Table Of ContentAbdelkader Hameurlain
Wenny Rahayu
David Taniar (Eds.)
Data Management
9
5 in Cloud, Grid
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8
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6th International Conference, Globe 2013
Prague, Czech Republic, August 2013
Proceedings
123
Lecture Notes in Computer Science 8059
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CarnegieMellonUniversity,Pittsburgh,PA,USA
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Abdelkader Hameurlain Wenny Rahayu
David Taniar (Eds.)
Data Management
in Cloud, Grid
and P2P Systems
6th International Conference, Globe 2013
Prague, Czech Republic, August 28-29, 2013
Proceedings
1 3
VolumeEditors
AbdelkaderHameurlain
PaulSabatierUniversity
IRITInstitutdeRechercheenInformatiquedeToulouse
118,routedeNarbonne,31062ToulouseCedex,France
E-mail:[email protected]
WennyRahayu
LaTrobeUniversity
DepartmentofComputerScienceandComputerEngineering
Melbourne,VIC3086,Australia
E-mail:[email protected]
DavidTaniar
MonashUniversity
ClaytonSchoolofInformationTechnology
Clayton,VIC3800,Australia
E-mail:[email protected]
ISSN0302-9743 e-ISSN1611-3349
ISBN978-3-642-40052-0 e-ISBN978-3-642-40053-7
DOI10.1007/978-3-642-40053-7
SpringerHeidelbergDordrechtLondonNewYork
LibraryofCongressControlNumber:2013944289
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LNCSSublibrary:SL3–InformationSystemsandApplication,incl.Internet/Web
andHCI
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Preface
Globe is now an established conference on data management in cloud, grid and
peer-to-peer systems. These systems are characterized by high heterogeneity,
highautonomyanddynamicsofnodes,decentralizationofcontrolandlarge-scale
distributionofresources.Thesecharacteristicsbringnewdimensionsanddifficult
challenges to tackling data management problems. The still open challenges to
data management in cloud, grid and peer-to-peer systems are multiple, such
as scalability, elasticity, consistency, data storage, security and autonomic data
management.
The 6th International Conference on Data Management in Grid and P2P
Systems (Globe 2013) was held during August 28–29, 2013, in Prague, Czech
Republic. The Globe Conference provides opportunities for academics and in-
dustry researchers to present and discuss the latest data management research
and applications in cloud, grid and peer-to-peer systems.
Globe 2013 received 19 papers from 11 countries. The reviewing process led
to the acceptance of 10 papers for presentation at the conference and inclusion
inthis LNCSvolume.Eachpaperwasreviewedbyatleastthree ProgramCom-
mittee members. The selected papers focus mainly on data management (e.g.,
data partitioning, storage systems, RDF data publishing, querying linked data,
consistency), MapReduce applications, and virtualization.
The conference would not have been possible without the support of the
ProgramCommittee members, external reviewers, members of the DEXA Con-
ference Organizing Committee, and the authors. In particular, we would like to
thank GabrielaWagnerandRolandWagner(FAW, UniversityofLinz)for their
help in the realization of this conference.
June 2013 Abdelkader Hameurlain
Wenny Rahayu
David Taniar
Organization
Conference Program Chairpersons
Abdelkader Hameurlain IRIT, Paul Sabatier University, Toulouse,
France
David Taniar Clayton School of Information Technology,
Monash University, Clayton, Victoria,
Australia
Publicity Chair
Wenny Rahayu La Trobe University, Victoria, Australia
Program Committee
Philippe Balbiani IRIT, Paul Sabatier University, Toulouse,
France
Nadia Bennani LIRIS, INSA of Lyon, France
Djamal Benslimane LIRIS, Universty of Lyon, France
Lionel Brunie LIRIS, INSA of Lyon, France
Elizabeth Chang Digital Ecosystems & Business intelligence
Institute,CurtinUniversity,Perth,Australia
Qiming Chen HP Labs, Palo Alto, California, USA,
Alfredo Cuzzocrea ICAR-CNR, University of Calabria, Italy
Fr´ed´eric Cuppens Telecom, Bretagne, France
Bruno Defude Telecom INT, Evry, France
Kayhan Erciyes Ege University, Izmir, Turkey
Shahram Ghandeharizadeh University of Southern California, USA
Tasos Gounaris Aristotle University of Thessaloniki, Greece
Farookh Hussain University of Technology Sydney (UTS),
Sydney, Australia
Sergio Ilarri University of Zaragoza, Spain
Ismail Khalil Johannes Kepler University, Linz, Austria
Gildas Menier LORIA, University of South Bretagne, France
Anirban Mondal University of Delhi, India
Riad Mokadem IRIT, Paul Sabatier University, Toulouse,
France
VIII Organization
Franck Morvan IRIT, Paul Sabatier University, Toulouse,
France
Fa¨ıza Najjar National Computer Science School, Tunis,
Tunisia
Kjetil Nørv˚ag Norwegian University of Science and
Technology, Trondheim, Norway
Jean-Marc Pierson IRIT, Paul Sabatier University, Toulouse,
France
Claudia Roncancio LIG, Grenoble University, France
Florence Sedes IRIT, Paul Sabatier University, Toulouse,
France
Fabricio A.B. Silva Army Technological Center, Rio de Janeiro,
Brazi
M´ario J.G. Silva University of Lisbon, Portugal
Hela Skaf LINA, Nantes University, France
A. Min Tjoa IFS, Vienna University of Technology, Austria
Farouk Toumani LIMOS, Blaise Pascal University, France
Roland Wagner FAW, University of Linz, Austria
Wolfram W¨oß FAW, University of Linz, Austria
External Reviewers
Christos Doulkeridis University of Piraeus, Greece
Franck Ravat IRIT, Paul Sabatier University, Toulouse,
France
Raquel Trillo University of Zaragoza, Spain
Shaoyi Yin IRIT, Paul Sabatier University, Toulouse,
France
Table of Contents
Data Partitioning and Consistency
Data Partitioning for Minimizing Transferred Data in MapReduce ..... 1
Miguel Liroz-Gistau, Reza Akbarinia, Divyakant Agrawal,
Esther Pacitti, and Patrick Valduriez
Incremental Algorithms for Selecting Horizontal Schemas of Data
Warehouses: The Dynamic Case ................................... 13
Ladjel Bellatreche, Rima Bouchakri, Alfredo Cuzzocrea, and
Sofian Maabout
Scalable and Fully Consistent Transactions in the Cloud through
Hierarchical Validation ........................................... 26
Jon Grov and Peter Csaba O¨lveczky
RDF Data Publishing, Querying Linked Data, and
Applications
A Distributed Publish/Subscribe System for RDF Data............... 39
Laurent Pellegrino, Fabrice Huet, Fran¸coise Baude, and
Amjad Alshabani
An Algorithm for Querying Linked Data Using Map-Reduce........... 51
Manolis Gergatsoulis, Christos Nomikos, Eleftherios Kalogeros, and
Matthew Damigos
Effects of Network Structure Improvement on Distributed RDF
Querying ....................................................... 63
Liaquat Ali, Thomas Janson, Georg Lausen, and
Christian Schindelhauer
Deploying a Multi-interface RESTful Application in the Cloud......... 75
Erik Albert and Sudarshan S. Chawathe
Distributed Storage Systems and Virtualization
Using Multiple Data Stores in the Cloud: Challenges and Solutions..... 87
Rami Sellami and Bruno Defude
X Table of Contents
Repair Time in Distributed Storage Systems......................... 99
Fr´ed´eric Giroire, Sandeep Kumar Gupta, Remigiusz Modrzejewski,
Julian Monteiro, and St´ephane Perennes
Development and Evaluation of a Virtual PC Type Thin Client
System ......................................................... 111
Katsuyuki Umezawa, Tomoya Miyake, and Hiromi Goto
Author Index.................................................. 125
Data Partitioning for Minimizing Transferred
Data in MapReduce
Miguel Liroz-Gistau1, Reza Akbarinia1, Divyakant Agrawal2,
Esther Pacitti3, and Patrick Valduriez1
1 INRIA& LIRMM, Montpellier, France
{Miguel.Liroz Gistau,Reza.Akbarinia,Patrick.Valduriez}@inria.fr
2 University of California, SantaBarbara
[email protected]
3 UniversityMontpellier 2, INRIA& LIRMM, Montpellier, France
[email protected]
Abstract. ReducingdatatransferinMapReduce’sshufflephaseisvery
important because it increases data locality of reduce tasks, and thus
decreases the overhead of job executions. In the literature, several op-
timizations have been proposed to reduce data transfer between map-
persandreducers.Nevertheless,alltheseapproachesarelimitedbyhow
intermediate key-value pairs are distributed over map outputs. In this
paper, we address the problem of high data transfers in MapReduce,
and propose a technique that repartitions tuples of the input datasets,
and thereby optimizes the distribution of key-values over mappers, and
increases the data locality in reduce tasks. Our approach captures the
relationships between inputtuplesand intermediatekeysbymonitoring
the execution of a set of MapReduce jobs which are representative of
theworkload.Then,basedonthoserelationships,itassignsinputtuples
to the appropriate chunks. We evaluated our approach through experi-
mentation in a Hadoop deployment on top of Grid5000 using standard
benchmarks.Theresultsshowhighreductionindatatransferduringthe
shufflephase compared to NativeHadoop.
1 Introduction
MapReduce [4] has established itself as one of the most popular alternatives
for big data processing due to its programmingmodel simplicity and automatic
management of parallel execution in clusters of machines. Initially proposed by
Google to be used for indexing the web, it has been applied to a wide range
of problems having to process big quantities of data, favored by the popularity
of Hadoop [2], an open-source implementation. MapReduce divides the compu-
tation in two main phases, namely map and reduce, which in turn are carried
out by several tasks that process the data in parallel. Between them, there is
a phase, called shuffle, where the data produced by the map phase is ordered,
partitioned and transferred to the appropriate machines executing the reduce
phase.
A.Hameurlain,W.Rahayu,andD.Taniar(Eds.):Globe2013,LNCS8059,pp.1–12,2013.
(cid:2)c Springer-VerlagBerlinHeidelberg2013
