Clustering using Hypergraph for P2P Query Routing
Simulation and Evaluation
Anis Ismail
1
, Mohammad Hajjar
1
, Mohamed Quafafou
2
,
Nicolas Durand
2
and Mazen El Sayed
1
1
Lebanese University, Beirut, Lebanon
2
LSIS, Domaine Universitaire de Saint-Jérôme, Marseille, France
Keywords: Ecclat, Hypergraphs, Mtminer, P2P Network, Query Routing.
Abstract: Peer-to-peer overlay networks offer a flexible architecture for decentralized data sharing. In P2P schema-
based systems, each peer is a database management system in itself, ex-posing its own schema. In such a
case, the main objective is the efficient search across peer databases by processing each incoming query
without overly consuming bandwidth. The usability of these systems depends on efficient and effective
routing of content-based queries is an emerging problem in P2P networks. This work was attended to
motivate the use of mining algorithms in the P2P context to improve the efficiency of such methods. Our
proposed method combines clustering and hypergraphs. We use ECCLAT to build approximate clustering
and discovering meaningful clusters with slight overlapping. We use the algorithm MTMINER to extract all
minimal transversals of a hypergraph (clusters) for query routing. The set of clusters improves the
robustness in queries routing mechanism and scalability in P2P Network. Our experimental results prove
that our method generates impressive levels of performance and scalability with respect to important criteria
such as response time, precision and recall.
1 INTRODUCTION
Peer-to-Peer (P2P) has emerged as an efficient way
to share huge volumes of data (Akbarinia and
Martins, 2006). The most important problem in such
networks is query routing, i.e. deciding to which
other peers, the query has to be sent for high
efficiency and effectiveness. However, systems that
broadcast all queries to all peers suffer from limited
effectiveness and scalability.
Nodes, like super-peers, process queries and
produce as results, a group of peers; the result of a
query is the union of results from every super-peer
(SP) and their peers that process the query. When a
peer submits a query, this peer becomes the source
of this query that is transmitted to its SP. The routing
policy in use determines relevant neighbours SP
quickly, based on semantic mappings between
schemas of (super-)peers, and then send the query to
them. When a SP receives a query, it will process it
over its local collection of data sources taking into
account its different peers. If at least one of its peers
answers the query then results are found and the SP
will send a single response message back to the peer
source. The most important challenge for the
information retrieval in P2P networks is also to be
able to direct the query to the other peers that
contain the most relevant answers in a fast and
competent way.
Our main goal is the efficient search across the
P2P network while routing the queries directly to
relevant peers. To accomplish this goal, it is crucial
that each query is not broadcast into the whole
network, but is routed to a relevant set of peers.
Furthermore, the efficiency and good performance
of the whole P2P network does not only depend on
how the query is routed to relevant peers, but also on
how it is routed to these relevant peers with
minimum query processing and bandwidth
consumption.
The following section presents some related
works. Section 3, presents the baseline algorithm of
queries routing in hybrid P2P systems and the
concept of hyper-graph. Section 4 presents our work
“Minimal covering shortcut” approach. Section 5
presents Experiments and Evaluations. In Section 6,
we present the conclusion.
247
Ismail A., Hajjar M., Quafafou M., Durand N. and El Sayed M..
Clustering using Hypergraph for P2P Query Routing - Simulation and Evaluation.
DOI: 10.5220/0004452302470254
In Proceedings of the 15th International Conference on Enterprise Information Systems (ICEIS-2013), pages 247-254
ISBN: 978-989-8565-59-4
Copyright
c
 2013 SCITEPRESS (Science and Technology Publications, Lda.)

2 RELATED WORKS
Knowledge discovery and data mining (KDD) in
Peer-to-peer network is a relatively new field with
little related literature. P2P data mining has recently
emerged as an area of KDD research, specifically
focusing on algorithms which are efficient in query
routing and scalability. For instance, Bhaduri
(Bhaduri et al., 2008) propose an alternate solution
that works in a completely asynchronous manner in
distributed environments and offers low
communication overhead, a necessity for scalability.
Content location is a challenging problem in
decentralized peer-to-peer systems. And query-
flooding algorithm in Gnutella system suffers from
poor scalability and considerable network overhead.
Currently, based on the Small-world pattern in the
P2P system, a piggyback algorithm called interest
based shortcuts gets a relatively better performance.
In P2P systems, research, such as P-Grid
(Aberer, 2001) Chord, CAN (Ratnasamy et al.,
2001), or is based on various forms of distributed
hash tables (DHTs) and supports mappings from
keys, e.g., titles or authors, to locations in a
decentralized manner such that routing scales well
with the number of peers in the system. PlanetP
(Cuenca-Acuna et al., 2003) is a publish-subscribe
service for P2P communities and the first system
supporting content ranking search. PlanetP
distinguishes local indexes and a global index to
describe all peers and their shared information. The
global index is replicated using a gossiping
algorithm. The system, however, is limited to a few
thousand peers.
Strategies for P2P request routing beyond simple
key lookups but without considerations on ranked
retrieval have been discussed in (Cohen et al., 2003),
(Crespo and Garcia-Molina, 2002), but are not
directly applicable to our setting. The construction
of semantic overlay networks is addressed in,
(Crespo and Garcia-Molina, 2002) using clustering
and classification techniques; these techniques
would be orthogonal to our approach. Tong et al.
(Tong and Yang, 2005) distribute a global index
onto peers using LSI dimensions and the CAN
distributed hash table. In this approach peers give up
their autonomy and must collaborate for queries
whose dimensions are spread across different peers.
(Aberer et al., 2004) addresses the problem of
building scalable semantic overlay networks and
identifies strategies for their traversal. Castano and
Montanelli addressed the problem of formation of
semantic Peer-to-Peer communities (Castano and
Montanelli, 2005). Each peer is associated with an
ontology which gives a semantically rich
representation of the interests that the peer exposes
to the network, in terms of concepts, properties and
semantic relations. Each peer interacts with others
by submitting discovery queries in order to identify
the potential members of an interest-based
community, and by replying to incoming queries
whether it can join a community. A semantic
matchmaker is employed to check whether two peers
share the same interests. Datta et al. proposed an
algorithm for K-Means clustering over large,
dynamic networks (Datta et al., 2006).
3 SEMANTIC MAPPINGS
AND HYPER-GRAPHS
This section is devoted to the study of two methods
developed and used for queries routing in P2P
communities. The baseline method developed in
(Faye et al., 2007), uses semantic similarity
functions to establish semantic mapping between
peers and peers/super-peers. Unfortunately, this
approach is not being scale due to the mappings it
uses and this problem arise considering only
thousands of Peers in the network. This limit
motivates our investigation and the development of
our new method based respectively on
clustring/hypergraphs.
3.1 Baseline Approach
A new Peer Pj advertises its expertise by sending, to
its Super-Peer, a domain advertisement DA
j
= (PID;
j
XP
E
, T
j
; Ɛ
acc
; TTL) containing the Peer ID denoted
PID, the suggested expertise
j
XP
E
, the topic area of
interest T
j
, the minimum semantic similarity value
(Ɛacc) required to establish semantic mapping
between the suggested expertise
j
XP
E
and the theme
of its SP. When receiving an expertise
j
XP
E
, a
Super-Peer SP
A
invokes the semantic matching
process to find mappings between its suggested
schema and the received expertise.
The semantic routing algorithm (Algorithm 1) of
baseline approach exploits the expertise of (super-
)Peers and the two levels of mappings in order to
forward a query Q to only relevant Super-Peers. A
Peer P
2
submits its query Q
2
on its local data
schema. This query is sent to his Super-Peer SP
A
responsible for the community (See Figure 1). The
Super-Peer SP
A
in turn suggests, based on the index
obtained by the process of mediation (first level), the
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Peers P
1
of his community or the other Super-Peers
SP
P
that are able to treat this query. Each submitted
query received by a Super-Peer, is processed by
searching connections (second level of mappings)
between the subject of this query and expertise of
Peers (of the same community) or the description of
themes of other Super-Peers.
Figure 1: Network configuration and query routing
(Baseline approach).
Algorithm 1: Baseline algorithm
1:
2:
3:
4:
5:
6:
7:
8:
9:
10:
Input: Q : Query
SP : Super-peer of P
Output: SRQ : Set of answers of Q
Variables : PSet : Set of peers
NP : Neighbours of SP (set of super-peers)
SRQ = Φ
PSet = Capacity CMSP/P (Q) >ε
acc
repeat
SPQ = get(s ϵ PSet);
Remove SPQ from PSet;
SRQ = SRQ

Query(SPQ);
until (PSet = Φ)
repeat
SPQ = Capacity CMSP/SP (Q) > ε
acc
Remove SPQ from NP;
SRQ = SRQ

BL(Q, SPQ);
until (PSet = Φ)
Return(SRQ);
In turn, a SP from the nearby community, having
received this request, researches among Peers (in his
community) who are able to answer this query. The
major problem of this approach is the mediation at
the two levels cited above: if we take thousands of
Peers or Super-Peers this approach can not be scale
due to the mappings at both levels.
The routing of Query in these networks is
therefore very problematic. Semantic Routing is a
method of routing which focuses on the nature of the
query to be routed than the network topology.
Essentially semantic routing improves on traditional
routing by prioritizing nodes which have been
previously good at providing information about the
types of content referred to by the query. Semantic
Routing is obviously not the most optimal solution
for routing, and it wasn't long before other P2P
routing algorithms emerged which were more
efficient.
Assuming that peer P
2
issues a query Q
2
, the
query routing algorithm proceeds as follows:
- We first find the responsible super-peer for P
2
which in this example is SP
A
.
- The responsible Super-Peer (SP
A
) process the
query to find the relevant peers of his community
(ex.: P
1
) if there are, and also find the others
Super-Peers (ex.: SP
P
) that might contain relevant
peers to answer the query.
- Each relevant Super-peer(s) (SP
A
, SP
P
) treat(s)
query to find relevant peers using the function
CAP that measures the capacity of a peer of
expertise EXP(P
1
) on answering a given query of
subject of Sub(Q).
)),((
Sub(Q)
1
),(
Sub(Q) s
Exp(P) e
esSsMaxQPCap




- Then the final set of relevant peers
((P
1
:SP
A
)...(P
5
:SP
P
)) and their corresponding
super-peers are returned. Semantic routing is not a
reasonably idea when the network growth. This
motivates us to develop a new approach based on
clustering super-peer.
3.2 Hypergraph Transversals based
Approach
This section introduces a new efficient method for
queries routing in the P2P context that is based on
both the super-peer clustering algorithm called
Ecclat (Durand and Cremilleux, 2002), (Durand et
al., 2006) and the computation of a minimal query
routing strategy. The clustering of super-peers using
their expertise leads to the construction of
communities where each one is represented by a set
of super-peers (cluster of super-peers) with the
constraint that a super-peer may belong to more than
one cluster. In this situation the set of clusters
constitutes a set of hypergraph and where each node
constitutes a community. The question is than how
to find the minimal querying strategies where each
one is a set of super-peers that covers all
communities. The function cover means that the
minimal set contains at least on super-peer of each
community. Consequently, this strategy guaranties
that one represents all expertise of the network.
Thus, we consider that a strategy is a semantic
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249

context that can be useful for queries routing. In fact,
when a super-peer SP receives a query Q and can
not answer it using only its peers then it selects the
possible minimal strategy minS where SP

minS.
A transversal is minimal in the sense that
guaranties that all communities (clusters of super-
peers) are represented:

Tc

T ;

c

C : Tc

c


;
Where C is the set of communities (super-peers
clusters), T is the set of transversals.
In our context, we cluster super-peers according
to their expertise. Table1 presents an example of
transactional dataset. There are 8 transactions
(denoted SP
1
… SP
8
) and 9 items (denoted W
1
…
W
9
). Transactions correspond to super-peers. Items
correspond to components of a query successfully
processed by the super-peers. For example, W
1
is
present in the transaction SP
1
because W
1
is a
component of a query successfully processed by the
super-peer SP
1
. The obtained clusters with
minfr=20% and M=1 (M is an integer corresponding
to a number of transactions not yet classified that a
new selected cluster must classify) (minfr: minimum
number of transactions in a cluster) (Durand, 2002)
are: (W
1
, W
2
, W
3
; SP
1
, SP
2
, SP
3
), (W
4
, W
5
; SP
4
, SP
5
,
SP
6
), (W
1
, W
6
, W
7
; SP
6
, SP
7
) et (W
9
; SP
7
, SP
8
).
Table 1: Example of a dataset D1.
The cluster (W
1
, W
2
, W
3
; SP
1
, SP
2
, SP
3
) shows that
SP
1
, SP
2
and SP
3
share an expertise characterized by
the association of the components W
1
, W
2
and W
3
.
Table 2 presents another example with 300 Peers
and 10 Super-Peers. The resulting clusters minfr =
20% and M=1 are:
(W
19
, W
37
, W
40
, W
41
, W
45
, W
46
; SP
5
, SP
6
, SP
10
),
(W
17
, W
36
, W
37
, W
38
, W
39
, W
41
, W
42
; SP
4
, SP
6
, SP
7
),
(W
6
, W
21
; SP
2
, SP
8
, SP
9
), (W
5
, W
6
, W
8
; SP
1
, SP
2
,
SP
8
), (W
2
, W
4
; SP
1
, SP
3
, SP
5
)
Figure 2 focuses only on the resulted five
clusters. An interesting feature of the clustering
algorithm is its ability to produce a clustering with a
minimum overlapping between clusters
(approximate clustering) or a set of clusters with a
slight overlapping.
Table 2: A dataset D2.
Figure 2: Obtained Clusters with traversals.
4 MINIMAL COVERING
SHORTCUT APROACH
The goal of this method is to characterize
collectively the communities. Therefore we
characterize not a particular community, but all
communities in the network. First, we explicit
communities with a clustering algorithm. Then, we
formalize the problem of collective characterizing of
communities as research of MCS (Minimal
Covering Shortcuts) are shortcuts between Super-
Peers, minimum covers all communities.
Indeed, communities are built automatically
using the clustering algorithm ECCLAT (Durand
and Cremilleux, 2002). This is done by analyzing
the queries answered by each super-peer. Each
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community is represented by a set of super-peers
(cluster of super-peers), with the constraint that a
super-peer can belong to more than one cluster. In
this situation, the entire cluster is a hypergraph,
where each node is a super-peer. The question then
is how to exploit such a hypergraph to define
strategies for minimal query routing.
We define an MCS as a set of super-peers
covering all communities (a collection of super-
peers containing a super-peer for each community).
The naive method is to take a super-peer of each
community and define MCS as the union of all its
super-peers. Therefore, each MCS will have as
cardinality the number of communities in the
network. As communities overlap, we will search
MCS sets with minimum size respecting the
coverage constraint. Thus, we consider that MCS is
a semantic context, which can be useful for routing
queries.
Definition (Minimal Covering Shortcut - MCS):
Given  set of domains,  set of communities in
the network (  2 ), X  2, X says MCS if it
satisfies the constraints of coverage and the
following minimalist: (1) coverage :  Y  , Y 
X   et (2) minimal :  Y  X,  Z  ,: Z  Y =
.
Note that T

2, set of calculated MCS from
all communities  of network P2P and we say that
MCS is associated with a community if it contains at
least one member of this community.
For any given community, there is at least one
MCS associated with it:

c

,  T

T: T

c


;
Indeed, suppose the contrary: there is a community c
such that for all MCS T we have T

c =

. This
means that there is community c which has no
representative in T. This is a contradiction with the
property T that coverage must cover all communities
(it does not cover c).
However, it is possible that a SP (field) is not in
any MCS.
4.1 Architecture of MCS-Super-Peer
(MCS-SP)
In this section, we present the architecture of a
MCS-SP (Ismail et al., 2010). The general
architecture of a MCS-SP is described in Figure 3
each MCS-SP contains the following components:
• Query Manager: The role of this component is to
rewrite and route queries to the (Super-)Peers. It also
defines the implementation plans and optimizes
front to supervise their implementation across the
network.
• Communication module: As for the Peer
communication, it is provided by Sun's JXTA
[JXTA. www.jxta.org]
• Table of MCS: Contains a list of associated
community ties.
• k-Filter: Allows you to find k cross ties among
community to treat a given query.
Figure 3: Architecture d’un MCS-SP.
4.2 Calculate of Communities MCS
Clusters (Figure 4) are used to model the P2P
network as hypergraph. Each vertex is a SP. A
hyperedge corresponds to a cluster. Figure 4 shows
an example of the hypergraph. Then we use the
minimum traverse of the hypergraph to link all
vertices (SP) and thus forming routes for routing
queries. The calculation of MCS belongs to the
calculation of minimum traverses.
The results of all minimum traversals calculated
with MTMINER (Hebert, 2007) are:
Minimum traversals = {{SP
1
, SP
2
, SP
6
}, {SP
1
,
SP
6
, SP
8
}, {SP
1
, SP
6
, SP
9
}, {SP
2
, SP
3
, SP
6
}, {SP
2
,
SP
4
, SP
5
}, {SP
2
, SP
5
, SP
6
}, {SP
2
, SP
5
, SP
7
}, {SP
3
,
SP
6
, SP
8
}, {SP
4
, SP
5
, SP
8
}, {SP
5
, SP
6
, SP
8
}, {SP
5
,
SP
7
, SP
8
}, {SP
1
, SP
2
, SP
4
, SP
10
},{SP
3
, SP
7
, SP
8
,
SP
10
}, {SP
1
, SP
2
, SP
7
, SP
10
}, {SP
1
, SP
4
, SP
5
, SP
9
},
{SP
1
, SP
4
, SP
8
, SP
10
}, {SP
1
, SP
4
, SP
9
, SP
10
}, {SP
1
,
SP
5
, SP
7
, SP
9
}, {SP
1
, SP
7
, SP
8
, SP
10
}, {SP
1
, SP
7
,
SP
9
, SP
10
}, {SP
2
, SP
3
, SP
4
, SP
10
}, {SP
2
, SP
3
, SP
7
,
SP
10
}, {SP
3
, SP
4
, SP
8
, SP
10
}}
This method, searching for communities and
calculation of MCS, remains centralized, performed
by a central server. Once the calculation of the MCS
is made, the central server is responsible for sending
for each community the traverses associated with it.
These are stored in the MCS-SP.
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4.3 Query Routing in the MCS
Architecture
Once the communities (clusters) are determined and
calculated MCS, we will use them to route queries to
a set of super-peers and avoid the spreading to the
whole network. We develop what we called routing
strategies based on MCS. Thus, when a query Q is
sent by the peer P to the super-peer SP, this SP will
use the MCS associated with the community to
identify super-peers to which the query Q will be
sent. Several strategies are possible to achieve this
goal:
• 0-Filter: there is no MCS passed by a SP.
• 1-Filter: This strategy consist of selecting one of
MCS, noted T, which is associated with the
community c and containing SP, then send the
query Q to all super-peer of T except SP.
• k-Filter: This is the generalization of strategy 1-
Filter, since we consider k MCS associated with
the community c instead of one. Then, the query
Q is sent to each k MCS the strategy according to
1-Filter. The union of all results returned by each
MCS is the final result of k-Filter.
• *-Filter: we use here all MCS associated with the
community c.
The following algorithm uses only k MCS
(strategy) associated with community c to answer
the query Q sent by peer P which the super-peer
belongs c (algorithm 2):
Algorithm 2: k-Filter Calculate the Responses of Q
using the MCS
1 :
2 :
3 :
4 :
5 :
6 :
7 :
8 :
9 :
10 :
11 :
12 :
13 :
14 :
15 :
Input: query Q
Output : RQ, AQ
SP_Q : super-peer that receive the query Q
SP_MCS : list of super-peers composing the MCS
including SP_Q (Can treat the query Q)
C_SP : community of SP_Q
SP_MCS = MCS(SP_Q) // MCS including SP
IF empty(SP_MCS) Then
C_SP = Com(SP_Q)// Search for the community
of SP_Q
SP_MCS = MCS(C_SP, k) // return the k MCS
associated to the community of SP_Q
EndIF
AQ = {}, RQ = {}
For X

SP_MCS Do
For SP

X Do
Send(Q, SP) // and other KSPi
Local_Search(Q, SP) // Search for peers of
// SP can treat Q: Local Search
IF ((

Son(Pk, SP))  (CAP(Pk)>)) Then
// Pk can treat Q
AQ =

{SP@Pk}
Algorithm 2: k-Filter Calculate the Responses of Q
using the MCS (Cont.)
15 :
16 :
17 :
18 :
19 :
RQ =

Treat(Q, Pk) // Pk treat the
// query Q et return the response RQ
EndIF
EndFor // End treatment for MCS
EndFor // End treatment for K MCS
Return AQ, RQ.
The function (MCS C_SP, k) returns the k MCS
associated with the community C and are candidates
for processing the query Q of super-peer SP. This
choice may be more or less complex. For that we
order the MCS of a community according to their
size (number of super-peers that composed its), then
take the first k. The algorithms 1-Filter and *-Filter
can be reduced to the k-filter algorithm respectively
using a single MCS or all MCS of the community c.
The algorithm 2 selects only one Strategy, set of
super-peers, and sends the queries, considering only
its super-peers (belongs to minimal traversal), then
any super-peer in the community, in order to use the
CAP to choose relevant(s) pair(s) to respond to a
given query, taking into account their ability to
respond to the query Q.
Figure 4: Routing queries using multiple sleepers.
Example (1-Filer): In this example, we use a
single strategy {SP
1
, SP
2
, SP
6
}. A query Q is sent by
the super-peer SP
8
, which in its turn, sends it to the
super-peer SP
2
belongs to the traverse {SP
1
, SP
2
,
SP
6
}, then to all super-peers {SP
1
, SP
6
} belonging to
the same traverse and therefore the query arrived to
all communities of the P2P network. And
consequently, the query is sent to all super-peers in a
community (see Figure 4).
Example (3-Filter): In this example, we use
three traverses for the super-peer SP
1
by example
{{SP
1
, SP
2
, SP
6
}, {SP
1
, SP
6
, SP
8
}, {SP
1
, SP
6
, SP
9
}}.
A query Q is sent by the super-peer SP
1
, which in its
turn, sends it to all super-peers {SP
2
, SP
6
, SP
8
, SP
9
}
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(see Figure 4), because in this super-peer SP
1
, we
have all the traverses that passed by this super-peer
(such as routing table for queries.)
5 EXPERIMENTS
AND EVALUATIONS
We describe the performance evaluation of our
routing algorithm with a SimJava-based simulator.
In our experimental study we compared the
performance of our proposed system (Traverse) with
the SenPeer (Faye et al., 2007).
In our experiments, we have accomplished
various simulations respecting the following
protocol:
• For each class of architectures defined by the
number of peers NP and super-peers NSP, we
generate the set of domains D and the query Q. As
peer generates a query by using its schema, the
number of queries is equal to the number of peers.
• It generates peers, super peers and schema
• The above parameters are then used to simulate the
Baseline architecture and the Community
architecture MCS.
This allows us to compare architectures using the
same semantics on the same network and the same
requests. We systematically measured, for each
architecture, the average time to answer queries, the
precision and recall. As our objective is to study the
contribution of communities in the network SON,
then we compared with the approach Baseline (Faye
et al., 2007). For each pair of architecture, we
measured the average response time (Figure 5) and
accuracy (Figure 6) and recall (Figure 7). These
simulations show a significant improvement in the
results of our community-based approach compared
to the Baseline approach.
The Figure 6 compares the accuracy of the
community method over Baseline method. The
Baseline method is based on the notion of capacity
of a peer to answer a query. This capacity is based
on the expertise of the peer and the query. Indeed,
we consider that a peer has the capacity to process a
query if the number of components of the query
corresponding to the expertise elements of a peer. In
this example, we consider a peer has the capacity to
process a query if the expertise is at least two
components of the query. Therefore it is possible
that a peer with this method can be classified as
having the ability to process the query without being
able to really treat. This is a problem in the Baseline
approach.
The Figure 6 shows, for a network of size less than
5000 peers, the accuracy of the MCS architecture
(97%) compared to the architecture Baseline (87%).
We clearly see the difference between the
architecture MCS and the Baseline
architecture.Finally, a recall of the Baseline
approach is the lowest compared to recall of
Community method because it is based primarily on
research involving friends of super-peer that
received the query. However, we can find other
peers can process a given query without being
friends with the super-peer that has received this
query. The search space is reduced to the Baseline,
while the extension of this space in the case of
community-based approach allows them to increase
their recall.
Figure 5: Time Evaluation: Baseline-MCS.
65.00%
70.00%
75.00%
80.00%
85.00%
90.00%
95.00%
100.00%
600 1200 1800 2400 3000 3600 4200 4800 5000
Total number of peers in the network
Precision (Pairs)
Baseline MCS
Nombre total de pa irs dans le ré seau
Figure 6: Precision Baseline – MCS.
70.00%
75.00%
80.00%
85.00%
90.00%
95.00%
100.00%
600 1200 1800 2400 3000 3600 4200 4800 5000
Total number of peers in the network
Rappel (Pairs)
Baseline MCS
Nombre total de pairs dans le réseau
Figure 7: Recall Baseline – MCS.
Recall increases with the size of the network and
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reaches a percentage about 98% in the MCS
architecture, 97% in the hybrid architecture (CK-
Baseline) and 95% in the Baseline architecture.
We compared the simulation results of the tow
approaches while showing their sensitivity to some
adjustable parameters. It should be noted that these
results are also influenced by the mapping between
the super-peers. Indeed, this mapping is more
important (dense) more queries are received by a
super-peer are broadcasted to a larger number of
super-peers, which automatically increases the time
required to answer a query.
6 CONCLUSIONS
In this paper, we explored the contribution of
grouping domain within the SON. We were placed
in a specific context where each Peer has its own
schema and is related to a SP. As a Background, a
SP topology as a suitable topology for schema-based
P2P networks was discussed, and how additional
clustering in such network can be used for query
routing among peer communities. We proposed an
advanced method using hypergraph-based algorithm
with minimum traversal to route a given query. The
advantage of this model is the robustness in Queries
routing and scalability issues in P2P Network One
important area for improvement is performance.
An important problem remains unresolved for
communities approach proposed which is the
influence of the dynamics of groups and their
characterizations on system performance. Indeed, it
is possible to introduce mechanisms that allow a
community to exclude a member (inactive Super-
Peer) or to accept a new one (newcomers or those
whose interest has evolved).
ACKNOWLEDGEMENTS
This work has been done as a part of the following
projects: "Automatic information extraction form
Arabic texts" supported by the CNRSL, "Extraction
automatique d’information à partir des documents
Arabes", supported by the UL, and "Arabic speech
synthesis from text, with natural prosody, using
linguistic and semantic analysis" supported buy the
PCSL.
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