Encyclopedia of Big Data Technologies

Living Edition
| Editors: Sherif Sakr, Albert Zomaya

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

  • Chii Chang
  • Amnir Hadachi
  • Satish Narayana Srirama
  • Mart Min
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-63962-8_46-1

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 to check access

Notes

Acknowledgements

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

References

  1. 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):52Google Scholar
  2. 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): 166–177Google Scholar
  3. 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 1–8Google Scholar
  4. 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):475–487Google Scholar
  5. 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 13–16Google Scholar
  6. 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):70Google Scholar
  7. Chang C, Srirama NS, Buyya R (2017) Indie fog: an efficient fog-computing infrastructure for the internet of things. Computer 50(9):92–98Google Scholar
  8. 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):2795–2808Google Scholar
  9. 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 233–245Google Scholar
  10. Cheng X, Fang L, Hong X, Yang L (2017) Exploiting mobile big data: sources, features, and applications. IEEE Netw 31(1):72–79Google Scholar
  11. Chittaranjan G, Blom J, Gatica-Perez D (2013) Mining large-scale smartphone data for personality studies. Pers Ubiquit Comput 17(3):433–450Google Scholar
  12. 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
  13. Conti M, Giordano S, May M, Passarella A (2010) From opportunistic networks to opportunistic computing. IEEE Commun Mag 48(9):126Google Scholar
  14. 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 440–445Google Scholar
  15. 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
  16. Gandomi A, Haider M (2015) Beyond the hype: big data concepts, methods, and analytics. Int J Inf Manage 35(2):137–144Google Scholar
  17. 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 162–166Google Scholar
  18. 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 76:519–527Google Scholar
  19. 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 1049–1054Google Scholar
  20. Hajji W, Tso PF (2016) Understanding the performance of low power raspberry pi cloud for big data. Electronics 5(2):29Google Scholar
  21. 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 929–936Google Scholar
  22. IBM (2017) Edge analytics cookbook. https://developer. ibm.com/iotplatform/resources/edge-analytics-cook- book/. Accessed 12 Oct 2017
  23. Jin X, Wah BW, Cheng X, Wang Y (2015) Significance and challenges of big data research. Big Data Res 2(2):59–64Google Scholar
  24. Krempl G, žliobaite I, Brzeziński 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):1–10Google Scholar
  25. 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):291–304Google Scholar
  26. 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):21Google Scholar
  27. 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):385–392Google Scholar
  28. 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 315–320Google Scholar
  29. 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
  30. 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 73–80Google Scholar
  31. 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):236–262Google Scholar
  32. Nakamoto S (2008) Bitcoin: a peer-to-peer electronic cash system. Retrieved from https://bitcoin.org/bitcoin.pdf
  33. 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 170–177Google Scholar
  34. Paniagua C, Flores H, Srirama NS (2012) Mobile sensor data classification for human activity recognition using MapReduce on cloud. Proc Comput Sci 10: 585–592Google Scholar
  35. 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):81–93Google Scholar
  36. 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 101:63–80Google Scholar
  37. Rueppel U, Stuebbe MK (2008) BIM-based indoor-emergency-navigation-system for complex buildings. Tsinghua Sci Technol 13:362–367Google Scholar
  38. 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 18Google Scholar
  39. 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): 551–582CrossRefGoogle Scholar
  40. 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 374–393Google Scholar
  41. Shen W-L, Chen C-S, Lin CK-J, Hua AK (2014) Autonomous mobile mesh networks. IEEE Trans Mobile Comput 13(2):364–376CrossRefGoogle Scholar
  42. 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 136–141Google Scholar
  43. 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
  44. 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 79:1–24Google Scholar
  45. Wang K, Shao Y, Shu L, Zhu C, Zhang Y (2016) Mobile big data fault-tolerant processing for eHealth networks. IEEE Netw 30(1):36–42CrossRefGoogle Scholar
  46. 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):23–32CrossRefGoogle Scholar
  47. Zandbergen AP (2009) Accuracy of iPhone locations: a comparison of assisted GPS, WiFi and cellular positioning. Trans GIS 13(s1):5–25CrossRefGoogle Scholar
  48. 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):86–90CrossRefGoogle Scholar
  49. 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 1–9Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Chii Chang
    • 1
  • Amnir Hadachi
    • 2
  • Satish Narayana Srirama
    • 1
  • Mart Min
    • 3
  1. 1.Mobile & Cloud Lab, Institute of Computer ScienceUniversity of TartuTartuEstonia
  2. 2.Institute of Computer ScienceUniversity of TartuTartuEstonia
  3. 3.Thomas Johann Seebeck Institute of ElectronicsTallinn University of TechnologyTallinnEstonia

Section editors and affiliations

  • Rodrigo N. Calheiros
    • 1
  • Marcos Dias de Assuncao
    • 2
  1. 1.School of Computing, Engineering and MathematicsWestern Sydney UniversityPenrithAustralia
  2. 2.Inria, LIP, ENS LyonLyonFrance