BSI: BLOOM FILTER-BASED SEMANTIC INDEXING FOR UNSTRUCTURED P2P NETWORKS

International Journal of Peer to Peer Networks (IJP2P) Vol.6, No.1, February 2015
DOI : 10.5121/ijp2p.2015.6102 11
BSI: BLOOM FILTER-BASED SEMANTIC INDEXING
FOR UNSTRUCTURED P2P NETWORKS
Rasanjalee Dissanayaka1
, Sushil K Prasad2
, Shamkant B. Navathe3
and Vikram
Goyal4
1
Department of Computer Science, College of Saint Benedict and Saint John's University,
Collegeville, Minnesota USA
2
Department of Computer Science, Georgia State University, Atlanta, Georgia USA
3
College of Computing, Georgia Institute of Technology, Atlanta, Georgia USA
4
IIIT-Delhi, New Delhi, India
ABSTRACT
Resource management and search is very important yet challenging in large-scale distributed systems like
P2Pnetworks. Most existing P2P systems rely on indexing to efficiently route queries over the network.
However, searches based on such indices face two key issues. First, majority of existing search schemes
often rely on simply keyword based indices that can only support exact string based matches without taking
into account the meaning of words. Second it is difficult, if not impossible, to devise query based indexing
schemes that can represent all possible concept combinations without resulting in exponential index sizes.
To address these problems, we present BSI, a novel P2P indexing and query routing strategy to support
semantic based content searches. The BSI indexing structure captures the semantic content of documents
using a reference ontology. Our indexing scheme can efficiently handle multi-concept queries by
maintaining summary level information for each individual concept and concept combinations using a
novel space-efficient Two-level Semantic Bloom Filter(TSBF) data structure. By using TSBFs to represent
a large document and query base, BSI significantly reduces the communication cost and storage cost of
indices. Furthermore, We devise a low-overhead mechanism to allow peers to dynamically estimate the
relevance strength of a peer for multi-concept queries with high accuracy solely based on TSBFs. We also
propose a routing index compression mechanism to observe peers’ dynamic storage limitations with
minimal loss of information by exploiting a reference ontology structure. Based on the proposed index
structure, we design a novel query routing algorithm that exploits semantic based information to route
queries to semantically relevant peers. Performance evaluation demonstrates that our proposed approach
can improve the search recall of unstructured P2P systems up to 383.71% while keeping the
communication cost at a low level compared to state-of-art search mechanism OSQR [7].
KEYWORDS
Unstructured P2P Networks, P2P Search, Query Routing, Bloom Filters, Semantic Search
1. INTRODUCTION
P2P systems have become a popular means of sharing large amounts of data among users over
recent years. They are considered an attractive solution for applications requiring high scalability,
robustness and autonomy. Efficient resource discovery in basic unstructured P2P systems,
however, suffers from high costs mainly due to lack of global knowledge. To make matters
worse, with the advent of semantic web, more and more users associate their shared resources

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with semantic meta-information. This mandates that the P2P networks provide methods that allow
a user not only to describe resources from a semantic viewpoint but also allow users to run more
complex queries addressing several semantic properties or relationships among semantic entities
and resources. Majority of search algorithms proposed for P2P networks to date, however, are
simple keyword searches or title based searches. These are inadequate for formulating semantic
based queries and consequently, do not provide semantically relevant results.
Index engineering is at the heart of P2P search methods. P2P indices can be broadly categorized
to local, central and distributed indices. In a local index, a peer only keeps references to its own
data. In a central index scheme such as one employed by Napster [1], a single server peer
maintains data about all the other peers in the network in its index. Most widely used scheme is
the distributed index scheme where peers maintain pointers towards targets in the network.
Current P2P indexing schemes face three main problems. First, overwhelming majority of
indexing schemes proposed to date are simple keyword based indices. They do not associate
semantics(i.e. meaning) to keywords in index resulting in poor search accuracy. Second, the
handful of semantic based indexing schemes [11], [13] are either computationally intensive [13]
or index size varies largely with the dimensionality of the content [13]. Third, maintaining query
based indices (as opposed to simple keyword based indices)in a space efficient manner is
challenging in P2P networks. Maintaining minimum routing state per peer is a crucial component
in P2P searching. However, maintaining small size query based routing indices while not
compromising the quality of information they hold is a challenging problem and has not been
addressed sufficiently by the current body of work.
Maintaining query based indices in P2P networks are attractive for two reasons: First, such an
index allows a peer to maintain more information about its neighborhood by allowing it to assign
a relevance strength to a neighbor per query. Second, as indicate by many web search logs [19]
majority of searches are multi-term(keyword or concept) queries that could benefit from query
based indices to achieve better performance. Many of the proposed work, however, are optimized
for single keyword queries. Handling multi-keyword queries is rather inefficient using single
keyword indices. On the other hand, maintaining multi-term query based indices for better
performance is simply impractical due to the resulting exponential size of indices. Little to no
explicit measures have been taken to ensure high quality of indices regardless of the query length
while maintaining small index sizes.
To address the aforementioned issues, in this paper we propose BSI, a semantics based indexing
framework, which aims to improve the quality and efficiency of search in P2P networks by using
a shared ontology as a reference for building index. Our indexing framework is designed for
unstructured P2P systems where network imposes no structure on the overlay network or data
placement. Our work addresses several important issues raised by current indexing schemes:
First, our semantic based index framework takes into account the meaning of words thereby
allowing peers to evaluate multi-concept queries. A reference ontology concepts serve as the
index terms and relevance strengths for different queries (concept combinations)are maintained in
the index. Each relevance strength is a combined measure of information content and the number
of relevant documents reachable through a given peer and its neighborhood. Second, we maintain
attractive small size index while maintaining the quality of index using Bloom filters. The size of
the routing index is limited by the number of concepts in the ontology and can be easily
compressed to accommodate space restrictions by utilizing hierarchical ancestor-descendant
relations in semantic ontology. Finally, we explicitly capture the notion of multi-concept query
based indexing in our index structure by allowing peers to maintain relevance strength for
different queries for each neighbor. To maintain large volume of multi-concept queries and their

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corresponding relevance strength at a peer in a space efficient manner we introduce a novel
Bloom filter based data structure called Two-level Semantic Bloom Filters(TSBF). TSBF is an
extension to the traditional Bloom filter which incorporates the notions of ontology based meta
data and relevance strengths of queries. TSBF allows us to limit the size of a routing index while
maintaining large amount of summarized information regarding peers’ strengths for possible
queries to make a informed decision in routing multi-concept queries. Furthermore, We devise a
low-overhead mechanism to allow peers to dynamically estimate the relevance strength of a
neighbor for multi-term queries with high accuracy using TSBFs. We also propose a index
compression mechanism to observe dynamic storage limitations of peers with minimal loss of
information by exploiting the hierarchical relationships in the reference ontology structure.
Finally, based on the proposed indexing scheme, we design a novel query routing algorithm that
exploits semantic based information with different granularity to route queries to semantically
relevant peers.
In BSI, we do not employ computationally expensive methods like LSI. Rather, we use concepts
in a reference ontology to build peer local and routing indices to aid in the query routing process.
The process of constructing peer indices is much simpler and consumes less memory and time in
constructing them. Among many popular semantic indexing mechanisms OSQR [7], OLI [18],
Ontsum [11] The related work most comparable to ours is OSQR [7], a semantic search protocol
that maintains total relevant documents reachable through a neighbor for each concept from a
reference ontology in its routing index as routing state. The authors propose a mechanism to
calculate a conservative estimate of strength of a peer for a multi-concept query based on the
number of documents reachable from that peer for each individual query concepts. The
disadvantage of OSQR however is that its search cost is considerably high due its knowledge
dissemination mechanism which utilize search messages for piggybacking data.
Overall, many existing work for semantic P2P search lacks the ability to efficiently answer multi-
term queries with greater accuracy mainly due to lack of quality and quantity of routing state. BSI
gracefully handles multi-term queries by exploiting both the concepts in query as well as
structural relations of the query concepts in ontology to intelligently route a query to the most
promising peer. Our solution is scalable as the index structures can easily be compressed to
accommodate dynamic storage restrictions. Performance evaluation demonstrates that our
proposed approach can improve the search efficiency of unstructured P2P systems while keeping
the communication cost at a significantly lower level compared with state-of-art unstructured P2P
systems. The rest of the paper is organized as follows: We present the related work in Section 2 .
The preliminary concepts used in the paper are presented in Section 3. The P2P system
architecture including routing index construction and maintenance and query routing are given in
Section 4. In Section 5, we evaluate the proposed algorithms via simulation. Finally we conclude
and summarize our work in Section 6.
2. RELATED WORK
Many search mechanisms for P2P networks have been proposed in the literature over the past few
years. While some notable blind search mechanisms proposed for unstructured networks include
flooding [2] and random walk [12], popular informed search methods include Routing Indices
[21] and intelligent search [10]. However, these methods are intended for simple keyword
searches only.
Semantic Overlay Networks (SON) [6] is one of the earliest works in incorporating semantic
aspects into P2P file sharing applications. Here, authors propose a peer clustering approach based
on semantic content a peer shares. This clustering methodology is completely distributed and also

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supports self-organization. However, in SON, peer joining process requires flooding the network
to request classification hierarchy and also for finding semantically related peers. P2P systems
such as Edutella [15] and InfoQuilt [3] use flooding as a basis for semantic search. Edutella
proposes an RDF metadata model and is a super-peer based mechanism. However, the notion of
super-peers makes the solution less attractive as super-peers need more resources than their sub-
peers to maintain all routing indices as well as RDF triples representing sub-peers. This also leads
to a substantial overhead in index updating at super-peers when a node joins, leaves, or changes
its content. Compared to this, our approach is much simpler and involves minimal overhead in
terms of local knowledge maintenance within peers. There is no notion of super peers and each
peer only keeps a simple semantic based Bloom filter (TSBF) per its neighbor. pSearch [20] uses
a semantic vector to describe and find the resources in the P2P network. SemreX [9] uses a
concept tree to construct a semantic overlay network on top of P2P overlay. Both pSearch and
SemreX use Latent Semantic Indexing (LSI) [5] to map documents to semantic vectors. LSI
however is computationally expensive, and when the network content changes frequently, this
method needs to be applied often. In BSI, we do not employ computationally expensive methods
like LSI. Rather, we use concepts in a reference ontology to build peer local and routing indices
to aid in the query routing process. The process of constructing peer indices is much simpler and
consumes less memory and time in constructing them. OntIndex [13] is a ontology based
indexing mechanism where authors propose a semantic based search algorithm for unstructured
P2P systems. However, the notion of semantic information content is not taken into account in
[13] resulting in low search performance. Also, index generation and updating in OntIndex is
resource consuming and generates considerable traffic. OSQR [7] is a ontology based semantic
search protocol which provides a rich semantic knowledge representation of peers by combining
the both total documents reachable from a peer and the semantic richness of the peer based on the
information content of the reachable documents. OSQR, however, heavily relies on knowledge
dissemination mechanism which utilize search messages for piggybacking data contributing to
increased search cost. OLI [18] is yet another ontology based indexing method for structured P2P
systems. Semantic search methods applied in structured networks such as in OLI however,
generally suffer from the shortcoming of structured overlays such as strict enforcement of data
placement and high maintenance cost of peers joining, and leaving as well as the cost of content
updating. To avoid these problems, we consider unstructured P2P overlay for our work. Ontsum
[11] is another ontology based indexing scheme where heterogeneous ontologies between peers
are assumed. This approach however requires computationally expensive ontology generation and
ontology mapping techniques to be employed in a distributed setting and allow only RDQL based
queries which requires some level of end user expertise to be used.
3. PRELIMINARIES
3.1. Network Topology
We consider an unstructured P2P network with a large set of peers {P1,P2,..., PP}. Each peer in the
network can only communicate with its direct neighbors in one hop distance. A peer is assumed
to have on the average γ number of neighboring peers. Peers may leave and join the network at
any time. Each peer has a local text document database that can be accessed through a local
index. The peer uses its local index to evaluate all the content queries and returns pointers to the
documents having the queried content.

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3.2. Semantic Ontology
We assume the presence of a global reference semantic ontology which is known and agreed
upon by every peer in the network. The ontology is a set of m semantic concepts C={c1,c2,..., cm}
which are related to each other via IS-A relationship (i.e., hypernym/ hyponym). We denote all
the leaf concepts in the ontology by Cl .The root concept of the ontology is denoted by Cr.
3.3. Data Distribution
As stated earlier, each peer has a set of text documents. Each document is represented as a vector
of concepts from Cl using a Vector Space Model, a standard technique for representing document
contents in the area of information retrieval. Each document vector consists of a vector of concept
relevancy scores. To construct a document vector, the document is subjected to a process of text
mining followed by word sense disambiguation to identify those concepts that exist in the
document along with their concept frequencies(i.e. number of occurrences of a concept in the
document).Then the concept frequencies of each concept are normalized to a [0-1] range by
applying maximum frequency normalization by dividing them by the maximum concept
frequency for that concept encountered by the peer in its local document collection.
3.4. Semantic Query
A query consists of the conjunction or disjunction of one or more concepts from the leaf concepts
(Cl) of the ontology. Existing techniques [4], [5] proposed by the research community can be
employed to convert keyword queries to concept queries. A query returns a set of k documents
having its relevancy above some user defined threshold value. The relevancy of a document for a
given query is computed using concept vector similarity with respect to the query concepts. We
use the well known cosine similarity measure to calculate the similarity between a query Q and
document vector D:
݈ܵ݅݉݅ܽ‫ݕݐ݅ݎ‬ሺ‫,ܦ‬ ܳሻ =
‫.ܦ‬ ܳ
‖‫.‖ܦ‬ ‖ܳ‖ (1)
A document is said to qualify as an answer for a given query if the cosine similarity between its
document vector and query exceeds a predefined relevance threshold.
3.5. Bloom Filters
A Bloom Filter (BF) is a data structure suitable for performing set membership queries very
efficiently. A Standard Bloom Filter representing a set S={s1,s2,..., sn} of n elements is generated
by an array of m bits and uses k independent hash functions h1,h2,...,hk. They are space efficient
data structures which provide constant time lookups and no false negatives. The downsides of
BFs are that they can result in false positives and do not allow item deletion. However, based on
the application requirement the false positive rate can be significantly lowered. Therefore care
must be taken in choosing k and m so that the false positive rate is acceptable. It has been shown
that the probability of a false positive Pfalse can be given by the following equation
݂݈ܲܽ‫݁ݏ‬ = ሺ1 − ݁−݇݊/݉
ሻ݇
(2)

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There are many variants of standard BF such as Spectral BF, counting BF, compressed BF, etc. In
our work we utilize both standard Bloom filters and Spectral Bloom Filters (SBF). SBFs is an
extension of the traditional BF for multi-sets allowing filtering of elements whose multiplicities
are below a threshold. SBFs allow querying on item multiplicities as well as deletion. SBFs
maintain a counter per bit in the bit array to keep track of the number of times the bit is set.
Simply taking the minimum count over all bits set for a given element will produce the
multiplicity of that element in the represented multi-set.
Figure 1. Ontology with seven concepts and IS-A relations between them.
(a) (b)
Figure 2. (a) A traditional Bloom filter with three hash functions. (b) A Spectral Bloom filter with three
hash functions.
4. SYSTEM DESIGN
Three key issues in current indexing strategies are (i) their inability to associate semantic of
represented content , (ii)low efficiency in routing multi-term queries and (ii)large index sizes. In
order to overcome the shortcomings of existing indexing strategies and semantic query evaluation
techniques, we put forward BSI(Bloom filter-based Semantic Indexing). We begin with the
design of the Two-level semantic Bloom Filter (TSBF), a space efficient data structure that
represents probabilistic information regarding contents a peer share with the network. We then
describe our routing index structure used for efficient query routing.
4.1. Two-level Semantic Bloom Filter (TSBF)
Bloom filters serve as appropriate index structures for resource discovery due to their scalability,
and low cost storage and distribution. However, they do not support semantic based multi-term
queries as they have no means of representing probabilistic information regarding ontological
data. To this end, we introduce a novel Bloom filter based data structure (TSBF) that aim at
supporting efficient conjunctive (and disjunctive) semantic query routing in unstructured P2P
networks.

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TSBF is an extension of the traditional Bloom Filter to encode probabilistic information regarding
richness of a peer for different queries based on its local documents with the same false positive
probability as the traditional Bloom filters. In particular, it encodes goodness of a peer for
different multi-concept queries in terms of its distance to documents, the size of the document
collection and information content of these documents. Posed with the query "Is Query Q a
member of TSBFP ?" the TSBFP of peer P returns a relevance strength of P for Q based on
information encoded in it. In our work, we define this relevance strength of a peer to be the total
number of local documents in P for Q.
To implement this functionality TSBF is designed as a two-level Bloom filter where level 1
(TSBF1,P ) represents the set of qualified documents in P and level 2 represents the set of queries
answerable by P. In particular, for level 1 there exist multiple bit arrays each representing the
local documents of P rich in C (TSBF1,P (C)). These bit arrays are similar to those employed in
traditional Bloom filters and is supported by a sufficiently large body of research work [14], [16],
[17] that allows us to estimate number of documents reachable for a multi-concept query solely
based on these bit arrays.
Similar to level 1, level 2(TSBF2,P) also contains multiple bit arrays each representing different
multi-concept queries that whose concepts have C as the least common ancestor in the ontology
hierarchy for which P has at least one qualified document in its local document collection
(TSBF2,P (C)). To enable associating the number of relevant documents reachable through P for
each query Q we implement each bit array in level 2 as Spectral Bloom Filters(SBFs). SBFs
maintains a counter per each bit in a bit array thereby allowing deriving the number of times an
element is added to bit array. In a level 2 bit array elements are queries and a given query Q is
added as many times as the number of qualified documents exist in P for Q.
Intuitively, while level 1 contain a bit array per ontology concept, level 2 only needs to maintain a
bit array per non-leaf ontology concept. Posed with a query Q, TSBF2,P returns the actual number
of documents in P qualifies as answers for Q whereas the TSBF1,P provides an estimate of the
same. While both provide two alternative ways for estimating the number of documents in P for
Q, TSBF2,P is proffered over the other as it provides exact information regarding P’s strength for a
given query. When Q does not exist as a member in TSBF2,P then TSBF1,P is used to derive an
estimate.
In our implementation, we size the set of bit arrays in both levels in a TSBF the same , but their
sizes can be adjusted independently. Similar to traditional Bloom filters, TSBF uses a fixed
number of hash functions h1,h2,...,hk. Figure 3 shows a TSBF of a peer P for the ontology given in
Figure 1. In the figure, the TSBF is a set of 10 bit arrays, 3 of which is for level 2, each
representing possible queries per nn-leaf concept in the ontology while the other 7 is for level 1,
where each bit array represents documents reachable for the given concept.

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Figure 3. The TSBF for a peer P with documents d1, d2, d3, d4, d5, d6. for ontology in Figure 1.
Such a TSBF of a peer serves two purposes: it allows fast query processing within the peer and
also serve as the foundation for constructing peer’s routing index. Use of Spectral Bloom filters in
place of traditional Bloom filters allows associating a frequency of occurrence to each individual
document/concept combination which adds to the information quality of Routing indices.
1) TSBF Construction: The TSBF of a peer P, TSBFP is constructed at start-up when peer joins
the network for the first time. Initially all the bits of level 1 bit arrays are initialized to false while
the all the bits in level 2 are initialized with a counter value zero. To populate level 1 bit arrays
peer can analyze its document vectors to identify those qualifying documents per ontology
concept and insert into the bit array responsible for the concept to populate them. In other words,
P insert all its local documents that can answer concept C in its TSBF1,P (C) bit array.
Following the same process for populating level 2 bit arrays, however, leads to practical issues.
We could ideally generate all possible concept combinations (queries)that exceeds a predefined
cosine similarity threshold per local document of P and insert them in the appropriate bit arrays in
level 2. Note that, same concept combination can be answered successfully by multiple
documents thus allowing the combination to be inserted into the relevant bit array that many
times. For instance, a concept combination answerable by a document d in P is inserted into
TSBF2,P (C) where C is the least common ancestor of concepts in combination based on the
reference ontology. This method of level 2 TSBF population is certainly a viable option for those
peers with sufficiently large amount of storage at hand as the number of concept combination that
can be answerable by a peers document collection could be quite large. Such a technique is often
unfavorable, however, as this might lead to high false positive rates due to insertion of large
number of elements to bit arrays. Moreover, there is no need to generate and insert queries of all
possible lengths to a TSBF. As a query length analysis [19] on various search engines conducted
in 2011 indicate, an average length of a query contains 3.08 terms and more than 92% queries
contain only 5 terms or less. Moreover, there may be concept combinations that end user is
simply not interested in as queries and the number of such concept combinations could be
unnecessarily large. Therefore we propose that a peers dynamically build TSBF level 2 based on
the queries it receives by adding only those queries that it can answer using its documents or
through neighborhood. This will significantly lower the useless concept combinations in a TSBF
level 2 thus controlling the false positive rate from going up. A peer also has the capability of
aggressively building its TSBF level 2 by periodically gossiping with neighbors to exchange their
query histories to discover new concept combinations that should be in its TSBF.

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4.2. Routing Index
The routing index of a peer summarizes information of its neighborhood useful for intelligent
query routing. One of the key requirements of the routing index of a peer is to represent all
possible queries answerable by a neighbor along with the corresponding relevance strengths of
the neighbor per each individual query in a compact manner. The routing index structure achieves
this by summarizing TSBF’s of neighbors at the peer. The routing index of a peer P consist of a
set of <key, value> pairs where key is a neighbor N and value is a TSBFP→N , a summarized
TSBF that represents the set of documents reachable through N and the set of queries answerable
through the N based on the set of peers in the network N can reach. TSBFP→N generally represents
an aggregation of a set of TSBFP′ structures each belonging to a peer P′ reachable through P.
4.2.1 Routing Index Updation
The drastic reduction in the overheads of routing table construction and maintenance in BSI is
achieved through use of TSBFs. At startup, peers exchange their TSBFs with each other.
Therefore the entry for neighbor N, TSBFP→N, at P’s routing index is initially a replica of TSBFN
at N representing the set of documents and queries in N’s local storage. This is later updated by
information carried by N to P from different query routes to represent documents and queries
reachable through N. Particularly, presented with piggybacked information carried in a query
path, an update at P aggregates TSBFPi of each peer Pi visited by the query received through N, by
taking the union equivalent of them subjecting bit counts to exponential decay depending on the
hop distance of Pi to P. The exponential decay of counts in bits during forwarding of search
messages, exponentially reduces the impact of a peer’s TSBF on another peer’s routing index
with the distance between the two.
Equation 3 gives the exponential decay based union for combining TSBFs of set of peers Nj for a
concept c. x here represents the level of the TSBF(i.e.x ∈{1,2}) and α is the decay factor. We
simply used the distance between peer Nj to P as α. Here the corresponding bits of respective bit
arrays are aggregated using a function F. F is simply bitwise OR between bit i of corresponding
bit arrays in all TSBFs in the query path for level 1 TSBFs and is sum for the same for level 2
TSBFs bit arrays. To avoid information becoming stale, periodic updates are triggered by peers
updating Routing index for each neighbor based on the messages it received. Even though the
updates are triggered periodically, the updates occur only if a peer sees a substantial difference in
the values in the bits in bit arrays it is interested in.
ܶܵ‫ܨܤ‬‫ݔ݁,ݔ‬‫݊݋ܷ݅݊݌‬ ሺܿሻ. ܾ݅‫ݐ‬݅ = ‫ܨ‬݆ =1
݇
ቆ
ܶܵ‫ܨܤ‬‫ܰ.ݔ‬݆
ሺܿሻ. ܾ݅‫ݐ‬݅
ߙ
ቇ (3)
The above design achieves our primary objective of efficiently maintaining probabilistic
information about the content stored in the neighborhood. Algorithm 1 summarizes the routing
index creation and updating procedure.
4.2.2 Routing Index Compression
To observe dynamically changing space restrictions, a peer can simply compress routing index at
will by exploiting ontology IS-A structure. The main intuition here is that, due to IS-A relations,
the ancestor concepts in an ontology hierarchy is fully qualified to represent a descendant
concept. Therefore the TSBFP→N of a neighbor N in P’s routing index can be compressed even
further by combining the bit arrays for two or more concepts sharing a IS-A relation by applying
union equivalent of represented sets to corresponding bit arrays. for example, corresponding level

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bit arrays of B and D in ontology in Figure 1 can be combined to generate one bit array
represented at D this way.
This union equivalent operation for a set of such TSBFs for a neighbor N at P representing a
combining set of concepts sharing IS-A relations is given in Equation 4. The level x is 1 or 2 and
Ca is the ancestor concept of all descendant concepts cj being aggregated. Similar to Equation 3, F
refers to bitwise or and summation of counts for level 1 and level 2 respectively.
ܶܵ‫ܨܤ‬‫ݔ‬,‫݊݋݅݊ݑ‬ ሺܿܽ ሻ. ܾ݅‫ݐ‬݅ = ‫ܨ‬݆ =1
݇
൫ܶܵ‫ܨܤ‬‫ݔ‬,ܲ→ܰሺ݆ܿ ൯. ܾ݅‫ݐ‬݅ (4)
When the need arises to compress a routing index, the priority first goes to combining bit arrays
for different concepts at level 2 (i.e. TSBF2,P) as compressing this does not result in minimal loss
of information. The reason for this is, TSBF2,P represent exact information regarding frequency of
concept combinations. The second priority is given to combining level 1 TSBF1,P bit arrays. Since
this is used for estimation purposes, combining multiple bit arrays for different concept into one
will degrade the estimation accuracy. Similarly, when choosing which bit arrays to combine in a
given level, the higher probability is given for those bit arrays representing higher level concepts
closer to the root as the information represented by the ontology generalizes towards ontology
root.
Combining multiple bit arrays into one, however, results in slightly increased false positive rate
and can be controlled by either not having excessively large number of items in bit arrays or by
allowing bit arrays to grow as needed to maintain the desired false positive rate.
Algorithm 1 Routing Index Construction and Maintenance
Input:
Msg = Message
N = Neighbor sending Msg
TSBFList = The list of TSBFs in Msg, list index+1 denote distance between P and peer to
which TSBF at index belong
P = Message receiver peer executing algorithm
C = set of concepts in reference ontology
Output: ‘
selected = the top k
1:
∇ Create index entry for N for alive-ping message
2: if Msg.TYPE = ALIVE then
3: TSBFN ← TSBFList.get(0)
4: for each Concept c ∈ C do
5: for i = 0 to TSBFN(c).length - 1 do
6: index:TSBF1,P→N(c).biti ← TSBF1,N (c).biti
7: index:TSBF2,P→N(c).biti ← TSBF2,N (c).biti
8: end for
9: end for
10: end if
11: ∇ Remove index entry for N for leave-ping message
12: if Msg.TYPE = LEAVE then
13: Index.remove(N)
14: end if
15: ∇ Update index entry of N for information received through a search related message

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16: if Msg.Type = QUERY or HIT or MISS then
17: ∇ Determine concepts for which Routing index should be updated
18: Query q ← Msg.query
19: lca(q) ← least common ancestor concept of q
20: Cupdate ← empty set representing concepts for which index.TSBFP (N) should be updated
21: Cupdate.add(lca(q))
22: Cupdate.addAll(q)
23: ∇ Update Routing index entry for N
24: for each Concept c ∈ Cupdate do
25: ∇ Calculate a new TSBF entry for N based on TSBFList
26: TSBF1,N′ (c) ← calculate using Equation 3
27: TSBF2,N′ (c) ← calculate using Equation 3
28: if TSBF1,N′ (c).biti > index.TSBF1,P→N(c).biti then
29: Index.TSBF1,P→N′(c).biti ← TSBF1,N′ (C).biti
30: end if
31: if TSBF2,N′ (c).biti > index.TSBF2,P→N(c).biti then
32: Index.TSBF2,P→N(c).biti ←TSBF2,path(C).biti
33: end if
34: end for
35: end if
4.3. Query Routing
The query routing algorithm is summarized in Algorithm 2. The objective of search in BSI is to
retrieve many relevant documents as possible for a given query. The search messages in BSI are
TTL bounded and therefore a query can travel maximum TTL hops before it is discarded. Every
query receiver peer performs a local search and append results of retrieved documents to the
query and then forwards the query to the most promising neighbor if the TTL has not expired.
Upon TTL expiration, the peer returns a response message to the query originator along with the
retrieved results. The neighbor selection process at a peer P for a query q is performed as follows:
Upon receipt of q, P checks TSBFP→N associated with each of its neighbor N. The lookup returns
the frequency of documents (exceeding a relevance threshold) in each neighbor N containing
query as a concept combination. This denotes the relative probability of success of N for the
query compared to its neighbors. To calculate this TSBF, P first lookup in level 2, TSBFP→N (c),
where c is the least common ancestor of concepts in query. If query exist as an element, TSBFP→N
returns the associated document frequency. The fact that TSBF2,P→N (c) does not contain q as a
concept combination does not necessarily mean that combination is not reachable through N. It
may be a situation arising from P not exploiting all reachable peers in its TTL hop radius or P has
not encountered q in its query history. In such a case P calculates an estimate of the reachable
documents for a multi-concept queries based on TSBF2,P→N from its routing index. This estimation
procedure is described below.
1) Estimating set intersection based cardinality from Bloom filters: We need a reliable
mechanism to find the number of documents reachable through a neighbor for a given query
solely based on the set of Bloom filters each representing documents reachable through the
neighbor for each query concept. Research community has proposed many works to estimate the
cardinality(i.e. number of elements) of an original set solely based on its Bloom filter bit array
[14], [16], [17]. For our work we used the work presented by authors of [16].

22
Given a set S and its Bloom Filter BFS with m bits out of which t are true bits, and k hash
functions [16] defines the cardinality of a set is as follows:
|ܵ| =
݈݊ ቀ1 −
‫ݐ‬
݉ቁ
݇ × ݈݊ ቀ1 −
1
݉ቁ
(5)
Here, the m denotes the length of the Bloom filter and t the number of true bits in the Bloom
filter. |S| denotes the cardinality of the set represented by the Bloom filter bit array.
For cardinality estimation of union of multiple sets S∪ = S1∪ S2∪...∪Sn (needed for OR query
routing) authors simply propose to take the bitwise OR of the representative Bloom filter bit
arrays and apply equation 5 to the resulting Bloom filter. Estimating cardinality of intersection of
sets based on bloom filters is not so straightforward. We used the set inclusion-exclusion
principle in combinatorics to derive the cardinality of intersection of sets based on the
cardinalities of the unions of sets. We employed the equation 5 to calculate the intersection of sets
S∩ = S1∩ S2∩...∩Sn:
อሩ ܵ݅
݊
݅=1
อ = ෍ሺ−1ሻ݇+1
൭ ෍ |ܵ݅ ∪ … ܵ݇|
‫ݒ‬
1≤݅<݇≤݊
൱
݊
݇=1
(6)
Each set Si in our work represent the set of documents reachable for each concept ci for a given
query through a given peer P. The equation 5 only requires cardinalities of individual sets and
unions of sets to compute the cardinality of intersection. Therefore upon a receipt of a query a
peer can simply use above mentioned Bloom filter based cardinality estimation techniques to
calculate each term in right hand side of Equation 6 thus computing the cardinality of intersection
for a neighbor for a given query.
Algorithm 2 Query Routing
Input:
Q = Query Message
P = Message receiver peer
index = The routing index of P
Candidates(Q) = Neighborhood(P) - Q.VisitedPeers
k = the walker count
Output: ‘
R = Retrieved documents
Selected(Q) = the selected peers for query forwarding
1: Selected(Q) ← φ
2: Q.TTL ← Q.TTL - 1
3: R ← LocalSearch(o)
4: ∇ Found target Object
5: if Q.TTL ≤ 0 then
6: if R ≠ φ then
7: Return a HIT with R and terminate search
8: Else
9: Return a MISS with R and terminate search
10: end if

23
11: Else
12: Clca ← least common ancestor concept of Q
13: for each Neighbor N ∈ Candidates(Q) do
14: ∇ Look up score in index.TSBF2,P→N
15: if index.TSBF2,P→N(Clca) contains Q then
16: ScoreN ← minimum count of all bits hashed by Q in index.TSBF2,P→N(Clca)
17: else ∇ Estimate score from index.TSBF1,P→N
18: QueryBFListN ← φ
19: for each Concept c ∈ Q do
20: QueryBFListN.add(index. TSBF1,P→N(c))
21: end for
22: ScoreN ← calculate using Equation 6 with QueryBFListN as input
23: end if
24: Sort Candidates(Q) based on scores
25: Selected(Q) ← Neighbor in Candidates(Q) with highest score
26: end for
27: Return Selected(Q) and R
28: end if
5. EXPERIMENTS
In this section we present a simulation-based evaluation of BSI and compare its performance with
other mechanisms for query routing in unstructured peer to peer systems.
5.1. Simulation Methodology
The parameters for the simulation TSBF used in BSI are listed in Table 1. The simulations use a
default 1024 peer unstructured network, unless noted otherwise. To simulate the dynamic
behavior of the network under peer churn, we inserted online nodes to the network while
removing active nodes at varying frequencies. During a single simulation run, the network size
was maintained at roughly the original starting size by ensuring the number of nodes that join the
network dynamically was the same as that leaving the network. On an average, 80 nodes each are
added and removed from the network during each simulation run.
We mainly compare our work against OSQR [7] and Random Walk [8]. OSQR is a concept based
indexing mechanism which tries to improve the performance for multi-concept queries with a
concept based routing index. The OSQR routing index of a peer maintains the total reachable
documents per neighbor per concept in a reference ontology as the relevance strengths of the
neighbor for each ontology concept. The authors propose taking the minimum of the number of
documents reachable for each query concepts for a given neighbor as its relevance strength to the
query. For the simulation, the maximum number of entries in OSQR index was set to 128 and size
of maxVector (a data structure used by peers for global knowledge acquisition) was set to 10.
Random walk on the other hand is a popular blind search mechanism where a querying peer
deploys a search walkers by sending query to a randomly selected neighbor. The query format as
well as local search remains the same as our proposed BSI algorithm. We experimented with two
versions of BSI based on the design of the TSBF data structure:
1) BSI(TSBF-level1+level2): This is the original BSI algorithm where the TSBF structure
contains two levels: level1 contains a list of bit arrays each representing the set of documents

24
accessible per ontology concept and level2 contains a list of bit arrays each representing a set of
queries and the associated document frequencies per ontology concept of a peer.
2) BSI(TSBF-level1): This represents a reduced level BSI algorithm where TSBFs solely consists
of level1 only. Thus the peers rely on the reachable documents estimation process in query
routing.
Table 1. Simulation Parameters.
Parameter Range and default value
Network size 28
-211
Default:210
Peer degree distribution power law
Total distributed documents 5000
Average number of concepts per document 20
Document distribution among peers zipf (α=1.0)
Query distribution among peers zipf (α=1.2)
Mean peer documents 100
Reference Ontology Concepts 128
TTL 1-11 Default:7
Relevance threshold 0.7
TSBF filter length 250bits
No. of hash functions 7
5.2. Results
The primary performance results can be summarized as follows. For a low routing overhead, the
query recall improves by up to five orders of magnitude over OSQR proving the efficiency of the
algorithm in locating as many relevant documents as possible in a low search cost (Section 5.2.1).
Simulations over different query lengths and different filter sizes demonstrate the scalability of
BSI for multi-term query routing in sections 5.2.2 and 5.2.3 respectively. On average we observed
up to four fold performance improvement in terms of recall over OSQR for various query lengths
and different filter sizes. Finally, a study of the impact of search cost on overall search efficiency
in section 5.2.4 demonstrates that the performance improvement in recall is achieved at an
attractive two-fold improvement in search traffic over OSQR.
5.2.1 Recall
Recall is a standard performance measurement in information retrieval that denotes the fraction of
relevant documents found by an average query compared to the entire set of relevant documents
distributed in the network. While many query routing mechanisms can achieve high recalls at
sufficiently high hop limits only superior query routing algorithms can achieve high recalls with
low hop limits. Varying the hop limit allows us to demonstrate the effectiveness of our algorithm
in finding larger number of documents at low TTL values thus with lower search cost. Figure 4(a)
plots the average recall per query for BSI, OSQR and Random Walk. We observed that original
BSI (i.e. (TSBF-level1+level2)) algorithm achieved an average of 383.71% increase in recall over
OSQR while BSI(TSBF-level1) gained a 322.60% increase in recall over OSQR. The
performance improvement of BSI(TSBF-level1+level2) over Random walk was observed to be an
outstanding 1238.81%. The increase in recall is more evident in higher TTL values. From the
figure we can also infer that the original BSI with both levels present in the TSBF structure
performs better than BSI with the TSBF with only level1. An average increase of 45.16%
improvement was observed in BSI (TSBF level1+level2) over BSI (TSBF-level1). This is

25
expected, as having both levels in a TSBF obviously provide more information regarding query
relevance strength of a peer to make a intelligent query routing decision compared to a TSBF
level 1 which only provide an estimate of the same. As Figure 4(b) suggests, both versions of BSI
clearly outperforms OSQR and Random Walk in terms of recall regardless of the network size
thus proving the scalability of our algorithm.
(a) Recall vs. TTL (b) Recall vs. Network Size
Figure 4. Recall for Single Concept Query (Filter Size = 250bits)
5.2.2 Effect of Query Length
Effect of Query Length: One of the key design objectives of BSI is to achieve high recalls for
queries with more than single concept. Figure 5 plots the effect of query length on recall. We
observed a significant improvement in recall in BSI over OSQR for both BSI versions:
BSI(TSBF-level1+level2), and BSI(TSBF-level1). On average for all query sizes we observed
that recall improves four orders of magnitude (315.79%) and by three orders of magnitude
(223.88%) compared to OSQR in BSI(TSBF-level1+level2) and BSI(TSBF-level1) respectively.
Recall improves ten orders of magnitude (903.62%) and by three orders of magnitude (682.01%)
compared to Random Walk in BSI(TSBF-level1+level2) and BSI(TSBF-level1) respectively. The
superiority of BSI was more evident for queries with higher number of concepts. The reason for
this behavior can be explained using the peer knowledge summarization and the neighbor
selection mechanisms of all three algorithms. Random walk does not use any intelligence in
neighbor selection mechanism. Peers randomly select one if their neighbors to forward queries.
BSI and OSQR estimate the number of documents reachable thorough a peer for a query as its
relevance score in neighbor selection process. An OSQR peer only maintains the number of
reachable documents per ontology concept for each neighbor in its routing index and a peer
estimates the number of documents reachable from a given neighbor by taking the minimum of
the number of documents reachable for each queried concept for that neighbor. Taking the
minimum as the relevance score, however, is an optimistic estimate. This actually represents an
upper bound of the number of actual relevant documents reachable through a neighbor.

26
Figure 5. Recall vs. Query Length (TTL=7, Filter Size=250bits).
To take a more accurate decision, the decision taking peer must ideally have the set of documents
reachable through the neighbor for each queried concept and should perform a intersection of all
sets of documents relevant to the query to obtain the set of documents actually reachable through
that neighbor for the query. The size of this resulting set will accurately represent the number of
documents reachable through the neighbor for that query. However, maintaining such detailed
information is too costly in P2P networks. BSI compensates this information loss by maintaining
bloom filters representing the queries (along with number of documents reachable as associated
bit counts) and documents reachable through a neighbor, thereby providing a more compact and
high quality information summarization leading to more accurate estimations of number of
documents reachable for queries.
5.2.3 Effect of Filter Size
Having established the scalability of our general approach, we now turn our attention to the effect
of Bloom filter size in recall and storage cost.
Using TSBFs for local knowledge maintenance results in substantial increase in recall at low
storage cost. As shown in Figure 6 recall substantially increases with filter size in all BSI
versions. This is the expected behavior as larger filter sizes can keep large volume of information
at a tolerable false positive rate resulting in better query routing decision leading to high recall
rates. On average we observed a 300.47% and 216.37% average improvement in recall over
OSQR and a 1038.60% and 799.50% average improvement in recall over Random Walk for BSI
(TSBF level1+level2) and BSI (TSBF-level1) respectively.
Table. 2 shows the associated storage costs for various filter sizes. This storage cost accounts for
a peer’s TSBF and its routing index. Storage costs are shown for original BSI(TSBF with level 1
and level 2 present) and for two modified BSI versions where a TSBF contain only level1 or level
2 respectively. While maintaining a large filter size allows us to decrease the false positive rate
we observed that it substantially increases the TSBF size. However, while storing a TSBF in a
peer’s routing index per neighbor can contribute to increase in index sizes, this storage
requirement is not a burden to an average internet computing device. Moreover, as long as the
false positive rate of the TSBF is at an acceptable level for the application, there is no need to
unnecessarily increase the filter size. We found through experimentation that the optimal filter

27
size for our trace was approximately 250 bits. However, those peers that have storage limitations
and cannot afford to reserve a filter size below this size have the option of either compressing
their routing indices as illustrated in Section 4.2.2 or to eliminate one of the levels of TSBFs
stored in routing indices to observe their storage restrictions.
Figure 6. Recall vs. Filter Size (TTL=7, Average Query Length=2.5)
Table 2. Storage Overhead.
Algorithm Filter size (bits)
100 300 500
OSQR 10752.01 10752.01 10752.01
BSI (TSBF level 1+level 2) 4875.00 14625.00 24375.00
BSI (TSBF-level 1) 4800.00 14400.00 24000.00
BSI (TSBF-level 2) 56.25 168.75 281.25
5.2.4 Search Overhead
Using TSBFs for knowledge transfer and acquisition process results in significant lowering of
data transmission. OSQR consumes a significant amount of bandwidth in data transfer. OSQR on
average consumed 13.94KB of bandwidth per query in data transfer. This was in contrast to the
4.13KB and 3.88KB consumed by a query in BSI (TSBF level 1+level 2)and BSI (TSBF-level 1)
respectively. This corresponded to a 70.35%, and 95.46% reduction over OSQR in traffic cost for
BSI (TSBF level 1+level 2)and BSI (TSBF-level 1) respectively. Random Walk search message
consumed only 1.02KB bandwidth as a message does not carry additional information for
knowledge dissemination. The high search cost of OSQR is attributed to the fact that it transmits
routing index information about each taxonomy concept for every visited neighbor as feedback.
Queries in BSI on the other hand transfers much more space efficient TSBFs relevant only to the
queried taxonomy concepts thus significantly reducing the bandwidth consumption.

28
Figure 7. Search Cost (TTL=7, Filter Size=250bits, Average Query Length=2.5)
To get an accurate picture of overall search efficiency of a search algorithm we need a combined
measure of both recall and search cost. Therefore we define the search efficiency to be the ratio of
recall with search cost. In Figure 8 we plot the search efficiency of BSI, OSQR and Random
Walk against network size. From the results it is clear that efficiency of BSI (both versions) is
much higher compared to OSQR and Random Walk, and BSI is capable of producing high recall
at much lower message cost.
Figure 8. Search Efficiency (TTL=7, Filter Size=250bits, Average Query Length=2.5)
6. CONCLUSION AND FUTURE WORK
Routing in unstructured P2P networks is a challenging problem. In this work, we have explored
the approach of compact representation of large semantic query based indices through the use of
Bloom filters. Our Bloom filter based routing index quantifies the strength of a peer’s
neighborhood for individual queries and then uses this information for forwarding queries. The
simple yet powerful design of BSI makes it easy to implement. Simulation based comparison with
state-of-the art query routing mechanisms, under a wide variety of scenarios establish the
performance advantages of BSI. The BSI search framework presents interesting possibilities of
implementing high level semantics of trust, reliability, etc. using routing and forwarding policies.

29
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30
AUTHORS
Rasanjalee Dissanayaka received her Ph.D. in Computer Science from Georgia State
University in 2014. She is currently serving as an Assistant Professor of Computer
Science at College of St. Benedict & St. Johns University since August 2014. Her main
research interests are in the areas of distributed algorithms and semantic computing .
Rasanjalee's publications include papers in international journals and conference
proceedings.
Sushil K. Prasad (BTech’85 IIT Kharagpur, MS’86 Washington State, Pullman; PhD’90
Central Florida, Orlando - all in Computer Science/Engineering) is a Professor of
Computer Science at Georgia State University (GSU) and Director of Distributed and
Mobile Systems (DiMoS) Lab. He has carried out theoretical as well as experimental
research in parallel and distributed computing, resulting in 120+ refereed publications,
several patent applications, and about $3M in external research funds as principal
investigator and over $6M overall (NSF/NIH/GRA/Industry).
Shamkant Navathe is currently a professor at the College of Computing, Georgia
Institute of Technology, Atlanta. He is known for his work on database modeling,
database conversion, database design, distributed database fragmentation, allocation,
and redesign, and database integration. Current projects include biological genome
databases, data mining, modeling of database security, and knowledge base design. He
has worked with IBM and Siemens in their research divisions and has been a
consultant to various companies, including Honeywell, Nixdorf, CCA, ADR, Digital,
MCC, Harris, Equifax and Hewlett Packard corporations.
Vikram Goyal is a faculty in IIIT Delhi. He received his PhD in Computer Science
and Engineering from the Department of Computer Science and Engineering at IIT
Delhi in 2009. Before pursuing PhD, he completed his M.Tech in Information
Systems from the Department of Computer Science and Engineering at NSIT Delhi in
2003. His primary area of research is Spatial-Textual Data Management and Data
Privacy. He has been given a DST Young Scientist Award for his research on data
privacy in Location-based Services.

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