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Count / Top-k Continuous Queries on P2P Networks
01/11/2006
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Outline
Problem Definition P2P Architecture Count Top-K Experiment Setup Future Work
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Streaming Data in P2P
P2PDynamic changing topology, large scale, …
Streaming dataContinuous, unbounded, rapid, time-varying,
noise P2P + Streaming data
Dynamic in both data and topology
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Objective and Goal
Objective Issue a continuous query to estimate count and
top-K Goal
Lower down the communication costLightweight maintenanceApproximated answersAn adaptive and progressive approach
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Naïve approach
Flooding the overlay continuousPros
Closer to the exact answer
Cons Network congestion Still non-real time
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The State-of-the-Art
CountFocus on one-time answer in P2PDeal with streaming data only
Top-KP2P environment without streaming dataDistributed environment not P2P
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P2P architecture
AssumptionHierarchical P2P (Focused)
Super-peer hierarchical structure Query issuer is a super-peer Super peer connect with other super peers Each peer belongs to only one super peer
Pure unstructured P2P
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Big picture
Group
Accumulate information within a group based on the constraintand statistics
Set Constraint
Report changes
Approximated answer
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Group in hierarchical P2P
Issuer
Coordinator
Peer
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Group in hierarchical P2P
3
1
4
2
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Group in hierarchical P2P
4
3
3
1
4
2
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Group in hierarchical P2P
4
3
3
1
4
2
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After partition
Group1
Group3Group2
,01,... 0ii N C
Assume we have N objects and K Groups after partition
,
:1, ...,
:1, ...,
: Count at each peeri j
i N
j K
C
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User-specified Epsilon
Group1
Group3Group2
User-specifiedε(Precision)
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Consider a group
P4
P1
P3
P2
CoordinatorNode
Objects
O1
O2
O3
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Each node maintain the distribution information of owning objects
P2
P4
P1
P3
object
Rate
#
R1
R2
R3
R4
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At initial - Polling
P4
P1
P3
P2
CoordinatorNode
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At initial - Polling
P4
P1
P3
P2
CoordinatorNode
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Information at coordinator after polling
object
#
22
2633
P4
P3P2
P1
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Statistics information
object
# P1 P2 P3 P4 ΔO1 1/1 6/6 10/10 5/5 22O2 11/11 13/13 5/5 9/7 36O3 15/15 6/6 3/3 9/9 33R 0.3 0.2 -0.05 0.6T 15 15 17 13
22
2633
Updated time stamp
Maximum changing rate(+/-) of objects in each peer
Change value for each objectLatest real value
Estimated value
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Update to Coordinator
(Δ11, Δ21, Δ31)
T2
(Δ12, Δ22, Δ32)
(Δ13, Δ23, Δ33)
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Calculate Count
( 1) ( ),0 ,0 ,
1
Kl li i i j
j
C C
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Redistribute Epsilon
,0( , , )i if C
wi=Max(Δi)/Cx,0 where x is the i-index of Max(Δi)δi=wiεCx,0/ ∑wi
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Visiting sequence
P4
P3P2
P1
Pick those peers would violate δ
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Update information
Group
P1 P2 P3 P4 ΔO1 1/1 6/6 10/10 8/8 -O2 11/11 11/11 5/5 6/6 -O3 15/15 5/5 3/3 11/11 -R 0.3 0.4 -0.05 0.2T 15 30 17 33
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For those nodes not being visited
Group
P1 P2 P3 P4 ΔO1 1/2 6/6 10/9 8/8 25O2 11/13 11/11 5/4 6/6 34 O3 15/18 5/5 3/2 11/11 36 R 0.3 0.4 -0.05 0.2T 15 30 17 33
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Un-notified Leave
P1
Ping
P1 is dead
Remove P1’s information
P4
P3P2
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Experiment Setup
Generate synthetic data set by statistics distribution for Streaming dataLife time of peers
MetricsMessage sizeCommunication costResponse latencyResult accuracy
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Top-K
Use Regression to predicate the reasonable trend of changesOnce a updated result is required, Super Peer
only need to ask those doubtful peers for doubtful objects
Update its counting list, and return the top k objects
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Future Work
Connect and recommend latent good friends for each user Good friends: the ones with the same interests (behaviors)
Exploiting current connecting peers to discover good friends bit by bit
Design a system that could make clusters reflecting current interests of individual peers and connecting them together based on their similarity by using user’s social network
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Advantages
Reduce search time and diminish query traffic by using friends list
By utilizing their different strength of arcs/edges/ties = friendshipness, social networks exceed random-walk networks in quickly finding target objects
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Example
Level 1
Level 2
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Example
has larger weight than
Score(Ni)
Score(Ni)
1 1( ) ( , ) ( )i i i iscore N sim N N score N
Similarity