Design and Evaluation of P2P Transactive Memory System

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Design and Evaluation of P2P Transactive Memory
System

• Fu-ren Lin
• Institute of Technology Management
• National Tsing-hua University
• Hsin-chu Taiwan 300
• R.O.C.
• frlin_at_mx.nthu.edu.tw

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As We May Think

• by Vannevar Bush
• Originally published in the July 1945 issue of
  The Atlantic Monthly
• Consider a future device for individual use,
  which is a sort of mechanized private file and
  library. It needs a name, and to coin one at
  random, memex'' will do. A memex is a device in
  which an individual stores all his books,
  records, and communications, and which is
  mechanized so that it may be consulted with
  exceeding speed and flexibility. It is an
  enlarged intimate supplement to his memory.

Vannevar Bush (1890-1974 )
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Rationale

• A peer as a personalized agent can simulate its
  masters knowledge sharing behavior.
• A peer builds its social networks through
  communicating 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.

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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 (cont.)

• 4 inter-related processes for an agent to develop
  transactive memory system
• Expertise recognition
• Identifying who knows what
• KOxij indicates agent is perception of agent js
  level of expertise on a particular item, X.
• The actual expertise of agent j on item X would
  be defined as KIxj.

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Transactive memory system (cont.)

• Directory updating
• learning who knows what in the group
• KIxi f KIxi, INFxi, CAIxji
• KOxij f Ai, KIxi, COMij, Â((COMik)(KOxkj))

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Transactive memory system (cont.)

• Information allocation
• assigning memory items to group members
• CAIxij f KOxij, COMij, INFxi, Ai-Aj

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Transactive memory system (cont.)

• Retrieval coordination
• planning how to find items in a way that takes
  advantage of who knows what
• CRIxij f TASKxi, KOxij, COMij, Ai-Aj

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P2P Transactive Memory System (cont.)

• A P2P network embedded with transactive memory
  can achieve the dual purposes
• Knowledge network development
• Individual autonomy

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Research objectives

• Developing a P2P knowledge management system with
  transactive memory to assist peers 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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P2P transactive memory system
Directory updating
Expertise recognition
Expertise recognitionmodule
Stereotypemodule
Cognitive K.N.maintenancemodule
Authoritycomputing module
Cognitive K.N. exchange module
Information allocation
Retrieval coordination
Information allocation module
Retrieval coordination module
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Transactive Memory for Virtual Team Development
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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 peers 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
• The willingness of sharing knowledge depends on
  the peers altruism and the social network
  strength. It can be represented by following
  equation.
• SKNij function SOCij, Ali

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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 cant 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 f REQxj, SOCij, Ali, THRxj.

threshold
Peer j sends a request about knowledge item x to
peer i
Peers i and js social relation strength
Peer is altruism
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Evaluation

• This study has developed a prototyping P2P system
  to evaluate 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.

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Evaluation (cont.)

• 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
• whole networks.

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Experimental settings (cont.)

• A peers knowledge sharing decision making
• Propensity to share
• Altruism
• Strength of social network
• Ability
• The ability on certain knowledge is growing
  according to a peers learning curve.
• Learning curve is the path recording the progress
  track of a peer along its interactions with
  others.

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Learning Curve
k Y(x) 0.5 Y(x) ? 0.99
0.4 x 12 x 24
0.3 x 16 x 32
0.2 x 23 x 46
0.1 x 46 x 92
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Network status measure index
is the number of links a peer connects,
the maximum number of possible connections a
peer can have in the network
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 are measured in three parameters
• authority value,
• cumulative inquiry success rate, and
• risk aspect.

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Variables

• Ai denotes the authority values of peer i which
  owns a knowledge item in the knowledge network.
• SRi denotes the cumulative inquiry success rate
  of past transactions with peer i.
• Rij represents the risk aspect of of peer i
  toward peer j depends on the interaction
  frequency between peer i and j.
• If two peers have no interactions before, the
  risk is 1.0 if two peers had interaction, but
  did not succeed, the risk is set to 0.7.

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Priority sequence of peer inquiry (cont.)

• Exploration Y Sweight SRit Rweight Ri,
  where we set Sweight index1, and Rweight 1 -
  index1
  
• Exploitation Y Ai Aweight SRi Sweight
  Ri Rweight
• A peer selects one out of three peers suggested
  by exploration and exploitation.

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Performance criteria

• Cumulative inquiry success
• Learning outcomes

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Experimental design

• Initializing a P2P network
• Constructing cognitive knowledge networks
• Six stages, each stage performs five QA
  activities
• Exchange peers knowledge networks
• Comparing the evolutions of two groups via
  exploration and exploitation, respectively.

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Experimental results-cumulative success rate
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Experimental results-learning outcomes
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Experimental results-changes of indices 1 and 2
E1 the group with exploration scheme E2 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 E1s cumulative success rate
  ascribes to Index1s increase.

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Findings (cont.)

• E2s peers observe the whole network to find the
  authoritative peers.
• E2s members exchange their cognitive knowledge
  networks with others and increase the
  understanding of the whole network.
• E2s Index2 increases faster than E1s, but E2s
  Index1 stops increasing in the later stages.
• This is because E2s peers quickly find all their
  authoritative peers in the network and stop
  unnecessary interactions.

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Findings (cont.)

• 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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Evolutions of Peers KN By Exploration
Stage 1. Index10.44, Index20.09 Stage 2. Index10.72, Index20.14

Stage 3. Index10.83, Index20.17 Stage 4.Index10.94, Index20.19

Stage 5. Index10.94, Index20.19 Stage 6. Index11.0, Index20.2

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Evolutions of Peers KN By Exploitation
Stage 1. Index10.22, Index20.07 Stage 2. Index10.61, Index20.19

Stage 3. Index10.78, Index20.28 Stage 4. Index10.78, Index20.38

Stage 5. Index10.78, Index20.4 Stage 6. Index10.78, Index20.47

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Conclusion

• A transactive memory system has been designed and
  prototyped to assist peers expertise
  recognition, knowledge network maintenance,
  information allocation, and retrieval
  coordination.
• Evaluate cumulative inquiry success rate and
  learning outcomes.
• The evolutions of cognitive knowledge network
  show 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.

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Ongoing Works

• Developing P2P application systems to conduct
  field experiments.