Skip to main content
SpringerLink
Log in

Big data challenges: Prioritizing by decision-making process using Analytic Network Process technique

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

This paper presents an innovative technique to find out the most influential challenge faced by big data. Presently, the key challenges faced by big data are security, privacy, and accuracy. To prioritize the most influential factor among the three, we need to select the suitable factor that accomplishes our objectives within the available resources. In this study, we propose the Analytic Network Process (ANP) technique for evaluating these challenges. The study also appraises the ANP model in comparison with the priorities of all competing challenges inside the big data. The secondary data has been taken for the ANP analysis from numerous scholarly articles focusing on big data. In our analysis, we have proposed a policy assessment of the relation between big data characteristics and the challenges; it further prioritizes the consequent results for the most influential challenge by using ANP model. The empirical results conclude that the security has the highest influence value i.e. 54% of the total measurement, while privacy and accuracy stand second and third among the most influential factors with 36% and 8% values respectively. Finally, the study provides important clues in the decision-making process in finding the solutions to such challenges faced by big data.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. The eigenvector is defined as, “It is a vector whose direction remains unchanged when a linear transformation is applied to it”. It has many applications in computer vision and machine learning.

References

  1. Acker O, Blockus A, Pötscher F (2013) Benefiting from big data: a new approach for the telecom industry. Strategy & Analysis Report

  2. Agrawal D, El Abbadi A, Wang S (2013) Secure and privacy-preserving database services in the cloud. In: Data Engineering (ICDE), 2013 I.E. 29th International Conference on. IEEE, pp 1268–1271

  3. Ahmed ESA, Saeed RA (2014) A Survey of big data cloud computing security. Int J Comput Sci Softw Eng (IJCSSE) 3:78–85

    Google Scholar 

  4. Ahmed F, Naeem M, Iqbal M (2016) ICT and renewable energy: a way forward to the next generation telecom base stations. Telecommun Syst:1–14. https://doi.org/10.1007/s11235-016-0156-4

  5. Ammu N, Irfanuddin M (2013) Big data challenges. Int J Adv Trends Comput Sci Eng 2:613–615

    Google Scholar 

  6. Andreichicova ON, Radyshevskaya TN (2009) An application of the Analytic Network Process to researching oral health. Int J Analytic Hierarchy Process 1(1):45–60

    Article  Google Scholar 

  7. Angello C, Wema E (2010) Availability and usage of ICTs and e-resources by livestock researchers in Tanzania: challenges and ways forward. Int J Educ Dev Inf Commun Technol 6(1):A34

    Google Scholar 

  8. Bello-Orgaz G, Jung JJ, Camacho D (2016) Social big data: Recent achievements and new challenges. Information Fusion 28:45–59

    Article  Google Scholar 

  9. Brynjolfsson E, McAfee A (2014) The second machine age: work, progress, and prosperity in a time of brilliant technologies. WW Norton & Company

  10. Butler M (2011) Android: changing the mobile landscape. IEEE Pervasive Comput. https://doi.org/10.1109/MPRV.2011.1

  11. Castells M (2011) The rise of the network society: the information age: economy, society, and culture, vol 1. Wiley

  12. Chen CLP, Zhang C-Y (2014) Data-intensive applications, challenges, techniques and technologies: a survey on big data. Inf Sci 275:314–347

    Article  Google Scholar 

  13. Chen M, Mao S, Liu Y (2014) Big data: a survey. Mob Netw Appl 19(2):171–209

    Article  Google Scholar 

  14. Chen J, Song X, Nie L, Wang X, Zhang H, Chua TS (2016) Micro tells macro: predicting the popularity of micro-videos via a transductive model. In: Proceedings of the 2016 ACM on Multimedia Conference. ACM, pp 898-907

  15. Colombo P, Ferrari E (2015) Privacy aware access control for big data: a research roadmap. Big Data Res 2(4):145–154

    Article  Google Scholar 

  16. Cuzzocrea A (2014) Privacy and security of big data: current challenges and future research perspectives. In: Proceedings of the First International Workshop on Privacy and Secuirty of Big data. ACM, pp 45–47

  17. Cuzzocrea A, Saccà D (2010) Balancing accuracy and privacy of OLAP aggregations on data cubes. In: Proceedings of the ACM 13th international workshop on Data warehousing and OLAP. ACM, pp 93–98

  18. Data, IBM Big (2015) IBM-bringing big data to the enterprise

  19. De Mauro A, Greco M, Grimaldi M (2015) What is big data? A consensual definition and a review of key research topics. In: AIP Conference Proceedings, vol 1644, 1, pp 97–104

  20. Dong Y, Zhang G, Hong W-C, Xu Y (2010) Consensus models for AHP group decision making under row geometric mean prioritization method. Decis Support Syst 49(3):281–289

    Article  Google Scholar 

  21. Douglas L (2001) 3d data management: controlling data volume, velocity and variety. Gartner Retrieved 6:2001

    Google Scholar 

  22. Dumbill E (2013) Making sense of big data. Big Data 1(1):1–2

    Article  Google Scholar 

  23. Ellaway RH, Pusic MV, Galbraith RM, Cameron T (2014) Developing the role of big data and analytics in health professional education. Med Teach 36(3):216–222

    Article  Google Scholar 

  24. Fisher D, DeLine R, Czerwinski M, Drucker S (2012) Interactions with big data analytics. Interactions 19(3):50–59

    Article  Google Scholar 

  25. Garuti C, Sandoval M (2005) Comparing AHP and ANP shiftwork models: hierarchy simplicity v/s network connectivity. In: Proceedings of the 8th international Symposium of the AHP

  26. Hashem IAT, Chang V, Anuar NB, Adewole K, Yaqoob I, Gani A, Ahmed E, Chiroma H (2016) The role of big data in smart city. Int J Inf Manag 36(5):748–758

    Article  Google Scholar 

  27. Herold R (2002) What is the difference between security and privacy. InformationShield

  28. Hilbert M (2014) What is the content of the world's technologically mediated information and communication capacity: how much text, image, audio, and video? Inf Soc 30(2):127–143

    Article  Google Scholar 

  29. Hilbert M (2016) Big data for development: a review of promises and challenges. Dev Policy Rev 34(1):135–174

    Article  MathSciNet  Google Scholar 

  30. Hilbert M, López P (2011) The world’s technological capacity to store, communicate, and compute information. Science 332(6025):60–65

    Article  Google Scholar 

  31. Hurwitz J, Nugent A, Halper F, Kaufman M (2013) Big data for dummies. Wiley

  32. Iqbal R, Doctor F, More B, Mahmud S, Yousuf U (2016) Big data analytics: computational intelligence techniques and application areas. Int J Inf Manag

  33. Jensen M (2013) Challenges of privacy protection in big data analytics. In: Proceedings-2013 Ieee International Congress on Big Data, Bigdata 2013. IEEE

  34. 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 

  35. Kaisler S, Armour F, Alberto Espinosa J, Money W. (2013) Big data: issues and challenges moving forward. In: System Sciences (HICSS), 2013 46th Hawaii International Conference on. IEEE, pp 995–1004

  36. Khalafzai AK, Nirupama N (2011) Building resilient communities through empowering women with information and communication technologies: a Pakistan case study. Sustainability 3(1):82–96

    Article  Google Scholar 

  37. Kim G-H, Trimi S, Chung J-H (2014) Big-data applications in the government sector. Commun ACM 57(3):78–85

    Article  Google Scholar 

  38. Kshetri N (2014) The emerging role of big data in key development issues: opportunities, challenges, and concerns. Big Data Soc 1(2):2053951714564227

    Article  Google Scholar 

  39. Kshetri N (2016) Big data’s role in expanding access to financial services in China. Int J Inf Manag 36(3):297–308

    Article  Google Scholar 

  40. Kwon TH, Kwak JH, Kim K (2015) A study on the establishment of policies for the activation of a big data industry and prioritization of policies: lessons from Korea. Technol Forecast Soc Chang 96:144–152

    Article  Google Scholar 

  41. Lad AP, Shah SA Big data–a pool of opportunities and negotiations: forseen and unforseen

  42. Laney D (2001) 3D data management: controlling data volume, velocity and variety. META Group Res Note 6:70

    Google Scholar 

  43. Letouzé E (2012) Big datafor development: what may determine success or failure

    Google Scholar 

  44. Li M, Zang W, Bai K, Yu M, Liu P (2013) MyCloud: supporting user-configured privacy protection in cloud computing. In: Proceedings of the 29th Annual Computer Security Applications Conference. ACM, pp 59–68

  45. Liu Y, Nie Li, Han L, Zhang L, Rosenblum DS (2015) Action2Activity: recognizing complex activities from sensor data. Paper presented at the Proceedings of the 24th International Conference on Artificial Intelligence, Buenos Aires, Argentina

  46. Liu L, Cheng L, Liu Y, Jia Y, Rosenblum DS (2016) Recognizing complex activities by a probabilistic interval-based model. In: AAAI, vol 30, pp 1266–1272

  47. Liu Y, Zhang L, Nie L, Yan Y, Rosenblum DS (2016) Fortune teller: predicting your career path. Paper presented at the AAAI

  48. Liu Y, Zheng Y, Liang Y, Liu S, Rosenblum DS (2016) Urban water quality prediction based on multi-task multi-view learning. Paper presented at the Proceedings of the International Joint Conference on Artificial Intelligence

  49. Liu Y, Liang Y, Liu S, Rosenblum DS, Zheng Y (2016) Predicting urban water quality with ubiquitous data. arXiv preprint arXiv:1610.09462

    Google Scholar 

  50. Lokanathan S, Gunaratne RL (2015) Mobile network big data for development: demystifying the uses and challenges. Commun Strateg 97:75–94

    Google Scholar 

  51. Marker P, McNamara K, Wallace L (2002) The significance of information and communication technologies for reducing poverty. DFID, London

    Google Scholar 

  52. Matturdi B, Zhou X, Li S, Lin F (2014) Big Data security and privacy: a review. China Commun 11(14):135–145

    Article  Google Scholar 

  53. Mayer-Schönberger V, Cukier K (2013) Big data: a revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt

  54. McAfee A, Brynjolfsson E, Davenport TH, Patil DJ, Barton D (2012) Big data. The management revolution. Harvard Bus Rev 90(10):61–67

    Google Scholar 

  55. Meade LM, Presley A (2002) R&D project selection using the analytic network process. IEEE Trans Eng Manag 49(1):59–66

    Article  Google Scholar 

  56. Meade L, Sarkis J (1998) Strategic analysis of logistics and supply chain management systems using the analytical network process. Transport Res E-Log 34(3):201–215

    Article  Google Scholar 

  57. Munir AB, Yasin M, Hajar S, Muhammad-Sukki F (2015) Big data: big challenges to privacy and data protection. Int Scholarly Sci Res Innov 9(1)

  58. Nasser T, Tariq RS (2015) Big data challenges. J Comput Eng Inf Technol 4:3. https://doi.org/10.4172/2324 9307: 2

    Article  Google Scholar 

  59. Nazir S, Anwar S, Khan SA, Shahzad S, Ali M, Amin R., … Cosmas J (2014) Software component selection based on quality criteria using the analytic network process. In: Abstract and Applied Analysis, vol 2014. Hindawi Publishing Corporation

  60. Nie L, Zhang L, Yang Y, Wang M, Hong R, Chua TS (2015) Beyond doctors: future health prediction from multimedia and multimodal observations. In: Proceedings of the 23rd ACM international conference on Multimedia. ACM, pp 591–600

  61. Normandeau K (2013) Beyond volume, variety and velocity is the issue of big data veracity. Inside Big Data

  62. Preoţiuc-Pietro D, Liu Y, Hopkins D, Ungar L (2017) Beyond binary labels: political ideology prediction of twitter users in ACL

  63. Proctor RA (1992) Selecting an appropriate strategy: a structured creative decision support method. Mark Intell Plan 10(11):21–24

    Article  Google Scholar 

  64. Pulse, Global (2013) United Nations Global Pulse. 2014–08–15. http://www.un.org/millenniumgoals/pdf/GP%20Backgrounder-General2013_Sept2013.pdf

  65. Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New York

    MATH  Google Scholar 

  66. Saaty TL (2005) Theory and applications of the analytic network process: decision making with benefits, opportunities, costs, and risks. RWS publications

  67. Saaty TL (2008) Decision making with the analytic hierarchy process. Int J Serv Sci 1(1):83–98

    MathSciNet  Google Scholar 

  68. Saaty TL, Vargas LG (2013) Decision making with the analytic network process: economic, political, social and technological applications with benefits, opportunities, costs and risks, vol 195. SpringerScience& Business Media

  69. Sadeghi M (2012) Using analytic network process in a group decision-making for supplier selection. Informatica 23(4):621–643

    Google Scholar 

  70. Sagiroglu S, Sinanc D (2013) Big data: a review. In: Collaboration Technologies and Systems (CTS), 2013 International Conference on. IEEE, pp 42–47

  71. Schlichter BR, Danylchenko L (2014) Measuring ICT usage quality for information society building. Gov Inf Q 31(1):170–184

    Article  Google Scholar 

  72. Se-jin K et al (2013) A study on laws for the activation of IT convergence. KLRI

  73. Siraj S, Mikhailov L, Keane JA (2015) PriEsT: an interactive decision support tool to estimate priorities from pairwise comparison judgments. Int Trans Oper Res 22(2):217–235

    Article  MathSciNet  MATH  Google Scholar 

  74. Song X, Nie L, Zhang L, Akbari M, Chua TS (2015) Multiple social network learning and its application in volunteerism tendency prediction. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, pp 213–222

  75. Taslicali AK, Ercan S (2006) The analytic hierarchy & the analytic network processes in multicriteria decision making: a comparative study. Doctoral dissertation, Old Dominion University

  76. Techio LR, Misaghi M (2015) CSRA model–a cloud service risk assessment model

  77. Teltscher S, Magpantay E, Gray V, Olaya D, Vallejo I (2010) Measuring the information society: the ICT development index. Telecommunication Development Bureau, International Telecommunications Union (ITU), Geneva

    Google Scholar 

  78. Tole AA (2013) Big data challenges. Database Syst J 4:31–40

    Google Scholar 

  79. Tran LT, Knight CG, O'neill RV, Smith ER (2004) Integrated environmental assessment of the mid-Atlantic region with analytical network process. Environ Monit Assess 94(1–3):263–277

    Article  Google Scholar 

  80. Triantaphyllou E (2000) Multi-criteria decision making methods. In: Multi-criteria decision making methods: a comparative study. Springer, US, pp 5–21

  81. Wallenius J, Dyer JS, Fishburn PC, Steuer RE, Zionts S, Deb K (2008) Multiple criteria decision making, multiattribute utility theory: recent accomplishments and what lies ahead. Manag Sci 54(7):1336–1349

    Article  MATH  Google Scholar 

  82. Ward JS, Barker A (2013) Undefined by data: a survey of big data definitions. arXiv preprint arXiv:1309.5821

    Google Scholar 

  83. Weidner M, Dees J, Sanders P (2013) Fast OLAP query execution in main memory on large data in a cluster. In: Big Data, 2013 I.E. International Conference on. IEEE, pp 518–524

  84. Zhou, X. (2012) Fuzzy analytical network process implementation with matlab. MATLAB–A fundamental tool for scientific computing and engineering applications, vol 3, pp 133–160

Download references

Acknowledgments

I would like to thanks, Professor Dr. Zeng Jianqiu and Shahid Latif for their intuitive discussions on Big Data and ANP at the “Triple Play Networking Lab” at School of Economics and Management Sciences, Beijing University of Posts and Telecommunications.

Author information

Authors and Affiliations

  1. School of Economics and Management, Beijing University of Posts and Telecommunications (BUPT), Hadian District, 10, Xitucheng Road, Beijing, China

    Zahid Latif, Wang Lei, Zulfiqar Hussain Pathan & Zeng Jianqiu

  2. Sarhad University of Science and Technology Peshawar, Peshawar, Khyber Pakhtunkhwa, Pakistan

    Shahid Latif

  3. School of Optics and Communications, Beijing University of Posts and Telecommunications (BUPT), Hadian District, 10, Xitucheng Road, Beijing, China

    Rahat Ullah

Authors
  1. Zahid Latif
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wang Lei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shahid Latif
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zulfiqar Hussain Pathan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rahat Ullah
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zeng Jianqiu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Zahid Latif proposed and prepared the whole research while the other authors reviewed and gave suggestions to improve the article. All authors have read and approved the final manuscript.

Corresponding author

Correspondence to Zahid Latif.

Ethics declarations

Conflicts of Interest

The authors affirm no conflicts of interest.

Appendices

Appendix 1. Questionnaire Format

  1. 1.

    Factors

Given in Table 3.

  1. 2.

    Direction for questionnaire and example

    1. 2.1

      Question format

The survey has a number of question sets. Each question asks to compare two factors at a time (pairwise comparison). All these factors are drawn from Table 3. The question format is given in Fig. 1.

  1. 2.2

    What to fill?

Fill in empty boxes only and not to fill the 1 or 0.

  1. 2.3

    How to fill?

For each question, we have a number scale from 1 to 9.

In order to make an ultimate decision, how much a selection factor is important in row’ as compared to another factor in column ‘n’.

1/9 means that, ‘m’ is extremely less important than ‘n’; and 9/1 or 9 indicates that, ‘m’ is extremely more important than ‘n’, while 1 means, ‘m’ and ‘n’ are equally important.

figure d

Appendix 2. Weighted super Matrix

$$ \left(\begin{array}{cccccccccc}& \mathrm{Accuracy}& \mathrm{privacy}& \mathrm{security}& \mathrm{Value}& \mathrm{Variety}& \mathrm{Velocity}& \mathrm{Veracity}& \mathrm{Volume}& \mathrm{Mostinfluential}\\ {}\mathrm{Accuracy}& 0& 0& 0& 0.07692& 0.08875& 0.08875& 0.07692& 0.08875& 0\\ {}\mathrm{privacy}& 0& 0& 0& 0.46154& 0.35219& 0.35219& 0.46154& 0.35219& 0\\ {}\mathrm{security}& 0& 0& 0& 0.46154& 0.55907& 0.55907& 0.46154& 0.55907& 0\\ {}\mathrm{Value}& 0.06639& 0.06639& 0.06639& 0& 0& 0& 0& 0& 0.04953\\ {}\mathrm{Variety}& 0.29621& 0.29621& 0.29621& 0& 0& 0& 0& 0& 0.29498\\ {}\mathrm{Velocity}& 0& 0.29621& 0.29621& 0& 0& 0& 0& 0& 0.29454\\ {}\mathrm{Veracity}& 0.04497& 0.04497& 0.04497& 0& 0& 0& 0& 0& 0.6052\\ {}\mathrm{Volume}& 0.29621& 0.29621& 0.29621& 0& 0& 0& 0& 0& 0.30043\\ {}\mathrm{Mostinfluential}& 0& 0& 0& 0& 0& 0& 0& 0& 0\end{array}\right) $$

Appendix 3. Un-weighted super Matrix

$$ \left(\begin{array}{cccccccccc}& \mathrm{Accuracy}& \mathrm{privacy}& \mathrm{security}& \mathrm{Value}& \mathrm{Variety}& \mathrm{Velocity}& \mathrm{Veracity}& \mathrm{Volume}& \mathrm{Mostinfluential}\\ {}\mathrm{Accuracy}& 0& 0& 0& 0& 0.08875& 0.08875& 0.07692& 0.08875& 0\\ {}\mathrm{privacy}& 0& 0& 0& 0& 0.35219& 0.35219& 0.46154& 0.35219& 0\\ {}\mathrm{security}& 0& 0& 0& 0& 0.55907& 0.55907& 0.46154& 0.55907& 0\\ {}\mathrm{Value}& 0.06639& 0.06639& 0.06639& 0.04953& 0& 0& 0& 0& 0.04953\\ {}\mathrm{Variety}& 0.29621& 0.29621& 0.29621& 0.29498& 0& 0& 0& 0& 0.29498\\ {}\mathrm{Velocity}& 0.29621& 0.29621& 0.29621& 0.29454& 0& 0& 0& 0& 0.29454\\ {}\mathrm{Veracity}& 0.04497& 0.04497& 0.04497& 0.06052& 0& 0& 0& 0& 0.06052\\ {}\mathrm{Volume}& 0.29621& 0.29621& 0.29621& 0.30043& 0& 0& 0& 0& 0.30043\\ {}\mathrm{Mostinfluential}& 0& 0& 0& 0& 0& 0& 0& 0& 0\end{array}\right) $$

Appendix 4. Limit Matrix

$$ \left(\begin{array}{cccccccccc}& \mathrm{Accuracy}& \mathrm{privacy}& \mathrm{security}& \mathrm{Value}& \mathrm{Variety}& \mathrm{Velocity}& \mathrm{Veracity}& \mathrm{Volume}& \mathrm{Most}\ \mathrm{influential}\\ {}\mathrm{Accuracy}& 0.04371\kern0.5em & 0.04371\kern0.5em & 0.04371& 0.04371& 0.04371& 0.04371& 0.04371& 0.04371& 0.04371\\ {}\mathrm{privacy}& 0.18218& 0.18218& 0.18218& 0.18218& 0.18218& 0.18218& 0.18218& 0.18218& 0.18218\\ {}\mathrm{security}& 0.27410& 0.27410& 0.27410& 0.27410& 0.27410& 0.27410& 0.27410& 0.27410& 0.27410\\ {}\mathrm{Value}& 0.03320& 0.03320& 0.03320& 0.03320& 0.03320& 00.03320& 0.03320& 0.03320& 0.03320\\ {}\mathrm{Variety}& 0.14811& 0.14811& 0.14811& 0.14811& 0.14811& 0.14811& 0.14811& 0.14811& 0.14811\\ {}\mathrm{Velocity}& 0.14811& 0.14811& 0.14811& 0.14811& 0.14811& 0.14811& 0.14811& 0.14811& 0.14811\\ {}\mathrm{Veracity}&\ 0.02249&\ 0.02249& 0.02249& 0.02249& 0.02249& 0.02249& 0.02249& 0.02249& 0.02249\\ {}\mathrm{Volume}& 0.14811& 0.14811& 0.14811& 0.14811& 0.14811& 0.14811& 0.14811& 0.14811& 0.14811\\ {}\mathrm{Most}\ \mathrm{influential}& 0& 0& 0& 0& 0& 0& 0& 0& 0\end{array}\right) $$

Appendix 5

Table 6 Fundamental scale of weights/numbers
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Latif, Z., Lei, W., Latif, S. et al. Big data challenges: Prioritizing by decision-making process using Analytic Network Process technique. Multimed Tools Appl 78, 27127–27153 (2019). https://doi.org/10.1007/s11042-017-5161-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-017-5161-4

Keywords

Search

Navigation