Abstract
Big data analytics (BDA) applications use machine learning to extract valuable insights from large, fast, and heterogeneous data sources. The architectural design and evaluation of BDA applications entail new challenges to integrate emerging machine learning algorithms with cutting-edge practices whilst ensuring performance levels even in the presence of large data volume, velocity, and variety (3Vs). This paper presents a design process approach based on the Attribute-Driven Design (ADD) method and Architecture tradeoff analysis method (ATAM) to specify, deploy, and monitor performance metrics in BDA applications supported by domain-specific modeling and DevOps. Our design process starts with the definition of architectural drivers, followed by functional and deployment specification through integrated high-level modeling which enables quality scenarios monitoring. We used two use cases from avionics to evaluate this proposal, and the preliminary results suggest advantages by integrating multiple views, automating deployment and monitoring compared to similar approaches.
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