Table Of ContentDevis Bianchini Valeria De Antonellis
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Roberto De Virgilio
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Transactions on
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Large-Scale
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Abdelkader Hameurlain • Josef Küng • Roland Wagner
Editors-in-Chief
Special Issue on Big Data and Open Data
123
Lecture Notes in Computer Science 8990
Commenced Publication in 1973
Founding and Former Series Editors:
Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen
Editorial Board
David Hutchison
Lancaster University, Lancaster, UK
Takeo Kanade
Carnegie Mellon University, Pittsburgh, PA, USA
Josef Kittler
University of Surrey, Guildford, UK
Jon M. Kleinberg
Cornell University, Ithaca, NY, USA
Friedemann Mattern
ETH Zurich, Zürich, Switzerland
John C. Mitchell
Stanford University, Stanford, CA, USA
Moni Naor
Weizmann Institute of Science, Rehovot, Israel
C. Pandu Rangan
Indian Institute of Technology, Madras, India
Bernhard Steffen
TU Dortmund University, Dortmund, Germany
Demetri Terzopoulos
University of California, Los Angeles, CA, USA
Doug Tygar
University of California, Berkeley, CA, USA
Gerhard Weikum
Max Planck Institute for Informatics, Saarbrücken, Germany
More information about this series at http://www.springer.com/series/8637
ü
Abdelkader Hameurlain Josef K ng
(cid:129)
Roland Wagner Devis Bianchini
(cid:129)
Valeria De Antonellis Roberto De Virgilio (Eds.)
(cid:129)
Transactions on
Large-Scale
Data- and Knowledge-
Centered Systems XIX
Special Issue on Big Data and Open Data
123
Editors-in-Chief
Abdelkader Hameurlain Roland Wagner
IRIT,Paul Sabatier University FAW,University ofLinz
Toulouse Linz
France Austria
JosefKüng
FAW,University ofLinz
Linz
Austria
Guest Editors
Devis Bianchini RobertoDe Virgilio
University ofBrescia Universityof RomeIII
Brescia Rome
Italy Italy
Valeria DeAntonellis
University ofBrescia
Brescia
Italy
ISSN 0302-9743 ISSN 1611-3349 (electronic)
Lecture Notesin ComputerScience
ISBN 978-3-662-46561-5 ISBN 978-3-662-46562-2 (eBook)
DOI 10.1007/978-3-662-46562-2
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SpringerHeidelbergNewYorkDordrechtLondon
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LNCS Transactions on Large-Scale Data-
and Knowledge-Centered Systems (TLDKS)
Special Issue on Big Data and Linked Open Data
Linked Data and Big Data have been featured in recent years due to growing interest.
Proper use of enabling technologies meant for these two kinds of data is a critical
success factor in the evolution of the Web. The Linked Data perspective inspired
researcheffortsforbuilding,maintaining,andexploitingtheWebasaglobaldatabase,
whereresourcesareidentified(bymeansofURIs),semanticallydescribed(bymeansof
RDF), and connected through RDF links. This perspective goes beyond the potential
ofWeb2.0,enablingpeopleandapplicationstodiscovernewlinkedinformationinan
unexpectedway,accordingtoanexplorativeperspective.BigDataemphasizesthefact
thatnewtechniquesandinfrastructuresarerequiredforthesustainableexploitationofa
hugeamountofdata.TheLinkedDataparadigmisoftenseenasanapproachtocoping
withBigData,asitmovestheattentionfromaWebofdocumentstoaWebofrichdata.
Nevertheless,thegreatavailabilityofresourcesraisesdatamanagementissues,that
must be faced in a dynamic, highly distributed, and heterogeneous environment, such
astheWeb:(i)howtomodellargeamountsof(linked)data,(ii)howtoquerydataand
reasonontheminafeasibleway,(iii)howtoexploitBigandLinkedDataapplications
in real-worldscenarios. To solve these issues means toexploit thesynergism between
the conceptual foundations of data management and logical foundations of Big and
Linked Data initiatives. At the same time, emerging Big Data technologies could be
useful in addressing data management issues within the Linked Data context. Among
them,moderndistributedtechnologiesbasedontheprinciplesofCAPtheorem,suchas
NoSQL DBMS and Map/Reduce data processing.
ThisSpecialIssuecollectsfourhigh-qualitypapersthataimatinvestigatingLinked
Data and Big Data interleaving issues under a data management perspective: (a) two
papers propose the application of clustering techniques for performing inference and
search over (linked) data sources; (b) a paper leverages graph analysis techniques to
enable application-level integration of institutional data; (c) a paper describes an
approachforprotectingusers’profiledatafromdisclosure,tampering,andimproperuse.
January 2015 Devis Bianchini
Valeria De Antonellis
Roberto De Virgilio
Editorial Board
Reza Akbarinia Inria, France
Bernd Amann LIP6 – UPMC, France
Dagmar Auer FAW, Austria
Stéphane Bressan National University of Singapore, Singapore
Francesco Buccafurri Università Mediterranea di Reggio Calabria, Italy
Qiming Chen HP Lab, USA
Tommaso Di Noia Politecnico di Bari, Italy
Dirk Draheim University of Innsbruck, Austria
Johann Eder Alpen-Adria-Universität Klagenfurt, Austria
Stefan Fenz Vienna University of Technology, Austria
Georg Gottlob Oxford University, UK
Anastasios Gounaris Aristotle University of Thessaloniki, Greece
Theo Härder Technical University of Kaiserslautern, Germany
Andreas Herzig IRIT, Paul Sabatier University, France
Hilda Kosorus FAW, Austria
Dieter Kranzlmüller Ludwig-Maximilians-Universität München,
Germany
Philippe Lamarre INSA Lyon, France
Lenka Lhotská Technical University of Prague, Czech Republic
Vladimir Marik Technical University of Prague, Czech Republic
Mukesh Mohania IBM Research, India
Franck Morvan IRIT, Paul Sabatier University, France
Kjetil Nørvåg Norwegian University of Science and Technology,
Norway
Gultekin Ozsoyoglu Case Western Reserve University, USA
Themis Palpanas Paris Descartes University, France
Torben Bach Pedersen Aalborg University, Denmark
Günther Pernul University of Regensburg, Germany
Klaus-Dieter Schewe University of Linz, Austria
David Taniar Monash University, Australia
A Min Tjoa Vienna University of Technology, Austria
Chao Wang Oak Ridge National Laboratory, USA
Reviewers
Ladjel Bellatreche ENSMA, France
Soon Ae Chun CSI/City University of New York, USA
Mirel Cosulschi University of Craiova, Romania
Philippe Cudre-Mauroux University of Fribourg, Switzerland
Peter Haase Fluid Operations AG, Germany
Prateek Jain IBM TJ Watson Research Center, USA
VIII Editorial Board
Jose-Norberto Mazón University of Alicante, Spain
Marina Mongiello Technical University of Bari, Italy
Simon Scerri Digital Enterprise Research Institute, Galway,
Ireland
Steffen Staab University of Koblenz-Landau, Germany
Wolfram Woess Johannes Kepler University Linz, Austria
Fouad Zablith American University of Beirut, Lebanon
Contents
Structure Inference for Linked Data Sources Using Clustering . . . . . . . . . . . 1
Klitos Christodoulou, Norman W. Paton, and Alvaro A.A. Fernandes
The Web Within: Leveraging Web Standards and Graph Analysis
to Enable Application-Level Integration of Institutional Data . . . . . . . . . . . . 26
Luiz Gomes Jr. and André Santanchè
Dimensional Clustering of Linked Data: Techniques and Applications. . . . . . 55
Alfio Ferrara, Lorenzo Genta, Stefano Montanelli, and Silvana Castano
ProProtect3: An Approach for Protecting User Profile Data
from Disclosure, Tampering, and Improper Use in the Context
of WebID. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
Stefan Wild, Fabian Wiedemann, Sebastian Heil, Alexey Tschudnowsky,
and Martin Gaedke
Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
Structure Inference for Linked Data Sources
Using Clustering
B
Klitos Christodoulou( ), Norman W. Paton, and Alvaro A.A. Fernandes
School of Computer Science, University of Manchester,
Oxford Road, Manchester M13 9PL, UK
{christodoulou,norm,alvaro}@cs.man.ac.uk
Abstract. Linked Data (LD) overlays the World Wide Web of docu-
mentswithaWebofData.Thisisbecomingsignificantasshowninthe
growth of LD repositories available as part of the Linked Open Data
(LOD) cloud. At the instance-level, LD sources use a combination of
terms from various vocabularies, expressed as RDFS/OWL, to describe
data and publish it to the Web. However, LD sources do not organ-
ise data to conform to a specific structure analogous to a relational
schema; instead data can adhere to multiple vocabularies. Expressing
SPARQL queries over LD sources – usually over a SPARQL endpoint
thatispresentedtotheuser–requiresknowledgeofthepredicatesused
so as to allow queries to express user requirements as graph patterns.
Although LD provides low barriers to data publication using a single
language(i.e.,RDF),sourcesorganisedatawithdifferentstructuresand
terminologies.Thispaperdescribesanapproachtoautomaticallyderive
structural summaries over instance-level data expressed as RDF triples.
The technique builds on a hierarchical clustering algorithm that organ-
ises RDF instance-level data into groups that are then utilised to infer
a structural summary over a LD source. The resulting structural sum-
mariesareexpressedintheformofclasses,propertiesand,relationships.
Ourexperimentalevaluationshowsgoodresultswhenappliedtodifferent
types of LD sources.
· · ·
Keywords: Schema Linked Data Clustering Query formulation
1 Introduction
In recent years there has been a significant growth in the amount of publicly
available structured data on the Web using a graph-based representation model
and a set of simple principles, the so-called Linked Data Principles [3]. A moti-
vation for the adoption of these principles is the fact that they are based upon
established web infrastructures (like URIs and HTTP) and semantic web stan-
dards (like RDF and RDFS), thus providing low barriers to data publication.
The adoption of these principles is apparent in the number of Linked Data
(LD) repositories that form the Linked Open Data (LOD) cloud1. An interest-
ingaspectofthiskindofWebisthatdatasetsarenotonlypublishedinisolation
1 http://lod-cloud.net.
(cid:2)c Springer-VerlagBerlinHeidelberg2015
A.Hameurlainetal.(Eds.):TLDKSXIX,LNCS8990,pp.1–25,2015.
DOI:10.1007/978-3-662-46562-21
