Studies in Big Data 42 Ali Emrouznejad · Vincent Charles Editors Big Data for the Greater Good Studies in Big Data Volume 42 Series editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland e-mail: [email protected] Theseries“StudiesinBigData”(SBD)publishesnewdevelopmentsandadvances in the various areas of Big Data- quickly and with a high quality. The intent is to coverthetheory,research,development,andapplicationsofBigData,asembedded inthefieldsofengineering,computerscience,physics,economicsandlifesciences. The books of the series refer to the analysis and understanding of large, complex, and/or distributed data sets generated from recent digital sources coming from sensorsorotherphysicalinstrumentsaswellassimulations,crowdsourcing,social networks or other internet transactions, such as emails or video click streams and others. The series contains monographs, lecture notes and edited volumes in Big Data spanning the areas of computational intelligence including neural networks, evolutionary computation, soft computing, fuzzy systems, as well as artificial intelligence, data mining, modern statistics and operations research, as well as self-organizing systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output. More information about this series at http://www.springer.com/series/11970 Ali Emrouznejad Vincent Charles (cid:129) Editors Big Data for the Greater Good 123 Editors Ali Emrouznejad Vincent Charles Operations andInformationManagement, Buckingham Business School AstonBusiness School University of Buckingham AstonUniversity Buckingham Birmingham UK UK ISSN 2197-6503 ISSN 2197-6511 (electronic) Studies in BigData ISBN978-3-319-93060-2 ISBN978-3-319-93061-9 (eBook) https://doi.org/10.1007/978-3-319-93061-9 LibraryofCongressControlNumber:2018942927 ©SpringerInternationalPublishingAG,partofSpringerNature2019 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpart of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission orinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilar methodologynowknownorhereafterdeveloped. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publicationdoesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfrom therelevantprotectivelawsandregulationsandthereforefreeforgeneraluse. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authorsortheeditorsgiveawarranty,expressorimplied,withrespecttothematerialcontainedhereinor for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictionalclaimsinpublishedmapsandinstitutionalaffiliations. Printedonacid-freepaper ThisSpringerimprintispublishedbytheregisteredcompanySpringerInternationalPublishingAG partofSpringerNature Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland Preface Today, individuals and organizations are changing the world with Big Data. Data hasbecomeanewsourceofimmenseeconomicandsocialvalue.Advancesindata miningandanalyticsandthemassiveincreaseincomputingpoweranddatastorage capacityhaveexpanded,byordersofmagnitude,thescopeofinformationavailable to businesses, government, and individuals. Hence, the explosive growth in data and Big Data Analytics has set the next frontier for innovation, competition, pro- ductivity, and well-being in almost every sector of our society, from industry to academia and the government. From ROI calculations, revenue impact analyses, and customer insights, to earthquake prediction and environmental scanning, sui- cide and crime prevention, fraud detection, better medical treatments, and poverty reduction—this is a reminder of all the good that can come from Big Data. But driving knowledge and value from today’s mountains of data also brings policies related to ethics, privacy, security, intellectual property, and liability to the fore- front,asthemainconcernontheagendaofpolicymakers.TohelpBigDatadeliver itssocietalpromiseswillrequirerevisedprinciplesofmonitoringandtransparency, thus new types of expertise and institutions. The present book titled ‘Big Data for the Greater Good’ brings together some ofthefascinatinguses,thought-provokingchanges,andbiggestchallengesthatBig Data can convey to our society. Along theory and applications, the book compiles theauthors’experiencessothatthesemaybeaggregatedforabetterunderstanding. This book should be of interest to both researchers in the field of Big Data and practitioners from various fields who intend to apply Big Data technologies to improve their strategic and operational decision-making process. Finally, we hope that this book is an invitation to more intensive reflection on Big Data as a source for the common and greater good. The book is well organized in nine chapters, contributed by authors from all around the globe: Austria, Brazil, Czech Republic, Denmark, France, Germany, Greece, Italy, The Netherlands, UK, and the USA. Chapter 1 provides an introduction to Big Data, as well as the role it currently plays and could further play in achieving outcomes for the ‘Greater Good’. This chapterdiscussesthemainliteratureonBigDatafortheGreaterGoodforinterested v vi Preface readerscomingfromdifferentdisciplines.Chapter2looksatthemeansthatcanbe used to extract value from Big Data, and to this end, it explores the intersection between Big Data Analytics and Ethnography. The authors advance that the two approaches to analysing data can complement each other to provide a better sense of the realities of the contexts researched. As such, Big Data Analytics and Ethnography together can assist in the creation of practical solutions that yield a greater societal value. Chapter3complementsthefirsttwochaptersandprovidesaglobaloverviewof what Big Data is, uncovers its origins, as well as discusses the various definitions of the same, along with the technologies, analysis techniques, issues, challenges, and trends related to Big Data. The chapter further examines the role and profile of the Data Scientist, by means of taking into consideration aspects such as func- tionality, academic background, and required skills. The authors inspect how Big Data is leading the world towards a new way of social construction, consumption, and processes. Chapters4and5dealwiththeapplicationofBigDatainthefieldofhealthcare. Chapter4focusesontheuseofdataforin-patientcaremanagementinhigh-acuity spaces, such as operating rooms, intensive care units, and emergency departments. Inaddition, itdiscusses avariety ofmathematical techniquestoassistinmanaging andmitigatingnon-actionablealarmsignalsonmonitoredpatients.Chapter5shows how the combination of novel biofeedback-based treatments producing large data setswithBigDataandCloud-Dewtechnologycancontributetothegreatergoodof patientswithbraindisorders.Thisapproachisaimedatoptimizingthetherapywith regardtothecurrentneedsofthepatient,improvingtheefficiencyofthetherapeutic process, and preventing patient from overstressing during the therapy. The pre- liminary results are documented using a case study confirming that the approach offers a viable way towards the greater good of the patients. In the context of increased efforts dedicated to research on Big Data in agri- culturalandfoodresearch,Chap.6focusesonthepresentationofaninnovativeand integrated Big Data e-infrastructure solution (AGINFRA+) that aims to enable the sharing of data, algorithms, and results in a scalable and efficient manner across differentbutinterrelatedresearchstudies,withanapplicationtotheagricultureand fooddomain.Theauthorspresentthreeusecasesforperformingagri-foodresearch with the help of the mentioned e-infrastructure. The chapter also analyses the new challenges and directions that will potentially arise for agriculture and food man- agement and policing. Chapter7aimstodemonstratethebenefitsofdatacollectionandanalysistothe maintenanceandplanningofcurrentandfuturelow-voltagenetworks.Theauthors review several agent-based modelling techniques and further present two case studies wherein these techniques are applied to energy modelling on a real low-voltage network in the UK. The chapter shows that Big Data Analytics of supply and demand can contribute to a more efficient usage of renewable sources, which will also result in cutting down carbon emissions. Preface vii Itiscommonnowadaysforcustomerstorecordtheirexperiencesintheformof online reviews and blogs. In Chap. 8, the authors examine the case of customer feedbackatlocal,state,andnationalparksintheNewYorkStateParkSystem.The chapterdiscussesthedesign,development,andimplementationofsoftwaresystems that can download, organize, and analyse the voluminous text from the online reviews, analyse them using Natural Language Processing algorithms to perform sentiment analysis and topic modelling, and finally provide facility managers actionable insights to improve visitor experience. Finally, Chap. 9 discusses the issue of data privacy, which has proven to be a challenge in the Big Data age, but which, nevertheless, can be addressed through moderncryptography.Therearetwotypesofsolutionstotacklesuchproblematic: onethatmakesdataitselfanonymous,butwhichdegradesthevalueofthedata,and theotheronethatusesComputationonEncryptedData.Thischapterintroducesthe latter and describes three prototype and pilot applications of the same within privacy-preserving statistics. The applications originate from R&D projects and collaborations between the Danish financial sector and Statistics Denmark. The chapters contributed to this book should be of considerable interest and provide our readers with informative reading. Birmingham, UK Ali Emrouznejad Buckingham, UK Vincent Charles July 2018 Contents 1 Big Data for the Greater Good: An Introduction . . . . . . . . . . . . . . . 1 Vincent Charles and Ali Emrouznejad 2 Big Data Analytics and Ethnography: Together for the Greater Good. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Vincent Charles and Tatiana Gherman 3 Big Data: A Global Overview. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 Celia Satiko Ishikiriyama and Carlos Francisco Simoes Gomes 4 Big Data for Predictive Analytics in High Acuity Health Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 John Zaleski 5 A Novel Big Data-Enabled Approach, Individualizing and Optimizing Brain Disorder Rehabilitation . . . . . . . . . . . . . . . . . . . . 101 Marketa Janatova, Miroslav Uller, Olga Stepankova, Peter Brezany and Marek Lenart 6 Big Data in Agricultural and Food Research: Challenges and Opportunities of an Integrated Big Data E-infrastructure . . . . . . . . 129 Pythagoras Karampiperis, Rob Lokers, Pascal Neveu, Odile Hologne, George Kakaletris, Leonardo Candela, Matthias Filter, Nikos Manouselis, Maritina Stavrakaki and Panagiotis Zervas 7 Green Neighbourhoods: The Role of Big Data in Low Voltage Networks’ Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 Danica Vukadinović Greetham and Laura Hattam ix x Contents 8 Big Data Improves Visitor Experience at Local, State, and National Parks—Natural Language Processing Applied to Customer Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 Hari Prasad Udyapuram and Srinagesh Gavirneni 9 Big Data and Sensitive Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 Kurt Nielsen