A Fuzzy TOPSIS with Affinity Weight for Big Data Projects: Managing Cloud Solution Problems
Nurnadiah Zamri1, Wan Suryani Wan Awang2
1Nurnadiah Zamri, Faculty of Informatics and Computing, University Sultan Zainal Abidin, Besut Campus, Besut, Terengganu, Malaysia.
2Wan Suryani Wan Awang, Faculty of Informatics and Computing, University Sultan Zainal Abidin, Besut Campus, Besut, Terengganu, Malaysia.
Manuscript received on 18 July 2019 | Revised Manuscript received on 03 August 2019 | Manuscript Published on 10 August 2019 | PP: 446-451 | Volume-8 Issue-2S3 July 2019 | Retrieval Number: B10780782S319/2019©BEIESP | DOI: 10.35940/ijrte.B1078.0782S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This study underlines a fuzzy decision making method for determining weights of criteria that constitute for solving cloud solution problems in managing big data projects. The weight determination of cloud for big data projects is crucial since many uncertain and vagueness criteria that need to be considered concurrently. Furthermore, these criteria involve network performance, schedule and traffic management of cloud solutions problem. In response to these challenges, the affinity set applies to Fuzzy TOPSIS (FTOPSIS) method to propose timedependent weights of three criteria for managing big data projects. A major advantage of the affinity weights is that it incorporates performance-traffic management relationships between all criteria. This affinity weight with FTOPSIS method helps to solve the cloud solution problems. This paper also includes the same examples with different methods to compare and validate the proposed method. The proposed seven-step of affinity weight with FTOPSIS method finally managed to solve the cloud solution problem and the result was beautifully consistent with the other two methods.
Keywords: Affinity Weight, Big Data, Cloud, FTOPSIS.
Scope of the Article: Big Data Quality Validation