Title: Diapositiva 1
1ISDSI 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
2Motivation
- 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)
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
3The Reference Scenario
- 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 )
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
4The Reference Scenario (cont.)
- 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)
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
5Combining Semantic and Multimedia Query Routing
Techniques
- 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
6Outline
- Motivation
- Query Answering Semantics
- Query Routing
- Semantic Query Routing
- Multimedia Query Routing
- Combined Query Routing
- Routing Strategies
- Experimental Evaluation
- Conclusions and Future Works
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
7Query Answering Semantics
- 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
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
8Query Answering Semantics (cont.)
One-step query reformulation (pi?pj)
- 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
9Outline
- Motivation
- Query Answering Semantics
- Query Routing
- Semantic Query Routing
- Multimedia Query Routing
- Combined Query Routing
- Routing Strategies
- Experimental Evaluation
- Conclusions and Future Works
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
10Semantic Query Routing
- 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.
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
11Semantic Query Routing (cont.)
- 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
12Outline
- Motivation
- Query Answering Semantics
- Query Routing
- Semantic Query Routing
- Multimedia Query Routing
- Combined Query Routing
- Routing Strategies
- Experimental Evaluation
- Conclusions and Future Works
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
13Multimedia Query Routing
- 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
- Each peer maintains a condensed description of
such a classification of its objects by using
histograms (Peer Indices)
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
14Multimedia Query Routing (cont.)
- 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
15Outline
- Motivation
- Query Answering Semantics
- Query Routing
- Semantic Query Routing
- Multimedia Query Routing
- Combined Query Routing
- Routing Strategies
- Experimental Evaluation
- Conclusions and Future Works
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
16Combined Query Routing
- 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
17Combined 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
18Outline
- Motivation
- Query Answering Semantics
- Query Routing
- Semantic Query Routing
- Multimedia Query Routing
- Combined Query Routing
- Routing Strategies
- Experimental Evaluation
- Conclusions and Future Works
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
19Routing Strategies
- 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)
20Outline
- Motivation
- Query Answering Semantics
- Query Routing
- Semantic Query Routing
- Multimedia Query Routing
- Combined Query Routing
- Routing Strategies
- Experimental Evaluation
- Conclusions and Future Works
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
21Experimental Evaluation
Experimental setting
- 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
22Effectiveness Evaluation
- We measure the semantic quality of the obtained
results (satisfaction)
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
23Efficiency Evaluation
- 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
24Outline
- Motivation
- Query Answering Semantics
- Query Routing
- Semantic Query Routing
- Multimedia Query Routing
- Combined Query Routing
- Routing Strategies
- Experimental Evaluation
- Conclusions and Future Works
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
25Conclusions 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