Big data analytics (BDA) applications use machine learning algorithms to extract valuable insights from large, fast, and heterogeneous data sources. New software engineering challenges for BDA applications include ensuring performance levels of data-driven algorithms even in the presence of large data volume, velocity, and variety (3Vs). BDA software complexity frequently leads to delayed deployments, longer development cycles and challenging performance assessment. This paper proposes a Domain-Specific Model (DSM), and DevOps practices to design, deploy, and monitor performance metrics in BDA applications. Our proposal includes a design process, and a framework to define architectural inputs, software components, and deployment strategies through integrated high-level abstractions to enable QS monitoring. We evaluate our approach with four use cases from different domains to demonstrate a high level of generalization. Our results show a shorter deployment and monitoring times, and a higher gain factor per iteration compared to similar approaches.

中文翻译：

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ACCORDANT：大数据分析架构的特定领域模型和 DevOpsApproach

大数据分析 (BDA) 应用程序使用机器学习算法从大型、快速和异构数据源中提取有价值的见解。BDA 应用程序面临的新软件工程挑战包括确保数据驱动算法的性能水平，即使存在大量数据、速度和多样性 (3V)。BDA 软件的复杂性经常导致部署延迟、开发周期延长和具有挑战性的性能评估。本文提出了一个特定领域模型 (DSM) 和 DevOps 实践来设计、部署和监控 BDA 应用程序中的性能指标。我们的提议包括一个设计过程和一个框架，通过集成的高级抽象来定义架构输入、软件组件和部署策略，以实现 QS 监控。我们使用来自不同领域的四个用例来评估我们的方法，以展示高水平的泛化。我们的结果表明，与类似方法相比，部署和监控时间更短，每次迭代的增益因子更高。