Acta Informatica Pragensia 2015, 4(2), 108-121 | DOI: 10.18267/j.aip.645187

AHP Model for the Big Data Analytics Platform Selection

Martin Lněnička
Institute of System Engineering and Informatics, Faculty of Economics and Administration, University of Pardubice, Studentska 84, 532 10 Pardubice, Czech Republic

Big data analytics refers to a set of advanced technologies, which are designed to efficiently operate and maintain data that are not only big, but also high in variety and velocity. This paper analyses these emerging big data technologies and presents a comparison of the selected big data analytics platforms through the whole data life. The main aim is then to propose and demonstrate the use of an AHP model for the big data analytics platform selection, which may be used by businesses, public sector institutions as well as citizens to solve multiple criteria decision-making problems. It would help them to discover patterns, relationships and useful information in their big data, make sense of them and to take responsive action.

Keywords: AHP model, Big data analytics, Big data life cycle, Platform selection, Decision-making

Received: July 21, 2015; Revised: September 30, 2015; Accepted: October 15, 2015; Published: December 30, 2015  Show citation

ACS AIP APA ASA Harvard Chicago Chicago Notes IEEE ISO690 MLA NLM Turabian Vancouver
Lněnička, M. (2015). AHP Model for the Big Data Analytics Platform Selection. Acta Informatica Pragensia, 4(2), 108-121. doi: 10.18267/j.aip.64
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. Bengtsson, P., & Bosch, J. (1998). Scenario-based software architecture reengineering. In Proceedings of the Fifth International Conference on Software Reuse (pp. 308-317). New York: IEEE.
    2. Brožová, H., Houška, M., & Šubrt, T. (2013). Modely pro vícekriteriální rozhodování. Praha: Česká zemědělská univerzita v Praze.
    3. Cattell, R. (2011). Scalable SQL and NoSQL data stores. ACM SIGMOD Record, 39(4), 12-27. doi: 10.1145/1978915.1978919 Go to original source...
    4. Che, D., Safran, M., & Peng, Z. (2013). From Big Data to Big Data Mining: Challenges, Issues, and Opportunities. In B. Hong et al. (Eds.), Database Systems for Advanced Applications (pp. 1-15). Heidelberg: Springer. Go to original source...
    5. Chen, M., Mao, S., & Liu, Y. (2014). Big Data: A Survey. Mobile Networks and Applications, 19(2), 171-209. doi: 10.1007/s11036-013-0489-0 Go to original source...
    6. Daniluk, A. (2012). Visual modeling for scientific software architecture design. A practical approach. Computer Physics Communications, 183(2), 213-230. doi: 10.1016/j.cpc.2011.07.021 Go to original source...
    7. Demchenko, Y., Grosso, P., Laat, C., & Membrey, P. (2013). Addressing Big Data Issues in Scientific Data Infrastructure. In 2013 International Conference on Collaboration Technologies and Systems (pp. 48-55). New York: IEEE. Go to original source...
    8. Elgendy, N., & Elragal, A. (2014). Big Data Analytics: A Literature Review Paper. In P. Perner (Ed.), ICDM 2014. LNAI, vol. 8557 (pp. 214-227). Heidelberg: Springer. Go to original source...
    9. Guo, S. (2013). Hadoop Operations and Cluster Management Cookbook. Birmingham: Packt Publishing.
    10. Kaisler, S., Armour, F., Espinosa, J.A., & Money, W. (2013). Big Data: Issues and Challenges Moving Forward. In 46th Hawaii International Conference on System Sciences (pp. 995-1004). New York: IEEE. Go to original source...
    11. Karaarslan, N., & Gundogar, E. (2009). An application for modular capability-based ERP software selection using AHP method. The International Journal of Advanced Manufacturing Technology, 42(9-10), 1025-1033. doi: 10.1007/s00170-008-1522-5 Go to original source...
    12. Lai, V. S., Wong, B. K., & Cheung, W. (2002). Group decision making in a multiple criteria environment: A case using the AHP in software selection. European Journal of Operational Research, 137(1), 134-144. doi: 10.1016/S0377-2217(01)00084-4 Go to original source...
    13. Lake, P., & Drake, R. (2014). Information Systems Management in the Big Data Era. London: Springer. Go to original source...
    14. Lee, K. H., Lee, Y. J., Choi, H., Chung, Y.D., & Moon, B. (2011). Parallel Data Processing with MapReduce: A Survey. SIGMOD Rec., 40(4), 11-20. doi: 10.1145/2094114.2094118 Go to original source...
    15. Liou J. J. H., & Tzeng, G.-H. (2012). Comments on "Multiple criteria decision making (MCDM) methods in economics: an overview". Technological and Economic Development of Economy, 18(4), 672-695. doi: 10.3846/20294913.2012.753489 Go to original source...
    16. Lnenicka, M. (2015). An In-Depth Analysis of Open Data Portals as an Emerging Public E-Service. International Journal of Social, Education, Economics and Management Engineering, 9(2), 589-599.
    17. Lněnička, M., & Komárková, J. (2014). An Overview and Comparison of Big Data Analytics Platforms. In Sborník příspěvků z mezinárodní vědecké konference MMK 2014 (pp. 3446-3455). Hradec Králové: Magnanimitas.
    18. Loshin, D. (2013). Big Data Analytics: From Strategic Planning to Enterprise Integration with Tools, Techniques, NoSQL, and Graph. Waltham: Elsevier. Go to original source...
    19. Madden, S. (2012). From Databases to Big Data. IEEE Internet Computing, 16(3), 4-6. doi: 10.1109/MIC.2012.50 Go to original source...
    20. Marakas, G. M., & O'Brien, J. A. (2013). Introduction to Information Systems. New York: McGraw-Hill/Irwin.
    21. Saaty, T. L. (1990). How to make a decision: The Analytic Hierarchy Process. European Journal of Operational Research, 48(1), 9-26. doi: 10.1016/0377-2217(90)90057-I Go to original source...
    22. Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83-98. doi: 10.1504/IJSSCI.2008.017590 Go to original source...
    23. Sakr, S., Liu, A., & Fayoumi, A.G. (2013). The Family of MapReduce and Large Scale Data Processing Systems. ACM Computing Surveys (CSUR), 46(1), 1-27. doi: 10.1145/2522968.2522979 Go to original source...
    24. Saecker, M., & Markl, V. (2013). Big Data Analytics on Modern Hardware Architectures: A Technology Survey. In M. A. Aufaure & E. Zimányi (Eds.), Business Intelligence (pp. 125-149). Berlin Heidelberg: Springer. Go to original source...
    25. Shamsi, J., Khojaye, M. A., & Qasmi, M. A. (2013). Data-Intensive Cloud Computing: Requirements, Expectations, Challenges, and Solutions. Journal of Grid Computing, 11(2), 281-310. doi: 10.1007/s10723-013-9255-6 Go to original source...
    26. Silva, J. P., Goncalves, J. J., Fernandes, J., & Cunha, M. M. (2013). Criteria for ERP selection using an AHP approach. In 2013 8th Iberian Conference on Information Systems and Technologies (pp. 1-6). New York: IEEE.
    27. Singh, D., & Reddy, C. K. (2014). A survey on platforms for big data analytics. Journal of Big Data, 1(8), 1-20. doi: 10.1186/s40537-014-0008-6 Go to original source...
    28. Tien, J. M. (2013). Big Data: Unleashing Information. Journal of Systems Science and Systems Engineering, 22(2), 127-151. doi: 10.1007/s11518-013-5219-4 Go to original source...
    29. Vaidya, O. S., & Kumar, S. (2006). Analytic hierarchy process: An overview of applications. European Journal of Operational Research, 169(1), 1-29. doi: 10.1016/j.ejor.2004.04.028 Go to original source...
    30. Valacich, J. S., George, J. F., & Hoffer, J. A. (2012). Essentials of Systems Analysis and Design. New Jersey: Prentice Hall.
    31. Vossen, G. (2014). Big data as the new enabler in business and other intelligence. Vietnam Journal of Computer Science, 1(1), 3-14. doi: 10.1007/s40595-013-0001-6 Go to original source...
    32. Wei, C. C., Chien, C. F., & Wang, M. J. J. (2005). An AHP-based approach to ERP system selection. International Journal of Production Economics, 96(1), 47-62. doi: 10.1016/j.ijpe.2004.03.004 Go to original source...
    33. Zavadskas, E. K., & Turskis, Z. (2011). Multiple criteria decision making (MCDM) methods in economics: an overview. Technological and Economic Development of Economy, 17(2), 397-427. doi: 10.3846/20294913.2011.593291 Go to original source...
    34. Zhao, L., Sakr, S., Liu, A., & Bouguettaya, A. (2014). Big Data Processing Systems. In Cloud Data Management (pp. 135-176). Heidelberg: Springer. Go to original source...

    This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content