Data Science and Big Data Computing: Frameworks and Methodologies

332 Pages·2016·2.998 MB·English

by  Zaigham Mahmood (eds.)

Zaigham Mahmood Editor
Data Science
and Big Data
Computing
Frameworks and Methodologies
Data Science and Big Data Computing
ThiSisaFMBlankPage
Zaigham Mahmood
Editor
Data Science and Big Data
Computing
Frameworks and Methodologies
Editor
ZaighamMahmood
DepartmentofComputingandMathematics
UniversityofDerby
Derby,UK
BusinessManagementandInformaticsUnit
NorthWestUniversity
Potchefstroom,SouthAfrica
ISBN978-3-319-31859-2 ISBN978-3-319-31861-5 (eBook)
DOI10.1007/978-3-319-31861-5
LibraryofCongressControlNumber:2016943181
©SpringerInternationalPublishingSwitzerland2016
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To
Rehana Zaigham Mahmood:
For her Love and Support
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Preface
Overview
Huge volumes of data are being generated by commercial enterprises, scientific
domains and general public. According to a recent report by IBM, we create 2.5
quintillion bytes of data every day. According to another recent research, data
productionwillbe44timesgreaterin2020thanitwasin2009.
Data being a vital organisational resource, its management and analysis is
becoming increasingly important: not just for business organisations but also for
other domains including education, health, manufacturing and many other sectors
ofourdailylife.Thisdata,duetoitsvolume,varietyandvelocity,oftenreferredto
asBigData,isnolongerrestrictedtosensoryoutputsandclassicaldatabases;italso
includes highly unstructured data in the form of textual documents, webpages,
photos,spatialandmultimediadata,graphicalinformation,socialmediacomments
andpublicopinions.SinceBigDataischaracterisedbymassivesamplesizes,high-
dimensionalityandintrinsicheterogeneity,andsincenoiseaccumulation,spurious
correlation and incidental endogeneity are common features of such datasets,
traditional approaches to data management, visualisation and analytics are no
longersatisfactorilyapplicable.There istherefore anurgentneed fornewertools,
better frameworks and workable methodologies for such data to be appropriately
categorised,logicallysegmented,efficientlyanalysedandsecurelymanaged.This
requirementhasresultedinanemergingnewdisciplineofDataSciencethatisnow
gaining much attention with researchers and practitioners in the field of Data
Analytics.
AlthoughthetermsBigDataandDataScienceareoftenusedinterchangeably,
the two concepts have fundamentally different roles to play. Whereas Big Data
referstocollectionandmanagementoflargeamountsofvarieddatafromdiverse
sources,DataSciencelookstocreatingmodelsandprovidingtools,techniquesand
scientific approaches to capture the underlying patterns and trends embedded in
thesedatasets,mainlyforthepurposesofstrategicdecisionmaking.
vii
viii Preface
Inthiscontext,thisbook,DataScienceandBigDataComputing:Frameworks
andMethodologies,aimstocapturethestateoftheartandpresentdiscussionsand
guidance on the current advances and trends in Data Science and Big Data Ana-
lytics.Inthisreferencetext,36researchersandpractitionersfromaroundtheworld
havepresentedlatestresearchdevelopments,frameworksandmethodologies,cur-
renttrends, stateoftheartreports,casestudiesandsuggestionsforfurther under-
standinganddevelopmentoftheDataScienceparadigmandBigDataComputing.
Objectives
The aim of this volume is to present the current research and future trends in the
development and use of methodologies, frameworks and the latest technologies
relatingtoDataScience,BigDataandDataAnalytics.Thekeyobjectivesinclude:
• CapturingthestateoftheartresearchandpracticerelatingtoDataScienceand
BigData
• Analysingthe relevanttheoreticalframeworks, practical approaches andmeth-
odologiescurrentlyinuse
• Discussingthelatestadvances,currenttrendsandfuturedirectionsinthesubject
areasrelatingtoBigData
• Providing guidance and best practices with respect to employing frameworks
andmethodologiesforefficientandeffectiveDataAnalytics
• In general, advancing the understanding of the emerging new methodologies
relevanttoDataScience,BigDataandDataAnalytics
Organisation
Thereare13chaptersinDataScienceandBigDataComputing:Frameworksand
Methodologies.Theseareorganisedinthreeparts,asfollows:
• PartI:DataScienceApplicationsandScenarios.ThissectionhasafocusonBig
Data (BD) applications. There are four chapters. The first chapter presents a
framework for fast data applications, while the second contribution suggests a
techniqueforcomplexevent processing forBDapplications. Thethirdchapter
focuses on agglomerative approaches for partitioning of networks in BD sce-
narios; and the fourth chapter presents a BD perspective for identifying
minimum-sizedinfluentialverticesfromlarge-scaleweightedgraphs.
• Part II: Big Data Modelling and Frameworks. This part of the book also
comprises four chapters. The first chapter presents a unified approach to data
modellingandmanagement,whereasthesecondcontributionpresentsadistrib-
utedcomputingperspectiveoninterfacingphysicalandcyberworlds.Thethird
chapter discusses machine learning in the context of Big Data, and the final
Preface ix
contributioninthissectionpresentsananalytics-drivenapproachtoidentifying
duplicaterecordsinlargedatarepositories.
• PartIII:BigDataToolsandAnalytics:Therearefivechaptersinthissectionthat
focus on frameworks, strategies and data analytics technologies. The first two
chapters present Apache and other enabling technologies and tools for data
mining. The third contribution suggests a framework for data extraction and
knowledgediscovery.Thefourthcontributionpresentsacasestudyforadaptive
decisionmaking;andthefinalchapterfocusesonsocialimpactandsocialmedia
analysisrelatingtoBigData.
Target Audiences
The current volume is a reference text aimed at supporting a number of potential
audiences,includingthefollowing:
• Datascientists,softwarearchitectandbusinessanalystswhowishtoadoptthe
newer approaches to Data Analytics to gain business intelligence to support
businessmanagers’strategicdecisionmaking
• Studentsandlecturerswhohaveaninterestinfurtherenhancingtheknowledge
ofDataScience;andtechnologies,mechanismsandframeworksrelevanttoBig
DataandDataAnalytics
• Researchersinthisfieldwhoneedtohaveup-to-dateknowledgeofthecurrent
practices,mechanismsandframeworksrelevanttoDataScienceandBigDatato
furtherdevelopthesame
Derby,UK ZaighamMahmood
Potchefstroom,SouthAfrica