a survey on large scale schema and ontology matching techniques
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A Survey on Large Scale Schema andOntology Matching Techniques
K. Saruladha, Dr. G. Aghila, and B. Sathiya
AbstractWith the fast growth and the increasing usage of the large variety of data like XML schemas, ontologies. In manydomains, the heterogeneity among the data increases enormously. Hence solving such heterogeneity needs matching
techniques. In areas like E-business, web and life sciences the size of XML schema and ontology is large. But most of the
existing matching techniques address only small match tasks. So, we present an overview of recently proposed matching
techniques like early pruning of the search space, divide and conquer strategies, parallel matching and some renowned
ontology matching tools which achieve high match efficiency or/and high quality for large-scale matching. In addition to this the
pros and cons of various matching techniques is summarized.
Index TermsSimilarity Measure, Schema Matching, Ontology Matching, Ontology Alignment.
1 INTRODUCTION
Ngeneral
relational
and
XML
database
is
called
as
Schemaandthedatabaserepresentedbysemantic lan
guage likeWebOntologyLanguage (OWL)orResource
DescriptionLanguage(RDF)iscalledasOntology.Match
ing technique finds semantic correspondences between
cartesianproductofentitypairof thegiven twoontolo
gies. These correspondences may stand for equivalent,
subsumption, or disjoint relationbetween the ontology
entities.Theresultofmatchingtechniqueisthesetofse
mantic correspondences called alignments. Fig.1 depicts
the general framework for the schema matching tech
niques which is also applicable for ontologymatching
techniques.As
shown
in
figure
the
input
of
the
matching
technique istwoschemaswhicharefirst importedintoa
format suitable forprocessing.For a faster computation
the schemamaybe preprocessed, e.g., preparing neig
bourlistforeachentitytofastenthestructuralsimilarity
calculation, removing redundant information from sche
ma,etc.Thesimilarityvalueiscalculatedforallcartesian
product entitypairsone from eachof twopreprocessed
schemas using a match workflow consisting of set of
matcher (e.g., lexical, structural and semanticmatcher).
Thesemanticvalueranges from0 to1, i.e.,value1 indi
catesequivalentandvalue0meansdisjointrelation.The
matcherworkflow
can
be
sequential,
parallel,
iterative
or
in some mixed fashion. The similarity value obtained
fromthematchworkflowcanbeaggregatedtoobtainthe
final alignment. For large scale schema matching the
workflowwill
slightly
vary
to
incorporate
techniques
like
early pruning andpartitioning of schema to reduce the
searchspace,parallelization,etc.
According toShvaikoPandEuzenatJ [11]oneof the
toughestchallengeformatchingsystemishandlinglarge
scaleschemasorontologyandonthebasisofRahmE[12]
work domain where large scale matching is necessary
includeebusiness[13],webdata[14],lifescienceontolo
gies,andmedicine [15] andweb directories [16]. Even
thoughtheadvancementsaremadeinthecurrentmatch
ing systems, they are stillunable to achievegood effec
tiveness (correctnessandcompletenessof thealignment)
andgood
efficiency
(time
and
space
efficiency).
The
effec
tiveness ismeasuredby precision and recallwhile the
efficiency ismeasured by execution time andmemory
used.
The remainder of thispaper is organized as follows:Section 2 discusses various large scale matching techniques. Section 3 gives abrief comparison of the largescalematching techniques. Finally, Section 4 concludesthesurveyofontologymatchingtechniques.
Fig. 1 General Framework for Matching Process
Mrs. K. Saruladha is with the Computer Science Department, Pondi-cherry Engineering College, Puducherry, Pin 605014, India.
Dr. G. Aghila is with the Computer Science Department, PondicherryUniversity, Puducherry, Puducherry, Pin 605014, India.
Ms. B. Sathiya is with the Computer Science Department, PondicherryEngineering College, Puducherry, Pin 605014, India.
I
2011 Journal of Computing Press, NY, USA, ISSN 2151-9617
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InputSchemas
Preprocessor
Preprocessor
Similarity
Computation
Similarity
AggregationAlignments
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2 APPROACHESUSEDFORLARGESCALEMATCHINGTECHNIQUE
In this section, we discuss about various large scale
matching techniques.Thematching techniques are cate
gorizedintofourmajorcategoriesasshownbelow
1) Earlypruningstrategybasedmatchingtechnique
a.
Quick
Ontology
Matching
algorithm
(QOM)
b. Eric peukert et al. schema and ontology
matchingalgorithm
2) Partitionbasedmatchingtechnique
a. Coma++ schema and ontologymatching al
gorithm
b. FalconAOontologymatchingalgorithm
c. Taxomapontologymatchingalgorithm
d. Anchorfloodontologymatchingalgorithm
3) Parallelmatching technique
a. Grossetal.ontologymatchingalgorithm
4) Othermatchingtool
a. RiMOMontologymatchingtoolb. ASMOV (AutomatedSemanticMatchingof
Ontologies with Verification) ontology
matchingtool
c. Agreementmaker schema and ontology
matchingtool
2.1Early Pruning Strategy Matching TechniqueTheaimofearlypruningtechniqueistoreducethesearch
space formatching, e.g., onematcher can prune entity
pairswhose semantic correspondencevalue isvery low,
thus reducing search space for the subsequentmatcher.
Thisidea
is
suitable
for
both
sequential
and
iterative
matcherworkflow.QOM [1]was the first system to im
plement this ideausing iterativematcherworkflowand
Peukert et al. [2] System implemented both matcher
workflows.
2.1.1 QOMQOM is theontologymatchingalgorithmadapted from
theNaiveOntologyMatching (NOM). Thebasicdiffer
encebetweenNOMandQOMisQOMusesheuristicand
dynamic programming approach to reduce the search
space with marginally compromising on effectiveness.
QOM isan iterativematcher consistingof the following
six
steps
to
match
the
ontologies:
Feature
engineering,
search step selection, similarity computation, similarity
aggregation,interpretationanditeration.
FeatureEngineering.Theprocessofdeterminingwheth
ertwoentitiesaresameornotisbasedontheentityfea
tures.RDF features likeentitiesunifiedresource identifi
ers (URIs), its label, its parent entities, similar entities,
childrenentitiesanddomainspecificfeaturesareusedfor
matching.Thisstepconstructsthesearchspaceofontolo
gy entitiesby computing the cartesianproductofentity
pairsusingentityfeaturesofbothontologies.
SearchStepSelection.Theentitypairsobtainedwillbe
pruned using heuristics strategies to reduce the search
space contructed in the previous step. The selected
matchingpairswillbeprocessed forsimilaritycomputa
tionandtheirinterpretation.Onceagainthesystemhasto
determinewhichmatchingpairstoaddtotheagendafor
thenext iteration.Theheuristics strategies arebasedon
labelsof entities, entityneighbour to thealignmentsob
tainedfrom
the
previous
iteration,
hierarchy
of
entities,
randompickorcombinationoftheabovestrategies.
Similarity Computation. The similarity between the
above selected entity pairs can be measured by wide
range of similarity functions likeLevenshteins editdis
tance,Dicecoefficientandcosinesimilarityvalue .Based
on the feature of the entity any of the above similarity
functioncanbechosen.
SimilarityAggregation.QOMusesweightedaverageof
similarityvaluesforagivenentitypair.Theweightofthe
similarityvaluesiscalculatedbysigmoidfunctionwhich
emphasizeshighindividualsimilaritiesanddeemphasiz
eslowindividualsimilarities.
Interpretation.First,
matching
pairs
with
similarity
value
less than a threshold isdiscarded.Next similaritypairs
whichviolatesbijectivity(onetooneandontoconstraint)
ofthematchingarediscarded.
Iteration. Initial iteration uses lexical knowledgewhile
later iterations use ontology structure knowledge for
matching.The number of iteration required irrespective
ofontologysizebasedontheirexperimentsisten.
BasedontheevaluationQOMisnearlytwentytimesfast
erthanNOM.ThedrawbackofQOMisthat,effectiveness
ismarginally lower thanNOM sincenotall entitypairs
areevaluated.
2.1.2 Peukert et al. MethodPeukertetal.proposedaschemaandontologymatching
algorithm.Itisarulebasedoptimizationtechniquewhich
rewritesmatchworkflow to improve performance. The
filteroperatorswithinmatchworkfloweliminatedissimi
larentitypairs (whosesimilarityvalue is less thansome
threshold) from intermediatematch results and thus re
ducingsearchspaceforfurthermatchers.Thethresholdis
either statically predetermined or dynamically derived
fromthesimilaritythresholdused inthepreviousmatch
workflowtoselectalignment.
TheinputofthisprocessisXMLschemas,metamod
els
or
ontologies.
These
input
formats
are
converted
into
a
standard internal format. The user must design the
matchingprocesswhich isrepresentedasagraphwhere
theverticesrepresentmatcherfromthelibraryandedges
representexecutionorderanddataflow.Sincethegraph
isdesignedbytheuserthematchworkflowcanbeparal
lelor serialor iterative.Theuser canutilize the rewrite
recommendation system, to increase efficiency of the
matchingworkflow.These rewrite recommendation sys
tem is similar to costbased rewrites in database query
optimizatione.g,theuseofrewriterulesaresimilartothe
useofpredicatepushdownrulesfordatabasequeryop
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timizationwhich reduce thenumberof input tuples for
joinsandotherexpensiveoperators.Asimplecostmodel
isusedtochoosewhichpartsofamatchingworkflowto
rewrite.Theserewritescanbeacceptedorrejectedbythe
user.Once thematching process graph is finalised the
graphcanbeexecutedtoobtainthealignment.
Thedrawbacksof thismethodareasfollows.Rewrite
rules,only
focus
on
efficiency
improvements
but
not
on
effectiveness improvement.Thematcher library consists
ofonlyfewnumbersofmatchers.
2.2 Partition Based Matching Technique
Theideaofthisapproachistopartitiontheontologyand
executeapartitionwisematching.Thepartitioningisper
formedinsuchawaythateachpartitionoffirstontology
ismatchedwithonlysmallsubsetofthepartitions(ideal
ly,onlywithonepartition)of the secondontology.This
methodreducesthesearchspaceandthusbetterefficien
cy.Space complexityof thematchingprocess is also re
duced. Four partitionbasedmethodsCOMA++ [5], Fal
conAO
[3],Taxomap
[4],
anchor
flood
[6]
will
be
dis
cussedbelow.
2.2.1 COMA++COMA++ is a generic schema and ontology matching
toolwith a library ofmatchers and a flexible option to
combinethematcherstorefinethematchingresults.This
methodcanprocessXMLschema,relationaldatabaseand
OWLontology.First the schema isprocessed to identify
the relevant components (node, path or fragment) for
matching. Then depending on the component chosen a
matcherworkflowisusedtocomputecomponentsimilar
ities.Finallysimilaritycombinationmethodsareused to
findthe
correspondences
between
components.
Fragmentcomponentbasedmatchingprocess iscapa
ble of handling large scale schema.A rooted subgraph
downtothe leaf level intheschemagraph isreferredas
fragment. Ingeneral, fragments shouldhave littleorno
overlap to avoid repeated similarity computations and
overlappingmatch results.The three typesofautomatic
fragmenttypesareschema,subschema,andshared frag
ments.Usercanalsoselectasubstructureasafragment.
Based on the fragment type, the schema is fragmented
andfragmentpairsfromeachontologyismatchedtofind
the most similar fragment pair.The search for similar
fragments
is
some
kind
of
light
weight
matching,
e.g.,
basedonthesimilarityofthefragmentroots.Nextsimilar
fragmentpair ismatchedelementwiseto find thealign
ments.
Thedrawbacksofthismethodareasfollows.COMA++
isdevelopedforXMLschemasandhenceitisnotsuitable
forcomplexontologygraph.Ituserelativelysimpleheu
risticrules topartition the inputschemasresultingoften
in too few or too many partitions and to find similar
fragmentpaironlytherootnodefeaturesareusedwhich
willleadtolessmatchingquality.
2.2.2 Falcon-AO
FalconAO is an ontologymatching toolwhich aims at
finding alignments of the given twoOWL/RDF ontolo
gies.Thematcher libraryofFalconAO consistofVDoc
[17] and ISub [19] which are lightweight linguistic
matchers,GMO [18] an iterative structuralmatcher and
PBM(PartitionBasedMatcher)whichadoptsthedivide
and
conquer
strategy
to
find
block
mappings
between
largescale ontologies. The three phase of PBM are ex
plainedbelow
Partitioning ontologies: Structural proximitiesbetween
entitiesare calculatedbasedonhow closely theyare re
latedinthehierarchies.Theontologyclustersareformed
based on structural proximities using modified ROCK
[20] clustering algorithm. RDF Blocks are constructed
fromtheclustersbyassigningeachRDFtripletoacluster
inwhichatleasttwoentitiesarecontained.
Matchingblocks:Thealignmentwithhighsimilaritiesis
referred as anchors. A lightweight string comparison
technique, ISUB, is firstly employed to exploit anchors
betweentwo
full
ontologies
and
then
the
blocks
from
the
twoontologiesarematchedbasedon thedistributionof
theanchors.
Discovering alignments:VDOC adopts a linguistic ap
proach toontologymatching.GMO isan iterativestruc
turalmatcher.Similaritycombinationisaheuristicstrate
gytotunethethresholdsoftheabovetwomatchers.
Thedrawbacksof thismethodareas follows.Theentire
ontologyneeds tobeprocessedtofindanchorsandthus
efficiencyofFalconAOisreduced.Maximumnumberof
entityinaclusterisdeterminedbytheGMOmatcherand
theclusteringalgorithm terminatesabruptly if it reaches
maximum
number
which
will
lead
to
poor
clustering.
2.2.3 TaxomapTaxomapisanontologymatchingalgorithmconsistingof
twopartitioningalgorithmnamelyPartitionAnchorParti
tion(PAP)andAnchorPartitionPartition(APP)whichhave
been designed to take the alignment objective into ac
count in the partitioning process. Themost structured
ontologyisreferredastargetontologyandthelessstruc
tured is referred as sourceontology.PAP is suitable for
structuredontologyvsunstructuredontologyandAPPis
suitable for structured ontology vs structured ontology.
Theentitypaironefromeachoftwoontologywhichhas
identicallabelsiscalledasanchorswhichwillbeusedin
bothPAPandAPP.Thealignmentisbasedonlexicalandstructure(subclass)similaritymeasure.
PartitionAnchorPartition(PAP):
1. UsePBMmatchertopartitionthetargetontologyinto
setofblocks.
2. Identify the set of anchorsbetween two ontologies.
This setwillbe the centerof the futureblockwhich
willbegeneratedfromthesourceontology.
3. Use PBM matcher to partition the source ontology
aroundtheidentifiedcenters.
4. Aligneachblockwiththecorrespondingblock.
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AnchorPartitionPartition(PAP):
1. Identifythesetofanchorsacrossontologies.
2. Partitionboththetargetontologyandsourceontology
by modifying PBM matcher in order to take into
accountsharedanchors.
3. Alignblocksthatsharemaximalnumberofanchors.
The
drawbacks
of
this
method
are
as
follows.
The
ef
fectivenessof thismethoddependson theavailabilityof
identical labels across ontologies. Only labels and hie
rarchystructureisusedformatchingandhencecompara
tivelylessrecall.
2.2.4 AnchorFlood
AnchorFlood is an ontologymatching algorithmwhich
implements a dynamic partition based matching that
avoidstheprioripartitioningoftheontologies.Thesimi
laritymeasure for alignment isbased on internal struc
ture,externalstructureandJaroWingler stringdistance.
The process starts ofwith the set of anchorswhere an
anchor is defined as a exact stringmatch of concepts,
propertiesorinstancespair.The algorithm preprocesses ontologies to normalize
the textual contents of entities. It startswith an anchor
and collects neighboring entity of the chosen anchor
acrossontologies.Thesegmentpairconstructedfromthe
abovecollectedneighbors isprocessed toproducealign
mentpairs.Theprocess repeatsuntileitherall the col
lected entity are explored, or no new aligned pair is
found.Theanchorwillbediscardedasmisalignment if
theconstructedsegmentof theanchordoesnotproduce
sufficientalignments.
Thedrawbacksofthismethodareasfollows.Semantic
similaritybetween
entities
is
not
explored.
It
ignores
cer
tain distantly placed aligned pairs since segments are
constructed fromneighborofanchors.Onlyexact string
match of concepts, properties and instances are consi
deredasanchorswhichleadtoinefficientalignments.
2.3 Parallel Matching Technique
A straightforwardmethod to increase the efficiency of
largescalematching is toexecutematcher inparallelon
severalprocessors.Thetwokindsofparallelmatchingare
intermatcher and intramatcher parallelization. Inter
matcher parallelization dealswith parallel execution of
independently executablematchers while intramatcher
parallelizationdeals
with
internal
decomposition
of
indi
vidualmatchersormatcherpartsintoseveralmatchtasks
thatcanbeexecuted inparallel.Grossetal.[7]matching
system implementsbothintermatcherandintramatcher
parallelizationwhichwillbediscussedbelow
Gross et al. proposed a parallel ontology matching
system with a distributed infrastructure to incorporate
intramatcherandintermatcherparallelism.Theelement
level and structurelevelmatching are also parallelized.
For intramatcher parallelism, a sizebased partitioning
algorithm hasbeen proposedby this system leading to
better loadbalancing, limitedmemoryconsumptionand
scalability without reducing the effectiveness of match
results.
The system first generates the context attributes for
each entity. Then the ontology is partitioned into set of
partitionbased on the sizebasedpartitioning algorithm
achievingintra
matcher
parallelism.
Now
the
partition
pairsareconstructedonefromeachofthetwoontologies.
Eachpartitionpair is assigned aprocessor.Within each
processorthepartitionpairisprocessedinparallelbythe
elementlevel,structurelevel, instancebasedmatchersso
as to achieve inter matcherparallelism.Thealignments
fromallthematchercanbeaggregatedtooutputthefinal
alignment.
The drawbacks of this method are as follows.The
matcherlibraryconsistsofonlyfewnumberofmatchers.
The partition algorithm uses only simple strategies for
partitioningwhichcanbeimproved.
2.4 Other Matching ToolsRiMOM [8] is the firstontologymatchingalgorithmde
velopedwithautomaticanddynamicselectionofmatch
ers for ontology alignment tasks. The input ontology
shouldbe inOWL format. Itconsiders lexical,structural
and instancesimilarities.Basedonthe featuresofthe in
put ontology and the predefined rules, appropriate
matchers are chosen to apply for thematching task.Ri
MOMconsistofsixstepsandareexplainedbelow.
OntologyPreprocessingandFeatureFactorsEstimation.
Foreachentityofbothontologies,generatethefeaturesof
theentitylikeitsname,label,children,etc.Thenthelabel
and
structure
similarity
of
the
entity
pairs
are
calculated
whichwillbeusedinthefollowingstep.
Strategy (Matcher)Selection.Thebasic ideaof strategy
selection is that, if two ontologies have some feature in
common, thenmatcherbased on these feature informa
tionareemployedwithhighweight;andifsomefeature
factorsaretwo low,then thesematchermay notbeem
ployed.Theentitiesarefirstlinguisticallymatched;struc
turalmatchingisonlyappliediftheschemasexhibitsuf
ficientstructuralsimilarity.
Single strategy execution. The selected strategies from
theabovestepisusedtofindthealignmentindependent
ly.Eachstrategyoutputsanalignmentresult.
Alignmentcombination.
In
this
phase,
RiMOM
combines
thealignmentresultsobtainedby theselectedstrategies.
The combination is conductedby a linear interpolation
method.
Similaritypropagation (optional). If the two ontologies
havehighstructuresimilarityfactor,RiMOMemploysto
findnewalignmentaccording to the structural informa
tion.
Alignment refinement. It refines the alignment results
fromtheprevioussteps.Itdefinedseveralheuristicrules
toremovetheunreliablealignments.
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TABLE 1Comparison of Matching Technique
*Notavailable**optional
The drawback of RiMOM is its inefficiency for dealing
withlarge
scale
ontologies.
Eventhough
it
shows
avery
good effectiveness for large scale ontologies it consume
longtimeand largeamountofmemorysince itdoesnot
incorporate search space reduction techniques like early
pruning,partitionofontologyorparalleltechnique.
ASMOV(Automated Semantic Matching of Ontologies
with Verification) [9] is an iterative ontologymatching
algorithm. The strength of ASMOV lies in the post
processing of the alignment to remove the alignments
whicharesemanticallyinconsistent.ItusesWordNetand
theUnifiedMedicalLanguageSystem(UMLS)toincrease
theeffectiveness.The inputontologyshouldbe inOWL
DLformat.
The
input
of
ASMOV
is
two
ontologies
and
an
optionalpredeterminedalignmentset.Thelexical,struc
tural and instance based similarity value between all
possiblepairsofentities,onefromeachofthetwoontol
ogies is calculated and is used as the optional input
alignmenttoadjustanycalculatedmeasures.Asimilarity
matrix containing the calculated similarity values for
everypairofentitiesisobtainedfromtheabovestep.For
eachentitychoose themaximumsimilarityvalueaspre
alignmentfromthesimilaritymatrix.
Thisprealignmentmustundergoaprocessofseman
ticverification,whichisanextensivepostprocessingto
eliminate potential inconsistencies among the set pre
alignment.Five
different
kinds
of
inconsistencies
are
checked.One such inconsistency rule isMultipleentity
correspondences,e.g., if twoalignments (a,b)and (b, c)
exist then theremustalsobealignment like (a, c), ifnot
the above two alignment cannotbe verified and hence
removed.Theoutputoftheabovestepisthesemantically
verified similaritymatrix,which is then testedagainsta
termination condition. If this condition is true,nomore
iteration is needed and the process stops.The resulting
alignmentisfinalalignmentset.
Thedrawbacksofthissystemareasfollows.Whenthe
given two ontologies are dissimilar the effectiveness is
decreased. Sometime semanticverification system elimi
natestoo
many
alignments
leading
to
less
recall.
For
each
iteration, theASMOVneedspolynomial time, thus lead
ingtoinefficiency.
AgreeementMaker[10]isaschemaandontologymatch
ing algorithm consisting of wide range ofmatcher for
lexicalandstructuralfeaturesoftheontology.Itprovides
bothserialandparallelmatcherworkflows.Thestrength
of the system lies inGUIwhich enable user to choose,
control and execute the iterativematchers and their re
sults.ThroughGUItheusercanchoosematcherfromthe
matcher librarybased on thematching granularity (ele
QOM Peukert
etal.
COMA++ Falcon
AO
Taxo
map
Anchor
Flood
Gross
etal.
RiMOM ASMOV Agreement
Maker
Inputas
XMLSchemas
No Yes Yes No No No No No No (Yes)
Inputas
Ontologies
Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
GUI No Yes Yes (Yes)** No No No No No Yes
Linguistic
Matcher
Yes Na* Yes Yes Yes Yes Yes (Yes) Yes Yes
Structural
Matcher
Yes Na Yes Yes Yes Yes Yes (Yes) Yes Yes
Instance
Matcher
Yes Na Yes No No No Yes (Yes) Yes Yes
Earlypruning Yes Yes No No No No No No No No
Schemaparti
tion
No No Yes Yes Yes Yes Yes No No No
Parallel
match
ingNo
(Yes)
No
No
No
No
Yes
No
No
No
Dyn.matcher
selection
No No No No No No No Yes No No
Mapping
reuse
No Yes Yes No No No No No No No
OAEIpartici
pation
No No Yes Yes Yes Yes No Yes Yes Yes
Useofexternal
dictionary
Yes No Yes No No No No Yes Yes Yes
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in Distributed computing, information security and ontology basedinformation retrieval. She is currently pursuing her Ph.D. in ontologybased information retreival systems.
Dr. G. Aghila working as Professor in Pondicherry University, Indiahas got a total of 21 years of teaching expereince. She has graduat-ed from Anna University Chennai, India. She has published nearly40 research papers in web crawlers, ontology based informationretrieval. She is currently guiding 8 Ph.D. scholars. She was in re-ceipt of schrneiger award. She is an expert in onology development.Her area of interest includes artificial intelligence, text mining andsemantic web technologies.
B. Sathiya is a post graduate student pursuing her M.Tech in Com-puter Science and Engineering (Distributed Computing Systemsspecialization) in Pondicherry Engineering College, Puducherry ,India.
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