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

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

Overview  Introduction  Architecture of KAARIBOGA Mobile Agents  Design Issues of FMADeSDM  Implementations of FMADeSDM  Concluding Remarks  Demo

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

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".

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

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

Architecture of KAARIBOGA Mobile Agents (cont.)  Navigation Model Send Kaariboga Message Kaariboga Base Transfer agent between Kaariboga bases Pack agent into message Unpack agent

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

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

Design Issues of FMADeSDM  Fuzzy Ranking lowmediumhigh  Min.(Min.+Max.)/2Max.Price Fuzzy linguistic values for price short mediumlong  Min.(Min.+Max.)/2Max. Distance Fuzzy linguistic values for distance very lowmediumvery high  lowhigh Rank Fuzzy linguistic values for rank Price Distance LowMedium Short Very HighHighMedium HighMediumLow Long MediumLowVery Low High Fuzzy rule base for Price, Distance and Rank

Design Issues of FMADeSDM (cont.)  Fuzzy Ranking Example lowmediumhigh  Price Fuzzifications for price = $ short mediumlong  Distance Fuzzifications for distance = 17miles PiDiPiDi  i P i D i Rank = (0.75*0.75* *0.75* *0.25* *0.25*0.2) (0.75* * * *0.2) = = 0.64 Fuzzy rule for Price = $185, Distance = 17mile Price Distance LowMedium HighMedium LongMediumLow

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

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

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

Implementation of FMADeSDM  Fuzzy Ranking Personalized fuzzy ranking criteria

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

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

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

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

Demo