Diapositiva 1

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Diapositiva 1

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Claudio Gennaro1, Federica Mandreoli2,4, Riccardo Martoglia2, ... mink [QueryIdx(Q) * MRI(p,pis)] S j=1...n mink [QueryIdx(Q) * MRI (p,pjs) ... – PowerPoint PPT presentation

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Title: Diapositiva 1

1
ISDSI 2009 June 25th, 2009
Combining Semantic and Multimedia Query Routing
Techniques for Unified Data Retrieval in a PDMS
Claudio Gennaro1, Federica Mandreoli2,4, Riccardo
Martoglia2, Matteo Mordacchini1, Wilma Penzo3,4,
and Simona Sassatelli2 1 ISTI PI/CNR, Pisa 2
DII - Università degli Studi di Modena e Reggio
Emilia 3 DEIS - Università degli Studi di
Bologna 4 IEIIT BO/CNR, Bologna
This work is partially supported by the Italian
co-founded Project NeP4B
2
Motivation

ICTs over the Web have become a strategic asset
Internet-based global market place where
automatic cooperation and competition are allowed
and enhanced

NeP4B Project (Networked Peers for Business)

The aim development of an advanced technological
infrastructure for SMEs to allow them to search
for partners, exchange data and negotiate without
limitations and constraints
The architecture inspired by Peer Data
Management Systems (PDMSs)

A peer a single SME or a mediator

It keeps its data in an OWL ontology
Multimedia objects ? multimedia attributes in the
ontology (e.g. image)

It is queried by exploiting a SPARQL-like query
language ? similarity predicates (FILTER
function LIKE )

Peers are connected by means of semantic mapping
(with scores)
Peers collaborate in solving users queries

Queries are formulated on the peers ontology

Answers can come from any peer that is connected
through a semantic path of mappings

How to effectively and efficiently answer a
query?

By adopting effective and efficient query routing
techniques
Flooding is not adequate
Overloads the network (traffic computational
effort)
Overwelms the querying peer (irrelevant results)

We leverage our distinct experiences on semantic
Mandreoli et al. WIDM 2006, Mandreoli et al.
WISE 2007 and multimedia Gennaro et al.
DBISP2P 2007, Gennaro et al. SAC 2008 query
routing

We propose to combine our approaches in order to
design an innovative mechanism for a unified data
retrieval

Two main aspects characterize our scenario
the semantic heterogeneity of the peers
ontologies
the execution of multimedia predicates

We pursue
Effectiveness by selecting the semantically best
suited subnetworks
Efficiency by promoting the networks zones where
potentially matching objects are more likely to
be found, while pruning the others

ISDSI09 - C. Gennaro, F. Mandreoli, R.
Martoglia, M. Mordacchini, W. Penzo, S. Sassatell
- Combining Semantic and Multimedia Query
Routing Techniques for Unified Data Retrieval in
a PDMS
6
Outline

Motivation
Query Answering Semantics
Query Routing
Semantic Query Routing
Multimedia Query Routing
Combined Query Routing
Routing Strategies
Experimental Evaluation
Conclusions and Future Works

Peer pi has an ontology Oi Ci1 , , Cim
A semantic mapping a fuzzy relation M(Oi,Oj) ?
Oi?Oj where each instance (C,C) has a membership
grade µ(C,C) ? 0,1

Query formula
f lttriple_patterngt ltfilter_patterngt
lttriple_patterngt triple lttriple_patterngt ?
lttriple_patterngt
ltfilter_patterngt f ltfilter_patterngt ?
ltfilter_patterngt
ltfilter_patterngt ? ltfilter_patterngt
(ltfilter_patterngt)
f is a relational (,lt,gt, lt, gt) or similarity
(t) predicate

Local query execution

evaluation of f on a local data instance i
s(f,i) ? 0,1
s(f,i) s( f(f1, , fn), i ) sfunf ( s(f1,i),
, s(fn,i) )
Boolean semantics for relational predicates
non-Boolean semantics for similarity predicates
t-norm ( resp. t-conorm) for scoring conjunctions
(resp. disjunctions)
Local query answers Ans(f,pi) (i,s(f,i))
s(f,i)gt0

according to the mapping M(Oi,Oj)
s(f,pj) sfunc ( µ(C1 ,C1), , µ(Cn ,Cn) )
sfunc is a t-norm

Multi-step query reformulation (p?p1??pm
Pp1pm )

f undergoes a chain of reformulations f?f1??fm
s(f, Pppm ) sfunr (s(f, p1), s(f1, p2), ,
s(fm-1, pm))
sfunr is a t-norm

Query answering semantics

f submitted to p
P p1,,pm set of accessed peers
Pppi path used to reformulate f over each pi
in P
Ans (f, p U P) Ans(f,p) U Ans(f, Ppp1 ) U
U Ans(f, Pp1pm )
where Ans(f, Pppi ) (Ans(f,pi), s(f, Pppi
))

ISDSI09 - C. Gennaro, F. Mandreoli, R.
Martoglia, M. Mordacchini, W. Penzo, S. Sassatell
- Combining Semantic and Multimedia Query
Routing Techniques for Unified Data Retrieval in
a PDMS
9
Outline

Motivation
Query Answering Semantics
Query Routing
Semantic Query Routing
Multimedia Query Routing
Combined Query Routing
Routing Strategies
Experimental Evaluation
Conclusions and Future Works

Whenever pi forwards a query to one of its
neighbors pj , the query might follow any of the
semantic paths originating at pj, , i.e. in pjs
subnetwork

Main idea introduction of a ranking approach for
query routing which promotes pi neighbors whose
subnetworks are the most semantically related to
the query.

Peer2s

Preliminaries
pjs the set of peers in the subnetwork rooted
at pj
Ojs set of schemas Ojk pjk in pjs
Ppipjs set of paths from pi to any peer in pj

A Generalized Semantic Mapping relates each each
concept C in Oi to the set of concepts Cs in Ojs
taken from the mappings in Ppipjs according to
an aggregated score which expresses the semantic
similarity between C and Cs.

Each peer p maintains a matrix named Semantic
Routing Index (SRI) containing the membership
grades given by the generalized semantic mappings
between itself and its neighborhood Nb(p)

SRIi,j represents how the j-th concept is
semantically approximated by the subnetwork
rooted at the i-th neighbor

SRI-based Query Processing

When a peer receives a query formula f, it
exploits its SRI scores to determine a ranking
for its neighborhood
Rpsem (f)pi sfunc(µ(C1,C1s), , µ(Cn,Cns))

ISDSI09 - C. Gennaro, F. Mandreoli, R.
Martoglia, M. Mordacchini, W. Penzo, S. Sassatell
- Combining Semantic and Multimedia Query
Routing Techniques for Unified Data Retrieval in
a PDMS
12
Outline

Motivation
Query Answering Semantics
Query Routing
Semantic Query Routing
Multimedia Query Routing
Combined Query Routing
Routing Strategies
Experimental Evaluation
Conclusions and Future Works

The execution of multimedia predicates is
inherently costly (CPU and I/O)

Main idea introduction of a ranking approach for
query routing which promotes pi neighbors whose
subnetworks contain the highest number of
potentially matching objects.

Preliminaries
Each peers object is classified w.r.t. its
distance (dissimilarity) to some reference
objects (pivots)

E.g.
object O, pivot P, with d(O, P) 42

Bit-vector

Each peer maintains a condensed description of
such a classification of its objects by using
histograms (Peer Indices)

Each peer p also maintains a Multimedia Routing
Index (MRI) containing the aggregated description
of the resources available in its neighbors
subnetworks

Any MRI row MRI(p,pis) is built by summing up the
Peer Indices in the i-th neighbors subnetwork

MRI-based Query Processing

Similarity-based Range Queries over metrics
objects

For each query Q, a vector representation
QueryIdx(Q) is built by setting to 1 all the
intervals covered by the requested range

When a peer p receives Q, it computes the
percentage of potential matching objects in each
neighbors subnetwork w.r.t the total objects in
Nb(p)

ISDSI09 - C. Gennaro, F. Mandreoli, R.
Martoglia, M. Mordacchini, W. Penzo, S. Sassatell
- Combining Semantic and Multimedia Query
Routing Techniques for Unified Data Retrieval in
a PDMS
15
Outline

Motivation
Query Answering Semantics
Query Routing
Semantic Query Routing
Multimedia Query Routing
Combined Query Routing
Routing Strategies
Experimental Evaluation
Conclusions and Future Works

Both the semantic and multimedia scores induce a
total order
They can be combined by means of a meaningful
aggregate function in order to obtain a global
ranking

Rpcomb (f) a Rpsem (f) ? ß Rpmm(f)

We inspire to
Fagin, Lotem Naor Optimal Aggregation
Algorithms for Middleware. Journal of Computer
and System Sciences, 66 47-58, 2003.

Optimal aggregation algorithms can only work with
monotone aggregation functions
E.g. min, mean, sum

ISDSI09 - C. Gennaro, F. Mandreoli, R.
Martoglia, M. Mordacchini, W. Penzo, S. Sassatell
- Combining Semantic and Multimedia Query
Routing Techniques for Unified Data Retrieval in
a PDMS
17
Combined Query Routing (cont.)
E.g.
SELECT ?company WHERE ?tile a Tile
. ?tile company ?company . ?tile
price ?price . ?tile origin ?origin .
?tile image ?image . FILTER
( (?price lt 30) (?origin Italy) LIKE
(?image, myimage.jpg, 0.3)) LIMIT 100

Final ranking Peer3-Peer2
Peer3 roots the most promising subnetwork!

ISDSI09 - C. Gennaro, F. Mandreoli, R.
Martoglia, M. Mordacchini, W. Penzo, S. Sassatell
- Combining Semantic and Multimedia Query
Routing Techniques for Unified Data Retrieval in
a PDMS
18
Outline

Motivation
Query Answering Semantics
Query Routing
Semantic Query Routing
Multimedia Query Routing
Combined Query Routing
Routing Strategies
Experimental Evaluation
Conclusions and Future Works

The adopted routing strategy determines the set
of visited peers and induces an order on it

When a peer p receives Q it computes the ranking
Rpcomb(f) on its neighbors

Different routing strategies relying on such
ranking and having different performance
priorities are possible

Efficiency (Depth First Model) ? the best peer in
one hop!
it exploits the only information provided by
Rpcomb (f)
Effectiveness (Global Model) ? the best known
peer!
it exploits the information provided by Up is
visited Rpcomb (f)

20
Outline

Motivation
Query Answering Semantics
Query Routing
Semantic Query Routing
Multimedia Query Routing
Combined Query Routing
Routing Strategies
Experimental Evaluation
Conclusions and Future Works

Simulation environment for SRI and MRI
Peers belong to different semantic categories
Ontologies consisting of a small number of
classes
Multimedia contents hundreds of images taken
from the Web, characterized by two MPEG7 standard
features (scalable color edge histogram)
Network topology
randomly generated with the BRITE tool
in the size of few dozens of nodes
Queries on randomly selected peers
Routing strategy DF search
Aggregation function mean
Stopping condition number of retrieved results

ISDSI09 - C. Gennaro, F. Mandreoli, R.
Martoglia, M. Mordacchini, W. Penzo, S. Sassatell
- Combining Semantic and Multimedia Query
Routing Techniques for Unified Data Retrieval in
a PDMS

Queries
on randomly selected peers
Routing strategy
DF search
Aggregation function
mean
Stopping condition
number of retrieved results
Effectiveness evaluation
We measure the quality of the results (combined
satisfaction)
Efficiency evaluation
We measure the number of performed hops

22
Effectiveness Evaluation

We measure the semantic quality of the obtained
results (satisfaction)

We measure the number of hops performed by the
query

ISDSI09 - C. Gennaro, F. Mandreoli, R.
Martoglia, M. Mordacchini, W. Penzo, S. Sassatell
- Combining Semantic and Multimedia Query
Routing Techniques for Unified Data Retrieval in
a PDMS
24
Outline

Motivation
Query Answering Semantics
Query Routing
Semantic Query Routing
Multimedia Query Routing
Combined Query Routing
Routing Strategies
Experimental Evaluation
Conclusions and Future Works

We presented a novel approach for processing
queries effectively and efficiently in a
distributed and heterogeneous environment, like
the one of the NeP4B Project
We proposed an innovative query routing approach
which exploits the semantic of the concepts in
the peersontologies and the multimedia contents
in the peersrepositories
We experimentally prove the effectiveness of our
techniques with a series of exploratory tests

(In the future) we will
perform new tests on larger and more complex
scenarios
integrate our techniques in a more general
framework for query routing (including latency,
costs, etc.)

ISDSI09 - C. Gennaro, F. Mandreoli, R.
Martoglia, M. Mordacchini, W. Penzo, S. Sassatell
- Combining Semantic and Multimedia Query
Routing Techniques for Unified Data Retrieval in
a PDMS

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