High Medium Low 1.0 medium long 1.0 low medium high short Long Medium Low Very Low 0 0 Distance Min. (Min.+Max.)/2 Max. Min. (Min.+Max.)/2 Max. Price Fuzzy linguistic values for distance Fuzzy linguistic values for price  Fuzzy rule base for Price, Distance and Rank very low medium very high 1.0 low high High 0 0 0.083 0.25 0.5 0.75 0.917 1.0 Rank Fuzzy linguistic values for rank Design Issues of FMADeSDM • Fuzzy Ranking

  12. PriceDistance Low Medium Medium High Medium Long Medium Low   1.0 medium long short 1.0 low medium high 0.8 0.75 0.25 0.2 0 0 180 185 200 Price 220 Distance 5 15 25 17 Fuzzifications for price = $185 Fuzzifications for distance = 17miles Fuzzy rule for Price = $185, Distance = 17mile iPiDi (0.75*0.75*0.8+0.5*0.75*0.2+0.5*0.25*0.8+0.25*0.25*0.2) = PiDi (0.75*0.8+0.75*0.2+0.25*0.8+0.25*0.2) Design Issues of FMADeSDM(cont.) • Fuzzy Ranking Example Rank = = 0.64

  13. dispatch go Search Agent Search Agent message with result generate search result store user Local File Local Agent go time out Search Agent Design Issues of FMADeSDM(cont.) • Shopping Searching Agents • Search Agent 1 Scenario for search agent 1

  14. 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 Design Issues of FMADeSDM(cont.) • Shopping Searching Agents • Search Agent 2 Scenario for search agent 2

  15. Local File store Fuzzy Ranking 2 search result go Search Agent message with rank dispatch go go Update Fuzzy Value Search Agent Search Agent user store message with rank Fuzzy Ranking 1 generate search result Local Agent Local File time out go time out counter=1 counter=2 Search Agent Search Agent go Scenario for search agent 3 Design Issues of FMADeSDM(cont.) • Shopping Searching Agents • Search Agent 3

  16. Personalized fuzzy ranking criteria Implementation of FMADeSDM • Fuzzy Ranking

  17. Search result of search agent 1 (a) Interface for dispatching search agent 1 Message on visited store server Search result of search agent 1 (b) Implementation of FMADeSDM(cont.) • Search Agent 1

  18. Interface for dispatching search agent Search result of search agent Implementation of FMADeSDM(cont.) • Search Agent 2 & 3

  19. Concluding Remarks • Kaariboga Mobile Agents system is introduced and studied • Fuzzy Mobile Agents for Distributed e-Shopping Data Mining System is developed • Implemented three kinds of search mobile agents • Proposed a simple scenario to monitor the aliveness of each search agent

  20. Concluding Remarks (cont.) • Fuzzy-user-preference-based ranking algorithm is used • Dynamically updated fuzzy values are employed in distributed data mining algorithm • Ideas proposed in FMADeSDM can be extended to similar applications beyond the e-commerce application

  21. Demo

More Related