Skip to main content
SpringerLink
Log in
Book cover

Smart Sustainable Cities of the Future pp 133–188Cite as

Big Data Analytics and Context-Aware Computing: Core Enabling Technologies, Techniques, Processes, and Systems

  • Chapter
  • First Online:

Part of the book series: The Urban Book Series ((UBS))

Abstract

Data sensing, information processing, and networking technologies are being fast embedded into the very fabric of the contemporary city to enable the use of innovative solutions to overcome the challenges of sustainability and urbanization. This has been boosted by the new digital transition in ICT. Driving such transition predominantly are big data analytics and context-aware computing and their increasing amalgamation within a number of urban domains, especially as their functionality involve more or less the same core enabling technologies, namely sensing devices, cloud computing infrastructures, data processing platforms, middleware architectures, and wireless networks. Topical studies tend to only pass reference to such technologies or to largely focus on one particular technology as part of big data and context-aware ecosystems in the realm of smart cities. Moreover, empirical research on the topic, with some exceptions, is generally limited to case studies without the use of any common conceptual frameworks. In addition, relatively little attention has been given to the integration of big data analytics and context-aware computing as advanced forms of ICT in the context of smart sustainable cities. This endeavor is a first attempt to address these two major strands of ICT of the new wave of computing in relation to the informational landscape of smart sustainable cities. Therefore, the purpose of this study is to review and synthesize the relevant literature with the objective of identifying and distilling the core enabling technologies of big data analytics and context-aware computing as ecosystems in relevance to smart sustainable cities, as well as to illustrate the key computational and analytical techniques and processes associated with the functioning of such ecosystems. In doing so, we develop, elucidate, and evaluate the most relevant frameworks pertaining to big data analytics and context-aware computing in the context of smart sustainable cities, bringing together research directed at a more conceptual, analytical, and overarching level to stimulate new ways of investigating their role in advancing urban sustainability. In terms of originality, a review and synthesis of the technical literature have not been undertaken to date in the urban literature, and in doing so, we provide a basis for urban researchers to draw on a set of conceptual frameworks in future research. The proposed frameworks, which can be replicated and tested in empirical research, will add additional depth and rigor to studies in the field. We argue that big data analytics and context-aware computing are prerequisite technologies for the functioning of smart sustainable cities of the future, as their effects reinforce one another as to their efforts for bringing a whole new dimension to the operating and organizing processes of urban life in terms of employing a wide variety of big data and context-aware applications for advancing sustainability.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   279.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Ahvenniemi H, Huovila A, Pinto-Seppä I, Airaksinen M (2017) What are the differences between sustainable and smart cities?. Cities 60:234–245

    Google Scholar 

  • Al-Hader M, Rodzi A (2009) The smart city infrastructure development and monitoring. Theor Empirical Res Urban Manage 4(2):87–94

    Google Scholar 

  • Al Nuaimi E, Al Neyadi H, Nader M, Al-Jaroodi J (2015) Applications of big data to smart cities. J Internet Serv Appl 6(25):1–15

    Google Scholar 

  • Andreas B, Gelenbe E, Di Girolamo M, Giuliani G, De Meer H, Dang MQ, Pentikousis K (2010) Energy-efficient cloud computing. Comput J 53(7):1045–1051

    Article  Google Scholar 

  • Arkian HR, Diyanat A, Pourkhalili A (2017) MIST: fog-based data analytics scheme with cost-efficient resource provisioning for IoT crowdsensing applications. J Network Comput Appl 82: 152–165

    Google Scholar 

  • Azodolmolky S, Dimakis N, Mylonakis V, Souretis G, Soldatos J, Pnevmatikakis A, Polymenakos L (2005) Middleware for in-door ambient intelligence: the polyomaton system. In: Proceedings of the 2nd international conference on networking, next generation networking middleware (NGNM 2005), Waterloo

    Google Scholar 

  • Batty M, Axhausen KW, Giannotti F, Pozdnoukhov A, Bazzani A, Wachowicz M, Ouzounis G, Portugali Y (2012) Smart cities of the future. Eur Phys J 214:481–518

    Google Scholar 

  • Beloglazov A, Abawajy J, Buyya R (2012) Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener Comput Syst 28(5):755–768

    Article  Google Scholar 

  • Bettencourt LMA (2014) The uses of big data in cities Santa Fe Institute. Santa Fe, New Mexico

    Google Scholar 

  • Bettini C, Brdiczka O, Henricksen K, Indulska J, Nicklas D, Ranganathan A, Riboni D (2010) A survey of context modelling and reasoning techniques. J Pervasive Mob Comput Spec Issue Context Model Reasoning Manage 6(2):161–180

    Article  Google Scholar 

  • Bibri SE (2015a) The human face of ambient intelligence, cognitive, emotional, affective, behavioral, and conversational aspects. Springer, Berlin

    Google Scholar 

  • Bibri SE (2015b) The shaping of Ambient intelligence and the internet of things: historico-epistemic, socio-cultural, politico-institutional and eco-environmental dimensions. Springer, Berlin

    Book  Google Scholar 

  • Bibri SE, Krogstie J (2016a) On the social shaping dimensions of smart sustainable cities: a study in science, technology, and society. Sustain Cities Soc 29:219–246

    Article  Google Scholar 

  • Bibri SE, Krogstie J (2016b) Big data analytics and context-aware computing for smart sustainable cities of the future. NOBIDS Symp 1818:4–17

    Google Scholar 

  • Bibri SE, Krogstie J (2017a) Smart sustainable cities of the future: an extensive interdisciplinary literature review. Sustain Cities Soc 31:183–212

    Article  Google Scholar 

  • Bibri SE, Krogstie J (2017b) ICT of the new wave of computing for sustainable urban forms: their big data and context-aware augmented typologies and design concepts. Sustain Cities Soc 32:449–474

    Article  Google Scholar 

  • Bishop CM (2006) Pattern recognition and machine learning. Springer, Heidelberg

    Google Scholar 

  • Böhlen M, Frei H (2009) Ambient intelligence in the city: overview and new perspectives. In: Nakashima H, Aghajan H, Augusto JC (eds) Handbook of ambient intelligence and smart environments. Springer, New York, NY, pp 911–938

    Google Scholar 

  • Bonomi F, Milito R, Zhu J, Addepalli S (2012) Fog computing and its role in the internet of things. In: Proceedings of the first edition of the MCC workshop on mobile cloud computing, ser. MCC’12. ACM, pp 13–16

    Google Scholar 

  • Borkar V, Carey MJ, Li C (2012) Inside big data management: ogres, onions, or parfaits? In: Proceedings of the 15th international conference on extending database technology. ACM, pp 3–14

    Google Scholar 

  • Brogi A, Forti S (2017) QoS-aware deployment of IoT applications through the fog. IEEE Internet Things J (99):1–1

    Google Scholar 

  • Buyya R, Beloglazov A, Abawajy J (2010) Energy-efficient management of data center resources for cloud computing: a vision, architectural elements, and open challenges. Paper presented at the 2010 international conference on parallel and distributed processing techniques and applications, PDPTA 2010, Las Vegas, USA

    Google Scholar 

  • Chaudhuri S (2012) What next?: a half-dozen data management research goals for big data and the cloud. In Proceedings of the 31st symposium on principles of database systems. ACM, pp 1–4

    Google Scholar 

  • Chen G, Kotz D (2000) A survey of context-aware mobile computing research. Paper TR2000–381. Department of Computer Science, Darthmouth College

    Google Scholar 

  • Chen L, Nugent C (2009) Ontology-based activity recognition in intelligent pervasive environments. Int J Web Inf Syst 5(4):410–430

    Article  Google Scholar 

  • Chen H, Chiang RHL, Storey VC (2012) Business intelligence and analytics: from big data to big impact. MIS Q 36(4):1165–1188

    Google Scholar 

  • Chen M, Mao S, Liu Y (2014) Big data: a survey. Mob Networks Appl 19(2):171–209. (Springer, US)

    Google Scholar 

  • Chen F, Deng P, Wan J, Zhang D, Vasilakos AV, Rong X (2015) Data mining for the internet of things: literature review and challenges. Int J Distrib Sens Networks 501(431047):1–14

    Google Scholar 

  • Chourabi H, Nam T, Walker S, Gil-Garcia JR, Mellouli S, Nahon K, Pardo TA, Scholl HJ (2012) Understanding smart cities: an integrative framework. In: The 245th Hawaii international conference on system science (HICSS), HI, Maui, pp 2289–2297

    Google Scholar 

  • Coutaz J, Crowley JL, Dobson S, Garlan D (2005) Context is key. Commun ACM 48(3):49

    Article  Google Scholar 

  • Crang M, Graham S (2007) Sentient cities: ambient intelligence and the politics of urban space. Inf Commun Soc 10(6):789–817

    Article  Google Scholar 

  • Criel J, Claeys L (2008) A transdisciplinary study design on context-aware applications and environments, a critical view on user participation within calm computing. Observatorio (OBS*) J 5:057–077

    Google Scholar 

  • Crutzen CKM (2005) Intelligent ambience between heaven and hell. Inf Commun Ethics Soc 3(4):219–232

    Article  Google Scholar 

  • DeRen L, JianJun C, Yuan Y (2015) Big data in smart cities. Sci China Inf Sci 58:1–12

    Google Scholar 

  • Dey AK (2000) Providing architectural support for building context-aware applications. Ph.D. thesis, College of Computing, Georgia Institute of Technology

    Google Scholar 

  • Dey AK (2001) Understanding and using context. Pers Ubiquit Comput 5(1):4–7

    Article  Google Scholar 

  • Dey AK, Abowd GD, Salber D (2001) A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications. Human Comput Inter 16(2–4):97–166

    Article  Google Scholar 

  • Dodge M, Kitchin R (2007) The automatic management of drivers and driving spaces. Geoforum 38(2):264–275

    Google Scholar 

  • Eagle N, Pentland A (2006) Reality mining: sensing complex social systems. Pers Ubiquitous Comp 10:255

    Article  Google Scholar 

  • Fan W, Bifet A (2013) Mining big data: current status, and forecast to the future. ACM SIGKDD Explor Newsl 14(2):1–5

    Article  Google Scholar 

  • Fayyad UM, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases. Artif Intell Mag 17(3):37–54

    Google Scholar 

  • Forrester Research Inc. (2012) The future of data security and privacy: controlling big data, Forrester US

    Google Scholar 

  • Gokhale A, Schmidt DC, Nataralan B, Wang N (2002) Applying model-integrated computing to component middleware and enterprise applications. Commun ACM 45(10):65–70

    Article  Google Scholar 

  • Gunnarsdóttir K, Arribas-Ayllon M (2012) Ambient intelligence: a narrative in search of users. Lancaster University and SOCSI, Cardiff University, Cesagen

    Google Scholar 

  • Ji C, Li Y, Qiu W, Awada U, Li K (2012) Big data processing in cloud computing environments. In: Pervasive systems, algorithms and networks (ISPAN), 2012 12th international symposium on IEEE, pp 17–23

    Google Scholar 

  • Johanson B, Fox A, Winograd T (2002) The interactive workspaces project: experiences with ubiquitous computing rooms. IEEE Pervasive Comput Mag 1(2):67–75

    Article  Google Scholar 

  • José R, Rodrigues H, Otero N (2010) Ambient intelligence: beyond the inspiring vision. J Univ Comput Sci 16(12):1480–1499

    Google Scholar 

  • Hancke GP, de Carvalho e Silva B, Hancke Jr GP (2013) The role of advanced sensing in smart cities. Sensors 13(1):393–425.

    Google Scholar 

  • Kalyvas JR, Overly MR, Karlyn MA (2013) Cloud computing: a practical framework for managing cloud computing risk-part I. Intell Property Technol Law J 25(3)

    Google Scholar 

  • Kamberov R (2015) Using social paradigms in smart cities mobile context-aware computing. New University of Lisbon, NOVA IMS

    Google Scholar 

  • Karun KA, Chitharanjan K (2013) A review on hadoop—HDFS infrastructure extensions. In: IEEE, information & communication technologies (ICT), pp 132–137

    Google Scholar 

  • Katal A, Wazid M, Goudar R (2013) Big data: issues, challenges, tools and good practices. In: Proceedings of 6th international conference on contemporary computing (IC3), Noida, August 8–10. IEEE, US, pp 404–409

    Google Scholar 

  • Khan Z, Kiani SL (2012) A cloud-based architecture for citizen services in smart cities In: ITAAC Workshop 2012, pp 315–320. IEEE fifth international conference on utility and cloud computing (UCC), Chicago, IL, USA. IEEE

    Google Scholar 

  • Khan Z, Ludlow D, McClatchey R, Anjum A (2012) An architecture for integrated intelligence in urban management using cloud computing. J Cloud Comput Adv Syst Appl 1(1):1–14

    Article  Google Scholar 

  • Khan Z, Anjum A, Kiani SL (2013) Cloud based big data analytics for smart future cities. In: Proceedings of the 2013 IEEE/ACM 6th international conference on utility and cloud computing, IEEE computer society, pp 381–386

    Google Scholar 

  • Khan Z, Kiani SL, Soomro K (2014a) A framework for cloud-based context-aware information services for citizens in smart cities. J Cloud Comput Appl Adv Syst Appl 3(14):1–17

    Google Scholar 

  • Khan Z, Pervez Z, Ghafoor A (2014b) Towards cloud based smart cities data security and privacy management In: 2014 7th IEEE/ACM international conference on utility and cloud computing—SCCTSA Workshop, 8th–11th December, London, UK, pp 806–811

    Google Scholar 

  • Khan Z, Anjum A, Soomro K, Tahir MA (2015) Towards cloud based big data analytics for smart future cities. J Cloud Comput Adv Syst Appl 4(2)

    Google Scholar 

  • Kitchin R (2014) ‘The real-time city? Big data and smart urbanism. Geo J 79:1–14

    Google Scholar 

  • Kitchin R, Dodge M (2011) Code/space: Software and everyday life. MIT Press, Cambridge, MA

    Google Scholar 

  • Kloeckl K, Senn O, Ratti C (2012) Enabling the real-time city: LIVE Singapore! J Urban Tech 19(2):89–112

    Google Scholar 

  • Kramers A, Höjer M, Lövehagen N, Wangel J (2014) Smart sustainable cities: exploring ICT solutions for reduced energy use in cities. Environ Model Softw 56:52–62

    Article  Google Scholar 

  • Kumar A, Prakash A (2014) The role of big data and analytics in smart cities. Int J Sci Res (IJSR) 6(14):12–23

    Google Scholar 

  • Kyriazis D, Varvarigou T, Rossi A, White D, Cooper J (2014) Sustainable smart city IoT applications: heat and electricity management and eco-conscious cruise control for public transportation. In: Proceedings of the 2013 IEEE 14th international symposium and workshops on a world of wireless, mobile and multimedia networks (WoWMoM), Madrid, Spain, pp 1–5

    Google Scholar 

  • Lane ND, Eisenman SB, Musolesi M, Miluzzo E, Campbell AT (2008) Urban sensing systems: opportunistic or participatory? In: HotMobile’08: Proceedings of the 9th workshop on mobile computing systems and applications. ACM, NY, USA, pp 11–16

    Google Scholar 

  • Laney D (2001) 3-D data management: controlling data volume, velocity and variety. META Group Research Note

    Google Scholar 

  • Lee SH, Han JH, Leem YT, Yigitcanlar T (2008) Towards ubiquitous city: concept, planning, and experiences in the Republic of Korea. In: Yigitcanlar T, Velibeyoglu K, Baum S (eds) Knowledge-based urban development: planning and applications in the information era. IGI Global, Information Science Reference, Hershey, Pa, pp 148–169

    Chapter  Google Scholar 

  • Li DR, Wang S, Li DY (2006) Spatial data mining theories and applications. Science Press, Beijing

    Google Scholar 

  • Lindblom J, Ziemke T (2002) Social situatedness: Vygotsky and beyond. In: 2nd international workshop on epigenetic robotics: modeling cognitive development in robotic systems, Edinburgh, Scotland, pp 71–78

    Google Scholar 

  • Lu S, Li MR, Tjhi CW, Leen KK, Wang L, Li X, Ma D (2011) A framework for cloud-based large-scale data analytics and visualization: case study on multiscale climate data In: Proceedings of the 3rd IEEE international conference on cloud computing technology and science, Nov 29–Dec 1 2011, Divani Caravel, Athens, Greece, pp 618–622

    Google Scholar 

  • Lueg C (2002) Operationalizing context in context-aware artifacts: benefits and pitfalls. Human Technol Inter 5(2)

    Google Scholar 

  • Lyshevski SE (2001/2005) Nano-and microelectromechanical systems: fundamentals of nano- and microengineering. CRC Press, Boca Ratón, EUA

    Google Scholar 

  • Manzoor A, Patsakis C, Morris A, McCarthy J, Mullarkey G, Pham H, Clarke S, Cahill V, Bouroche M (2014) Citywatch: exploiting sensor data to manage cities better. Trans Emerg Telecommun Technol 25:64–80

    Google Scholar 

  • Mastelic T, Oleksiak A, Claussen H, Brandic I, Pierson JM, Vasilakos AV (2014) Cloud computing: survey on energy efficiency. ACM Comput Surv 47(2):1–36

    Google Scholar 

  • Mitchell T (1997) Machine learning. McGraw Hill

    Google Scholar 

  • Mohamed N, Al-Jaroodi J (2014) Real-time big data analytics: applications and challenges. In: High performance computing & simulation (HPCS), 2014 international conference, pp 305–310

    Google Scholar 

  • Nathalie M, Symeon P, Antonio P, Kishor T (2012) Combining cloud and sensors in a smart city environment. EURASIP J Wireless Commun Network 247:1–10

    Google Scholar 

  • Numhauser BMJ (2012) Fog computing introduction to a new cloud evolution. Escrituras silenciadas: paisaje como historiografía. Spain: University of Alcala. pp 111–126. ISBN 978-84-15595-84-7

    Google Scholar 

  • Numhauser BMJ (2013) XMPP distributed topology as a potential solution for fog computing. In: MESH 2013 the sixth international conference on advances in mesh networks

    Google Scholar 

  • Ostberg et al (2017) Reliable capacity provisioning for distributed cloud/edge/fog computing applications. In: Networks and Communications (EuCNC), 2017 European Conference

    Google Scholar 

  • Paspallis N (2009) Middleware-based development of context-aware applications with reusable components, Ph.D. Thesis, University of Cyprus

    Google Scholar 

  • Perera C, Zaslavsky A, Christen P, Georgakopoulos D (2014) Sensing as a service model for smart cities supported by internet of things. Trans Emerg Telecommun Technol 1–12

    Google Scholar 

  • Perttunen M, Riekki J, Lassila O (2009) Context representation and reasoning in pervasive computing: a review. Int J Multimedia Eng 4(4)

    Google Scholar 

  • Provost F, Fawcett T (2013) Data science for business. O’Reilly Media Inc, Sebastopol, CA

    Google Scholar 

  • Riva G, Vatalaro F, Davide F, Alcañiz M (2005, 2008). Ambient intelligence: the evolution of technology, communication and cognition towards the future of human-computer interaction, IOS Press. Amsterdam

    Google Scholar 

  • Russell S, Norvig P (1995) Artificial intelligence: a modern approach. Prentice-Hall Inc

    Google Scholar 

  • Schilit B, Adams N, Want R (1994) Context-aware computing applications. In: Proceedings of IEEE workshop on mobile computing systems and applications, Santa Cruz, CA, USA, pp 85–90

    Google Scholar 

  • Schmidt DC (2002) Middleware for real-time and embedded systems. Commun ACM 45(6):43–48

    Article  Google Scholar 

  • Schmidt C (2011) Context-aware computing, Berlin Inst. Technology Tech, pp 1–9. Viewed 16 Sept 2016. http://diuf.unifr.ch/pai/education/2002_2003/seminar/winter/ubicomp/02_Pervasive.pdf/

  • Schmidt A, Beigl M, Gellersen HW (1999) There is more to context than location. Comput Graph UK 23(6):893–901

    Google Scholar 

  • Shahrokni H, Årman L, Lazarevic D, Nilsson A, Brandt N (2015) Implementing smart urban metabolism in the Stockholm Royal Seaport: smart city SRS. J Ind Ecol 19(5):917–929

    Article  Google Scholar 

  • Shearer C (2000) The CRISP-DM model: the new blueprint for data mining. Journal of Data Warehousing 5(4):13–22

    Google Scholar 

  • Shepard M (ed) (2011) Sentient city: ubiquitous computing, architecture and the future of urban space. MIT Press, Cambridge

    Google Scholar 

  • Shin D (2009) Ubiquitous city: urban technologies, urban infrastructure and urban informatics. J Inf Sci 35(5):515–526

    Article  Google Scholar 

  • Singh J, Singla V (2015) Big data: tools and technologies in big data. Int J Comput Appl (0975–8887) 112(15)

    Google Scholar 

  • Solanas A, Pérez-Martínez PA, Martínez-Ballesté A, Patsakis C, Conti M, Vlachos IS, Perrea DN, Ramos V, Falcone F, Postolache O, Di Pietro R (2014) Smart health: a context-aware health paradigm within smart cities. IEEE Commun Mag 52(8):74–81

    Google Scholar 

  • Soldatos J, Dimakis N, Stamatis K, Polymenakos L (2007) A breadboard architecture for pervasive context-aware services in smart spaces: middleware components and prototype applications. Pers Ubiquit Comput 11(3):193–212

    Article  Google Scholar 

  • Straub D, Welke R (1998) Coping with systems risk: security planning models for management decision making. MIS Q 22(4):441–469

    Article  Google Scholar 

  • Strimpakou M, Roussak I, Pils C, Anagnostou M (2006) Compact: middleware for context representation and management in pervasive computing. Pervasive Comput Commun 2(3):229–245

    Article  Google Scholar 

  • Tsai CW, Lai CF, Chao HC, Vasilakos AV (2015) Big data analytics: a survey 2(21)

    Google Scholar 

  • Ulrich W (2008) Information, context, and critique: context awareness of the third kind. In: The 31st information systems research seminar in Scandinavia, Keynote talk presented to IRIS 31

    Google Scholar 

  • Vongsingthong S, Smanchat S (2014) Internet of things: a review of applications and technologies. Suranaree J Sci Technol 21(4)

    Google Scholar 

  • Voorsluys W, Broberg J, Buyya R (2011) Introduction to cloud computing. In: Buyya R, Broberg J, Goscinski A (eds) Cloud computing: principles and paradigms. Wiley Press, New York, pp 1–44

    Google Scholar 

  • Wright D, Gutwirth S, Friedewald M (2007) Shining light on the dark side of ambient intelligence. Foresight 9(2):46–59

    Article  Google Scholar 

  • Wu X, Zhu X, Wu GQ, Ding W (2014) Data mining with big data. IEEE Trans Knowl Data Eng 26(1):97–107

    Article  Google Scholar 

  • Xiaofeng M, Xiang C (2013) Big data management: concepts, techniques and challenges. J Comput Res Dev 1(98). (wright)

    Google Scholar 

  • Zanella A, Bui N, Castellani A, Vangelista L, Zorzi M (2014) Internet of things for smart cities. IEEE Internet Things J 1(1)

    Google Scholar 

  • Zhang C (2016) Fog and IoT: an overview of research opportunities. IEEE Internet Things J

    Google Scholar 

  • Zhang Y, Cao T, Tian X, Li S, Yuan L, Jia H, Vasilakos AV (2016) Parallel processing systems for big data: a survey. In: Proceedings of the IEEE, special issue on big data

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer and Information Science, Department of Urban Design and Planning, Norwegian University of Science and Technology, Trondheim, Norway

    Simon Elias Bibri

Authors
  1. Simon Elias Bibri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Simon Elias Bibri .

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Bibri, S.E. (2018). Big Data Analytics and Context-Aware Computing: Core Enabling Technologies, Techniques, Processes, and Systems. In: Smart Sustainable Cities of the Future. The Urban Book Series. Springer, Cham. https://doi.org/10.1007/978-3-319-73981-6_3

Download citation

Publish with us

Policies and ethics

Search

Navigation