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The Design and Evaluation of P2P Transactive Memory System

Fu-ren LinInstitute of Technology Management

National Tsing-hua UniversityHsin-chu Taiwan 300

R.O.C.frlin@mx.nthu.edu.tw

Kuo-Lung Tsai, Pei-Chen SunInstitute of Information, Computer and Education

National Kaohsiung Normal UniversityKaohsiung, Taiwan 802

R.O.C.esl@cc.nsysu.edu.tw, sun@nknucc.nknu.edu

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Rationale• A peer as a personalized agent can simulate its master

’s knowledge sharing behavior.• A peer builds its social networks through communicat

ing with other peers in computer networks.• A peer-to-peer network can be viewed as a distributed

knowledge management system by imbedding transactive memory system.

• A P2P network embedded with transactive memory can achieve the dual purposes: knowledge network development and individual autonomy.

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Research objectives

• Developing a P2P knowledge management system with transactive memory to assist peer’s expertise recognition, knowledge network maintenance, information allocation, and retrieval coordination.

• Designing a system model which is obedient to human nature and follows the system development trend of decentralization.

• Observing the evolution of knowledge network in the community.

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Transactive memory system

• Transactive memory theory explains how interdependent people within a knowledge network, each with their own set of skills and expertise, develop cognitive knowledge networks that help them identify the skills and expertise of others in the network .

• Three memory types– Internal memory: what you know– External memory: what others know– Transactive memory: know who knows what

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Transactive memory system

• 4 processes:– expertise recognition

• identifying who knows what – directory updating

• learning who knows what in the group– information allocation

• assigning memory items to group members – retrieval coordination

• planning how to find items in a way that takes advantage of who knows what

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P2P transactive memory system

Directory updating Expertise recognition

Information allocation Retrieval coordination

Information allocation module Retrieval coordination module

Expertise recognition

moduleStereotype

module

Cognitive K.N.maintenance

module

Authoritycomputing

module

Cognitive K.N. exchange module

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Roles of peers

• Authority– A peer is an authority when the peer is an expert in a topic

and many other peers refer it when they need the knowledge of this topic.

– The role of an authority plays a knowledge center to distribute knowledge to peers.

– Determining who is an authority is based on the linkages of whole network.

• Hub• Referral

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Roles of peers: hub

• A hub connects to multiple relative authoritative peers.

• We can see a hub peer as a recommender to indicate who an authority is.

• The hub peer pulls together authorities on a topic and allows us to ignore unrelated peers.

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Roles of peers: referral

• A referral makes a bridge between requestors and providers.

• When a peer wants to get something from authorities, the criteria for an authority to decide whether to provide requested objects is the peer’s propensity to share which consists of its altruism and social network strength.

• A referral may be a friend or others who have direct or indirect relationship with requestors and providers.

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Directory updating

• Cognitive knowledge network maintenance module

• Authority computing module• Cognitive network exchange module

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Expertise recognition• Expertise recognition module

– The expertise recognition module uses knowledge base to provide the mapping between expertise and knowledge items. When a peer receives advertises of other peers, the information will be processed by this module and map to knowledge items which the recipient maybe knows.

• Stereotype module– The stereotype module plays the role as a knowledge

base to provide necessary mappings, such as profession-expertise and expertise-knowledge, for expertise recognition module. When a peer can’t find the sources of needed knowledge item, the stereotype module provides a substitute to look up the possible alternative sources.

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Information allocation• Information allocation decides how to store the new

information to an appropriate peer. • When the related knowledge items are collected and stored

by certain peers, it is easy to retrieve later. • These authorities become the knowledge center and have the

responsibility to store and share these knowledge items. • The information allocation module extracts the key concept

from the incoming file, and obtains the authority list from the cognitive knowledge network maintenance module.

• After mapping the correlation, the file will be sent to the authoritative peers to store.

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Retrieval coordination

• The retrieval coordination module receives the search results from the expertise recognition module and the authority list from cognitive knowledge network maintenances module to retrieve the knowledge items from authorized peers.

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Knowledge sharing decision model

• DMxij = function [REQxj, SOCij, Ali, THRxj].

Peer j sends a request about knowledge item

x to peer i Peers i and j’s social relation strength Peer i’s altruism

threshold

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Evaluation• This study has developed a prototyping P2P system to evaluat

e the performance of a transactive memory system on knowledge sharing and task collaboration.

• In experiments, knowledge networks on a P2P network are updated based on two schemes: exploration and exploitation.

• Through exploration, a peer is a risk seeker to search potential knowledge owners through its acquainted peers.

• Through exploitation, a peer acquires information of other peers’ expertise via its cognitive knowledge network based on its transactive memory in terms of authority and hub.

• In experiments, different degrees of exploration and exploitation during knowledge sharing and task collaboration may result in different team performance.

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Experimental settings

• Initialization– 6 stages of interactions

• Knowledge items (categories): between 3 and 7 items initially

• Propensity to share: three levels • Learning curve• Network status measure index: egocentric and

whole networks.

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Experimental settings

• A peer’s knowledge sharing decision making– Propensity to share

• Altruism• Strength of social network

– Ability• The ability on certain knowledge is growing according

to a peer’s learning curve. • Learning curve is the path recording the progress track

of a peer along its interactions with others.

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Network status measure index

Index1: )1(*21

p

s

NN

I , 0 ≦ 1I ≦ 1

Index2: )1(*2

pp

k

NNN

I , 0≦ 2I ≦ 1

sN is the number of links a peer connects,

pN the maximum number of possible connections a peer can have in the network

kN the number of existing links of the whole network.

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Priority sequence of peer inquiry• The different degrees of exploration and exploitation ar

e measured in three parameters: – authority value, – cumulative inquiry success rate, and – risk aspect.

• For the group using exploration scheme, – Y = Sweight * SRit + Rweight *Ri, where we set Sweight = index1, and

Rweight = 1 - index1 (3)• For the group using exploitation scheme,

– Y = Ai * Aweight + SRi * Sweight + Ri * Rweight (4)• In the experiment, a peer selects one out of three peers

suggested by equations (3) and (4).

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Performance criteria

• Cumulative inquiry success• Learning outcomes

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Experimental design

1. Initializing a P2P network2. Constructing cognitive knowledge networks3. Exchange peers’ knowledge networks4. Comparing the evolutions of two groups via

exploration and exploitation, respectively.

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Experimental results-cumulative success rate

Stage 1

Stage 2

Stage 3

Stage 4

Stage 5

Stage 6

E1 0.24 0.3 0.32 0.37 0.42 0.48 E2 0.28 0.41 0.55 0.66 0.73 0.78

E1: the group with exploration schemeE2: the group with exploitation scheme

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Experimental results-learning outcomes

Stage 1 Stage 2 Stage 3 Stage 4 Stage 5 Stage 6

E1 0.013 0.023 0.042 0.087 0.19 0.32

E2 0.013 0.029 0.10 0.27 0.45 0.6

E1: the group with exploration schemeE2: the group with exploitation scheme

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Experimental results-changes of indices 1 and 2

Stage1

Stage2

Stage3

Stage4

Stage5

Stage6

Index1 0.43 0.64 0.76 0.87 0.91 0.94 E1 Index2 0.09 0.13 0.15 0.18 0.18 0.19

Index1 0.42 0.63 0.7 0.71 0.71 0.71 E2

Index2 0.09 0.16 0.21 0.25 0.30 0.33

E1: the group with exploration schemeE2: the group with exploitation scheme

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Findings

• Index1 and Index2 are designed to measure the status of egocentric and the whole networks.

• E1 increases cumulative success rate through the growing knowledge of the egocentric network.

• If a peer explores its egocentric network completely, it will find all peers in the network and raise the cumulative success rate.

• The increase of E1’s cumulative success rate ascribes to Index1’s increase.

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Findings (cont.)• E2’s peers observe the whole network to find the authoritati

ve peers. • Therefore, E2’s members exchange their cognitive knowled

ge networks with others and increase the understanding of the whole network.

• E2’s Index2 increases faster than E1’s, but E2’s Index1 stops increasing in the later stages. This is because E2’s peers quickly find all their authoritative peers in the network and stop unnecessary interactions.

• In this comparison, we found that the exploitation scheme is superior to the exploration in finding authorities.

• This finding is consistent with the reality of our human society. Through interactions with someone and recognition from other people, we can make an impression quickly and fairly.

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Conclusion• In this study, a transactive memory system has been designed

and prototyped to assist peer’s expertise recognition, knowledge network maintenance, information allocation, and retrieval coordination.

• In the evaluation, we calculate cumulative inquiry success rate and learning outcomes.

• We also observed the evolutions of cognitive knowledge network and found that transactive memory can help peer improve the development of their cognition knowledge networks.

• The use of transactive memory to design P2P knowledge system is not only obedient to human nature and follows the system development trend of decentralization, but also enhances the mechanisms of privacy, autonomy, and self-organization which a centralized architecture is hard to achieve.