fuzzy mobile agents for distributed e-shopping data mining presented by lin lu
TRANSCRIPT
Fuzzy Mobile Agents for Fuzzy Mobile Agents for
Distributed e-Shopping Data MiningDistributed e-Shopping Data Mining
Presented byLin Lu
AcknowledgementAcknowledgement
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
OverviewOverview
Introduction Architecture of KAARIBOGA
Mobile Agents Design Issues of FMADeSDM Implementations of FMADeSDM Concluding Remarks Demo
IntroductionIntroduction 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
IntroductionIntroduction 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.)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 Architecture of KAARIBOGA Mobile AgentsMobile Agents
Life-cycle Model – Creation – Start – Destroy – Dispatch – Arrival – Sleep – Awake
– Message handling
Architecture of KAARIBOGA Architecture of KAARIBOGA Mobile Agents (cont.)Mobile Agents (cont.)
Navigation Model
Send Kaariboga Message
Kaariboga Base Kaariboga Base
Transfer agent between Kaariboga bases
Pack agent into message
Unpack agent
Architecture of KAARIBOGA Architecture of KAARIBOGA Mobile Agents (cont.)Mobile Agents (cont.)
Communication Model
Message exchange between agents and/or bases
Kaariboga Base Kaariboga Base
Kaariboga Domain
Architecture of KAARIBOGA Architecture of KAARIBOGA Mobile Agents (cont.)Mobile Agents (cont.)
Architecture of Kaariboga System
Architecture of Kaariboga system
Kaariboga Base Kaariboga Base
Design Issues of Design Issues of FMADeSDMFMADeSDM Fuzzy Ranking
1.0
0
low medium high
Min. (Min.+Max.)/2 Max. Price
Fuzzy linguistic values for price
1.0
0
short medium long
Min. (Min.+Max.)/2 Max. Distance
Fuzzy linguistic values for distance
1.0
0
very low medium very high
low high
0 0.25 0.5 0.75 1.0 Rank0.083 0.917Fuzzy linguistic values for rank
Price Distance
Low Medium
Short Very High High Medium
Medium High Medium Low
Long Medium Low Very Low
High
Fuzzy rule base for Price, Distance and Rank
Design Issues of Design Issues of FMADeSDMFMADeSDM(cont.)(cont.) Fuzzy Ranking Example
1.0
0
low medium high
180 200 220 Price
Fuzzifications for price = $185
0.75
0.25
185
1.0
0
short medium long
5 15 25 Distance
Fuzzifications for distance = 17miles
0.8
0.2
17
PiDi
iPiDiRank =
(0.75*0.75*0.8+0.5*0.75*0.2+0.5*0.25*0.8+0.25*0.25*0.2)
(0.75*0.8+0.75*0.2+0.25*0.8+0.25*0.2) = = 0.64
Fuzzy rule for Price = $185, Distance = 17mile
Price Distance
Low MediumMedium High Medium
Long Medium Low
Shopping Searching Agents• Search Agent 1
storeuser
Search Agent
dispatch
Local Agent
generate
goSearch Agent
result
Local File
search messagewith result
Search Agent
time out go
Scenario for search agent 1
Design Issues of Design Issues of FMADeSDMFMADeSDM(cont.)(cont.)
Search Agent
dispatch
user 1store
2store
Shopping Searching Agents• Search Agent 2
Scenario for search agent 2
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
Design Issues of Design Issues of FMADeSDMFMADeSDM(cont.)(cont.)
Shopping Searching Agents• Search Agent 3
Scenario for search agent 3
Search Agent
dispatch
Local Agent
generate
go
Local File
resultsearch
Search Agent
messagewith rank
time out go
resultsearch
message with rank go
go
Search Agent
counter=1time outcounter=2
go
user Search Agent
Local File
Search Agent
1store
2store Fuzzy
Ranking
Fuzzy Ranking
Design Issues of Design Issues of FMADeSDMFMADeSDM(cont.)(cont.)
Update FuzzyValue
Implementation of Implementation of FMADeSDMFMADeSDM Fuzzy Ranking
Personalized fuzzy ranking criteria
Implementation of Implementation of FMADeSDMFMADeSDM(cont.)(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 Implementation of FMADeSDMFMADeSDM(cont.)(cont.)
Concluding RemarksConcluding 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.)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
DemoDemo