Skip to main content
SpringerLink
Log in

Mobile Big Data: Foundations, State of the Art, and Future Directions

  • Reference work entry
  • First Online:
Encyclopedia of Big Data Technologies

  • 47 Accesses

Abstract

Emerging ubiquitous mobile services and applications have unveiled the Mobile Big Data era in which the large volumes of data derived from mobile Internet traffics become valuable sources to support various personal and public services such as personal recommendation services, spatiotemporal event detection, social behavior analytics, network resource planning, and many more. While data scientists are aware of the value of Mobile Big Data, they also need to understand the challenges involved. In general, Mobile Big Data derives from different sources, including the mobile application services, the network services, and the mobile devices themselves. The heterogeneous data sources and the data acquisition paths can influence the quality of data and can further influence the overall performance of the big data services. In order to address the major research trends, this chapter provides a state-of-the-art discussion of concept, management technology, and challenges in Mobile Big Data.

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

Access this chapter

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 849.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 999.99
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

Institutional subscriptions

References

  • Algizawy E, Ogawa T, El-Mahdy A (2017) Real-time large-scale map matching using mobile phone data. ACM Trans Knowl Discov Data (TKDD) 11(4):52

    Google Scholar 

  • Astarita V, Giofrè VP, Vitale A (2016) A cooperative intelligent transportation system for traffic light regulation based on mobile devices as floating car data (FCD). Am Sci Res J Eng Technol Sci (ASRJETS) 19(1): 166–177

    Google Scholar 

  • Bellairs J, Hlozek J, Egan T, Kuttel M (2016) An eHealth android application for mobile analysis of microplate assays. In: 2016 IST-Africa week conference. IEEE, pp 1–8

    Google Scholar 

  • Bermudez-Edo M, Elsaleh T, Barnaghi P, Taylor K (2017) IoT-lite: a lightweight semantic model for the Internet of things and its use with dynamic semantics. Pers Ubiquit Comput 21(3):475–487

    Article  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. ACM, pp 13–16

    Google Scholar 

  • Chang C, Srirama NS, Buyya R (2016) Mobile cloud business process management system for the internet of things: a survey. ACM Comput Surv (CSUR) 49(4):70

    Article  Google Scholar 

  • Chang C, Srirama NS, Buyya R (2017) Indie fog: an efficient fog-computing infrastructure for the internet of things. Computer 50(9):92–98

    Article  Google Scholar 

  • Chen X, Jiao L, Li W, Fu X (2016) Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Trans Netw 24(5):2795–2808

    Article  Google Scholar 

  • Cheng Y-C, Chawathe Y, LaMarca A, Krumm J (2005) Accuracy characterization for metropolitan-scale Wi-Fi localization. In: Proceedings of the 3rd international conference on mobile systems, applications, and services. ACM, pp 233–245

    Google Scholar 

  • Cheng X, Fang L, Hong X, Yang L (2017) Exploiting mobile big data: sources, features, and applications. IEEE Netw 31(1):72–79

    Article  Google Scholar 

  • Chittaranjan G, Blom J, Gatica-Perez D (2013) Mining large-scale smartphone data for personality studies. Pers Ubiquit Comput 17(3):433–450

    Article  Google Scholar 

  • Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2016–2021 (2017). https://www. cisco.com/c/en/us/solutions/collateral/service-provider/ visual-networking-index-vni/mobile-white-paper-c11- 520862.pdf. Accessed 19 Oct 2017

  • Conti M, Giordano S, May M, Passarella A (2010) From opportunistic networks to opportunistic computing. IEEE Commun Mag 48(9):126

    Article  Google Scholar 

  • El Khaddar MA, Harroud H, Boulmalf M, Elkoutbi M, Habbani A (2012) Emerging wireless technologies in e-health trends, challenges, and framework design issues. In: 2012 international conference on multimedia computing and systems (ICMCS). IEEE, pp 440–445

    Google Scholar 

  • Ericsson Mobility Report. June 2017. Publisher: Niklas Heuveldop (2017). https://www.ericsson.com/assets/ local/mobility-report/documents/2017/ericsson-mobil- ity-report-june-2017.pdf. Accessed 19 Oct 2017

  • Gandomi A, Haider M (2015) Beyond the hype: big data concepts, methods, and analytics. Int J Inf Manage 35(2):137–144

    Article  Google Scholar 

  • Gardner-Stephen P, Challans R, Lakeman J, Bettison A, Gardner-Stephen D, Lloyd M (2013) The serval mesh: a platform for resilient communications in disaster & crisis. In: 2013 IEEE global humanitarian technology conference (GHTC). IEEE, pp 162–166

    Google Scholar 

  • Guo J, Song B, Yu RF, Yan Z, Yang TL (2017) Object detection among multimedia big data in the compressive measurement domain under mobile distributed architecture. Futur Gener Comput Syst 76:519–527

    Article  Google Scholar 

  • Hadachi A, Batrashev O, Lind A, Singer G, Vainikko E (2014) Cell phone subscribers mobility prediction using enhanced Markov chain algorithm. In: 2014 IEEE intelligent vehicles symposium proceedings. IEEE, pp 1049–1054

    Google Scholar 

  • Hajji W, Tso PF (2016) Understanding the performance of low power raspberry pi cloud for big data. Electronics 5(2):29

    Article  Google Scholar 

  • Herring R, Hofleitner A, Abbeel P, Bayen A (2010) Estimating arterial traffic conditions using sparse probe data. In: 2010 13th international IEEE conference on intelligent transportation systems (ITSC). IEEE, pp 929–936

    Google Scholar 

  • IBM (2017) Edge analytics cookbook. https://developer. ibm.com/iotplatform/resources/edge-analytics-cook- book/. Accessed 12 Oct 2017

  • Jin X, Wah BW, Cheng X, Wang Y (2015) Significance and challenges of big data research. Big Data Res 2(2):59–64

    Article  Google Scholar 

  • Krempl G, Žliobaite I, BrzeziÅ„ski D, Hüllermeier E, Last M, Lemaire V, Noack T, Shaker A, Sievi S, Spiliopoulou M et al (2014) Open challenges for data stream mining research. ACM SIGKDD Explor Newsl 16(1):1–10

    Article  Google Scholar 

  • Kwon L, Long K, Wan Y, Yu H, Cunningham B (2016) Medical diagnostics with mobile devices: comparison of intrinsic and extrinsic sensing. Biotechnol Adv 34(3):291–304

    Article  Google Scholar 

  • LaValle S, Lesser E, Shockley R, Hopkins SM, Kruschwitz N (2011) Big data, analytics and the path from insights to value. MIT Sloan Manag Rev 52(2):21

    Google Scholar 

  • Lee CK, Chung N (2009) Understanding factors affecting trust in and satisfaction with mobile banking in Korea: a modified DeLone and McLean’s model perspective. Interact Comput 21(5–6):385–392

    Article  Google Scholar 

  • Lind A, Hadachi A, Batrashev O (2017a) A new approach for mobile positioning using the CDR data of cellular networks. In: 2017 5th IEEE international conference on models and technologies for intelligent transportation systems (MT-ITS). IEEE, pp 315–320

    Google Scholar 

  • Lind A, Hadachi A, Piksarv P, Batrashev O (2017b) Spatio-temporal mobility analysis for community detection in the mobile networks using CDR data. In: 2017 9th international congress on ultra modern telecommunications and control systems (ICUMT)

    Google Scholar 

  • Liyanage M, Chang C, Srirama NS (2016) MePaaS: mobile-embedded platform as a service for distributing fog computing to edge nodes. In: 2016 17th international conference on parallel and distributed computing, applications and technologies (PDCAT). IEEE, pp 73–80

    Google Scholar 

  • Mijumbi R, Serrat J, Gorricho J-L, Bouten N, De Turck F, Boutaba R (2016) Network function virtualization: state-of-the-art and research challenges. IEEE Commun Surv Tutorials 18(1):236–262

    Article  Google Scholar 

  • Nakamoto S (2008) Bitcoin: a peer-to-peer electronic cash system. Retrieved from https://bitcoin.org/bitcoin.pdf

  • Neumeyer L, Robbins B, Nair A, Kesari A (2010) S4: distributed stream computing platform. In: 2010 IEEE international conference on data mining workshops (ICDMW). IEEE, pp 170–177

    Google Scholar 

  • Paniagua C, Flores H, Srirama NS (2012) Mobile sensor data classification for human activity recognition using MapReduce on cloud. Proc Comput Sci 10: 585–592

    Article  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 25(1):81–93

    Article  Google Scholar 

  • Rathore MM, Ahmad A, Paul A, Rho S (2016) Urban planning and building smart cities based on the internet of things using big data analytics. Comput Netw 101:63–80

    Article  Google Scholar 

  • Rueppel U, Stuebbe MK (2008) BIM-based indoor-emergency-navigation-system for complex buildings. Tsinghua Sci Technol 13:362–367

    Article  Google Scholar 

  • Samadi Y, Zbakh M (2017) Threshold-based load balancing algorithm for big data on a cloud environment. In: Proceedings of the 2nd international conference on big data, cloud and applications. ACM, p 18

    Google Scholar 

  • Scannapieco M, Virgillito A, Marchetti C, Mecella M, Baldoni R (2004) The daquincis architecture: a platform for exchanging and improving data quality in cooperative information systems. Inf Syst 29(7): 551–582

    Article  Google Scholar 

  • Schantz ER, Loyall PJ, Rodrigues C, Schmidt CD, Krishnamurthy Y, Pyarali I (2003) Flexible and adaptive QoS control for distributed real-time and embedded middleware. In: Proceedings of the ACM/IFIP/USENIX 2003 international conference on middleware. Springer, New York, pp 374–393

    Google Scholar 

  • Shen W-L, Chen C-S, Lin CK-J, Hua AK (2014) Autonomous mobile mesh networks. IEEE Trans Mobile Comput 13(2):364–376

    Article  Google Scholar 

  • Sikder R, Uddin MJ, Halder S (2016) An efficient approach of identifying tourist by call detail record analysis. In: International workshop on computational intelligence (IWCI). IEEE, pp 136–141

    Google Scholar 

  • ur Rehman HM, Liew SC, Abbas A, Jayaraman PP, Wah YT, Khan US (2016) Big data reduction methods: a survey. Data Sci Eng 1(4):265–284. [Online]. Available https://doi.org/10.1007/s41019-016-0022-0

    Article  Google Scholar 

  • ur Rehman HM, Liew SC, Wah YT, Khan KM (2017) Towards next-generation heterogeneous mobile data stream mining applications: opportunities, challenges, and future research directions. J Netw Comput Appl 79:1–24

    Article  Google Scholar 

  • Wang K, Shao Y, Shu L, Zhu C, Zhang Y (2016) Mobile big data fault-tolerant processing for eHealth networks. IEEE Netw 30(1):36–42

    Article  Google Scholar 

  • Yang L, Cao J, Yuan Y, Li T, Han A, Chan A (2013) A framework for partitioning and execution of data stream applications in mobile cloud computing. ACM SIGMETRICS Perform Eval Rev 40(4): 23–32

    Article  Google Scholar 

  • Zandbergen AP (2009) Accuracy of iPhone locations: a comparison of assisted GPS, WiFi and cellular positioning. Trans GIS 13(s1):5–25

    Article  Google Scholar 

  • Zhang X, Yi Z, Yan Z, Min G, Wang W, Elmokashfi A, Maharjan S, Zhang Y (2016a) Social computing for mobile big data. Computer 49(9): 86–90

    Article  Google Scholar 

  • Zhang M, Xu F, Li Y (2016b) Mobile traffic data decomposition for understanding human urban activities. In: 2016 IEEE 13th international conference on mobile ad hoc and sensor systems (MASS). IEEE, pp 1–9

    Google Scholar 

Download references

Acknowledgements

The work is supported by the Estonian Centre of Excellence in IT (EXCITE), funded by the European Regional Development Fund.

Author information

Authors and Affiliations

  1. Mobile & Cloud Lab, Institute of Computer Science, University of Tartu, Tartu, Estonia

    Chii Chang & Satish Narayana Srirama

  2. Institute of Computer Science, University of Tartu, Tartu, Estonia

    Amnir Hadachi

  3. Thomas Johann Seebeck Institute of Electronics, Tallinn University of Technology, Tallinn, Estonia

    Mart Min

Authors
  1. Chii Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amnir Hadachi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Satish Narayana Srirama
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mart Min
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Satish Narayana Srirama .

Editor information

Editors and Affiliations

  1. Institute of Computer Science, University of Tartu, Tartu, Estonia

    Sherif Sakr

  2. School of Information Technologies, Sydney University, Sydney, Australia

    Albert Y. Zomaya

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Chang, C., Hadachi, A., Srirama, S.N., Min, M. (2019). Mobile Big Data: Foundations, State of the Art, and Future Directions. In: Sakr, S., Zomaya, A.Y. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-77525-8_46

Download citation

Publish with us

Policies and ethics

Search

Navigation