论文标题
符合：大数据分析体系结构的特定域模型和Devopsprace
ACCORDANT: A Domain Specific Model and DevOpsApproach for Big Data Analytics Architectures
论文作者
论文摘要
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，即使在存在较大的数据量，速度和多样性（3V）的情况下。 BDA软件复杂性通常会导致部署延迟，更长的开发周期和挑战性绩效评估。本文提出了一个特定领域的模型（DSM），并提出了DEVOPS实践，以设计，部署和监视BDA应用程序中的性能指标。我们的建议包括一个设计过程，以及通过集成的高级抽象来定义建筑输入，软件组件和部署策略的框架，以实现QS监视。我们使用来自不同领域的四种用例来评估我们的方法，以证明高水平的概括。我们的结果表明，与类似方法相比，我们的部署和监视时间较短，并且每次迭代的增益因子更高。
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.
