Big-Data Analytics and Cloud Computing: Theory, Algorithms and Applications

178 Pages·2015·4.35 MB·English

by  Marcello Trovati

Marcello Trovati · Richard Hill
Ashiq Anjum · Shao Ying Zhu
Lu Liu Editors
Big-Data
Analytics
and Cloud
Computing
Theory, Algorithms and Applications
Big-Data Analytics and Cloud Computing

Marcello Trovati • Richard Hill (cid:129) Ashiq Anjum
Shao Ying Zhu (cid:129) Lu Liu
Editors
Big-Data Analytics
and Cloud Computing
Theory, Algorithms and Applications
123
Editors
MarcelloTrovati RichardHill
DepartmentofComputing DepartmentofComputing
andMathematics andMathematics
UniversityofDerby UniversityofDerby
Derby,UK Derby,UK
AshiqAnjum ShaoYingZhu
DepartmentofComputing DepartmentofComputing
andMathematics andMathematics
UniversityofDerby UniversityofDerby
Derby,UK Derby,UK
LuLiu
DepartmentofComputing
andMathematics
UniversityofDerby
Derby,UK
ISBN978-3-319-25311-4 ISBN978-3-319-25313-8 (eBook)
DOI10.1007/978-3-319-25313-8
LibraryofCongressControlNumber:2015958882
SpringerChamHeidelbergNewYorkDordrechtLondon
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Foreword
Among developments that have led to the domain of cloud computing, we may
considerthefollowing.Veryoften,theworkplaceisnowdistributedandpotentially
evenglobal.Next,thereis theever-increasinguse beingmadeof background‘big
data’.When data is producedin realtime anddynamicallyevolving,then a cloud
platform is highly beneficial. Next comes the wide range of platforms used for
access and use of data and information. In this picture, mobile and networked
platformsareprominent.Sotooarethevariedaspectsofpervasiveandubiquitous
computingandsystems.
Cloud platforms are the foundationsfor our physical and virtual environments
that are empowered increasingly by the Internet of Things. That is, the general
progressionthatenablessmartercitiesandotherrelateddevelopments.Amongthese
alsoarethesmartworkplaceandthesmartlearningenvironment.
Thisbookcollectstogethermanydiscussionandresearchtopicsrelatingtocloud
services,technologiesanddeployments.Includedarecloudserviceprovision,inte-
grationwithadvancedinteractivityandcloud-basedarchitecturesfortheprovision
oflarge-scaleanalytics.Sustainabilityplaysacrucialrole,especiallyinrelationto
data centres, data grids and other layers of middleware that can be central parts
ofourcomputeenvironmentanddataclouds.Thefollowinginspirationalquotation
wasvoicedbyChristianBelady,GeneralManager,DataCenterServices,Microsoft:
‘Dataisreallythenextformofenergy::: Iviewdataasjustamoreprocessedform
ofenergy’.
Thecontributionsinthisbookaimatkeepingonefullyabreastofthesebigdata
and closely related developments.Even more rewarding is to be actively engaged
insuchtechnologicalprogress.Itcanwellbethecasethatdividinglineseffectively
disappearinregardtouserandsupplierandproducerandconsumer,wherethelatter
becomestheprosumer.
The readercan enjoy this book’scontents, and draw inspirationand benefit, in
ordertobepartoftheseexcitingdevelopments.
BigDataLaboratory ProfessorFionnMurtagh
UniversityofDerby,UK
August2015
v

Preface
Overview andGoals
Data is being created around us at an increased rate, in a multitude of forms and
types.Mostoftheadvancesinallthescientificdisciplinesthathaveoccurredover
thelastdecadehavebeenbasedontheextraction,managementandassessmentof
informationto providecutting-edgeintelligence.This, in turn,has acceleratedthe
need,aswellastheproductionoflargeamountsofdata,otherwisereferredtoasbig
data.
Due to the diverse nature of big data, there is a constant need to develop,
test and apply theoreticalconcepts, techniquesand tools, to successfully combine
multidisciplinary approaches to address such a challenge. As such, theory is
continuously evolving to provide the necessary tools to enable the extraction of
relevantandaccurateinformation,tofacilitateafullermanagementandassessment
ofbigdata.
As a consequence, the current academic, R&D and professional environments
require an ability to access the latest algorithms and theoretical advance in big
datascience,toenabletheutilisationofthemostappropriateapproachestoaddress
challengesinthisfield.
Big-DataAnalyticsandCloudComputing:Theory,AlgorithmsandApplications
presents a series of leading edge articles that discuss and explore theoretical
concepts,principles,tools,techniquesanddeploymentmodelsinthecontextofBig
Data.
Keyobjectivesforthisbookinclude:
(cid:129) Capturingthestateoftheartinarchitecturalapproachestotheprovisionofcloud-
basedbigdataanalyticsfunctions
(cid:129) Identifyingpotentialresearchdirectionsand technologiesto facilitate the reali-
sationofemergingbusinessmodelsthroughbigdataapproaches
(cid:129) Providing relevant theoretical frameworks and the latest empirical research
findings
vii
viii Preface
(cid:129) Discussing real-worldapplicationsof algorithmsand techniquesto address the
challengesofbigdata-sets
(cid:129) Advancingunderstandingofthefieldofbigdatawithincloudenvironments
OrganisationandFeatures
Thisbookisorganisedintotwoparts:
(cid:129) PartIreferstothetheoreticalaspectsofbigdata,predictiveanalyticsandcloud-
basedarchitectures.
(cid:129) PartIIdiscussesapplicationsandimplementationsthatutilisebigdata incloud
architectures.
TargetAudiences
We have written this book to supporta numberof potentialaudiences. Enterprise
architectsand businessanalystswill bothhavea needto understandhow bigdata
can impact upon their work, by considering the potential benefits and constraints
made possible by adopting architectures that can support the analysis of massive
volumesofdata.
Similarly,businessleadersandIT infrastructuremanagerswillhaveadesireto
appreciate where cloud computingcan facilitate the opportunitiesaffordedby big
data analytics, both in terms of realising previously hidden insight and assisting
criticaldecision-makingwithregardtoinfrastructure.
Thoseinvolvedinsystemdesignandimplementationasapplicationdevelopers
will observe how the adoption of architectures that support cloud computing can
positivelyaffectthemeansbywhichcustomersaresatisfiedthroughtheapplication
ofbigdataanalytics.
Finally,asacollectionofthelatesttheoretical,practicalandevaluativeworkin
thefieldofbigdataanalytics,weanticipatethatthisbookwillbeofdirectinterest
toresearchersandalsouniversityinstructorsforadoptionasacoursetextbook.
Suggested Uses
Big-DataAnalyticsandCloudComputingcanbeusedasanintroductiontothetopic
ofbigdatawithincloudenvironments,andassuchthereaderisadvisedtoconsult
PartIforathoroughoverviewofthefundamentalconceptsandrelevanttheories.
PartIIillustratesbywayofapplicationcasestudies,real-worldimplementations
ofscenariosthatutilisebigdatatoprovidevalue.
Preface ix
Readerscanusethebookasa‘primer’iftheyhavenopriorknowledgeandthen
consultindividualchaptersat will as a reference text. Alternatively,for university
instructors, we suggest the following programme of study for a twelve-week
semesterformat:
(cid:129) Week1:Introduction
(cid:129) Weeks2–5:PartI
(cid:129) Weeks5–11:PartII
(cid:129) Week12:Assessment
Instructors are encouraged to make use of the various case studies within the
booktoprovidethestartingpointforseminarortutorialdiscussionsandasameans
ofsummativelyassessinglearnersattheendofthecourse.
Derby,UK MarcelloTrovati
RichardHill
AshiqAnjum
ShaoYingZhu
LuLiu