Big data analytics architecture design — An application in manufacturing systems
Article
Fahmideh, Mahdi and Beydoun, Ghassan. 2019. "Big data analytics architecture design — An application in manufacturing systems." Computers and Industrial Engineering. 128, pp. 948-963. https://doi.org/10.1016/j.cie.2018.08.004
| Article Title | Big data analytics architecture design — An application in manufacturing systems |
|---|---|
| ERA Journal ID | 33009 |
| Article Category | Article |
| Authors | Fahmideh, Mahdi and Beydoun, Ghassan |
| Journal Title | Computers and Industrial Engineering |
| Journal Citation | 128, pp. 948-963 |
| Number of Pages | 16 |
| Year | Feb 2019 |
| Publisher | Elsevier |
| Place of Publication | United Kingdom |
| ISSN | 0360-8352 |
| 1879-0550 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.cie.2018.08.004 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0360835218303760 |
| Abstract | Context: The rapid prevalence and potential impact of big data analytics platforms have sparked an interest amongst different practitioners and academia. Manufacturing organisations are particularly well suited to benefit from data analytics platforms in their entire product lifecycle management for intelligent information processing, performing manufacturing activities, and creating value chains. This needs a systematic re-architecting approach incorportaitng careful and thorough evaluation of goals for integrating manufacturing legacy information systems with data analytics platforms. Furthermore, ameliorating the uncertainty of the impact the new big data architecture on system quality goals is needed to avoid cost blowout in implementation and testing phases. Objective: We propose an approach for goal-obstacle analysis and selecting suitable big data solution architectures that satisfy quality goal preferences and constraints of stakeholders at the presence of the decision outcome uncertainty. The approach will highlight situations that may impede the goals. They will be assessed and resolved to generate complete requirements of an architectural solution. Method: The approach employs goal-oriented modelling to identify obstacles causing quality goal failure and their corresponding resolution tactics. Next, it combines fuzzy logic to explore uncertainties in solution architectures and to find an optimal set of architectural decisions for the big data enablement process of manufacturing systems. Result: The approach brings two innovations to the state of the art of big data analytics platform adoption in manufacturing systems: (i) A goal-oriented modelling for exploring goals and obstacles in integrating systems with data analytics platforms at the requirement level and (ii) An analysis of the architectural decisions under uncertainty. The efficacy of the approach is illustrated with a scenario of reengineering a hyper-connected manufacturing collaboration system to a new big data architecture. |
| Keywords | Big data; Big data analytics platforms; Manufacturing systems; Goal-oriented modelling; Fuzzy logic; Data analytics architecture |
| ANZSRC Field of Research 2020 | 460908. Information systems organisation and management |
| Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
| Byline Affiliations | University of Technology Sydney |
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