Dynamic P2P Indexing and Search based on Compact Clustering Mauricio Marin Veronica Gil-Costa...
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Transcript of Dynamic P2P Indexing and Search based on Compact Clustering Mauricio Marin Veronica Gil-Costa...
Dynamic P2P Indexing and Search based on Compact Clustering
Mauricio Marin Veronica Gil-Costa Cecilia Hernandez
UNSL, Argentina Universidad de ChileYahoo! Research Latin America
OutlineIntroductionData Structure IndexP2P NetworksSimPeerP2P Bottom-upExperimentsConclusions and Future Work
IntroductionSimilarity search over a collection of metric-
space database objects distributed on a large and dynamic set of small computers forming a Peer-to-Peer (P2P) network has been widely studied in recent years.
Currently there are efficient solutions for structured networks like those based on the general purpose CAN and Chord protocols.
IntroductionSuper-peer systems are believed to represent
a good tradeoff between centralized and distributed architectures. They are also considered a reasonable tradeoff between unstructured and structured P2P networks.
In this case the network is seen as a collection of stable peers called super-peers to which normal peers can connect and initiate queries.
Previous WorkKM (SimPeers) is the state of the arte strategy
for peers and super-peers.
Its main drawback is that it employs local indexingin a bottom-up fashion.
This work (LC) employs global indexing in a top-downfashion.
List of Cluster (LC)I3
(c3, r3, I3)
I2
(c2, r2, I2)E2
I1
(c1, r1, I1)E1
c1r1
c2
c3
r2
r3
Clusters of fixed size
List of Cluster (LC)
c
r
q
rd(c,q) cr
q r
d(c,q)
c
r
q rd(c,q)
LC-SSS(c1, r1, I1) (c1, r1, I1) (c1, r1, I1)
Sparse Spatial Selection Algorithm
P2PHierarchical system of peers and super-peers
Super-peer
peers
Bottom-up
Np
Np
Np
1 … M
1 … M
(ci,ri)
M*Np1… M
1… M
LC-SSS
LC-SSS
semi-globalcenters
1… M
Bottom-up
Np
Np
Np
1 … M
1 … M
LC-SSS
LC-SSS
<ci,rm,rx,bi>
<cj,rm,rx,bj>
semi-globalcenters
…
(i,csp,sp,r’m,r’x)*(i,csp,sp,r’m,r’x)*(i,p,rm,rx)…(i,p,rm,rx)(i,p,rm,rx)
Searching
Np
<ci,rm,rx,bi>
<cj,rm,rx,bj>…
(i,csp,sp,r’m,r’x)*(i,csp,sp,r’m,r’x)*(i,p,rm,rx)…(i,p,rm,rx)(i,p,rm,rx)
qr
tp
ts
rx
rm
q
d(q,c)-r ≤ rx
q
d(q,c)+r rm
Updates
requerimiento Sends M semi-global centers (ci,ri)
Overflow area
Overflow areaNew centersIntersectionIntersection
degreedegree
M
c2
Updates: Intersection Degree
c1r1
c2
r2
If (d(c1, c2) ≤ r1 + r2) S1,2 = 1 Else S1,2 = 0
c1
c2
S1,2 = 1+r2/r1
c1
S1,2 = (r1/r2) ·S1,2 S1,2 = (|r1 − r2|/d(c1, c2) ) · S1,2
c1c2
All centers k for which Sk,1 is 0 are considered candidates to become new global centers (ck, rk)
Experimental ResultsMetric Spaces Library SISAP (
http://www.sisap.org/Home.html)Uniform 3.000.000Gauss 3.000.000NASA 3.000.00030 super-peers and 1.000 peersM = 10 centers
Constant Number of Peers
Total number of distance evaluations and messages for global and local indexing by using the LC strategy.
PERCENTAGE OF EFFECTIVENESS:Percentage of objects that are compared with the query and become part
of the query answer.
Increasing the Number of Peers
As new peers join to the network the algorithms require more distance evaluations to processes queries,
Further experiments in the paper
Conclusions
The paper has shown that by approximating global but resumed information about the indexed data in each peer, the average amount of computation and communication performed to solve range queries can be significantly reduced.
Future Work
Currently we are studying different cache techniques to optimize similar searches and reduce queries response time.
Contact Information
Mauricio Marin [email protected]
Veronica Gil-Costa [email protected]
Cecilia Hernandez [email protected]