Designing a database cost model is one of the main research topics related to the physical design phase. It follows the evolution of database technology in order to evaluate and quantify the performance metrics (e.g., response time, energy consumption, etc.). Therefore, it makes the community researchers sensitive to the generated results. However, reusing and comparing database cost models require extracting related information manually from the research publications. This process is error-prone and time-consuming. Unfortunately, many researchers claim the difficulty of surveying and reproducing cost models already published in several/journal articles and/or reports. This difficulty is due to the absence of a process describing the cost model itself formally as well as the context of its utilization. This article presents an approach enabling the extraction of cost models information (context, parameters, features, etc.) as a set of orchestrated services. These services are implemented using natural language processing and machine-learning techniques via a work-flow pipeline inspired by DevOps practices. We illustrate our approach on a case study to stress the feasibility and benefits of our proposal by emphasizing the reproduction and automatization facilities.

中文翻译：

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通过基于服务的管道将数据库成本模型流程资本化

设计数据库成本模型是与物理设计阶段相关的主要研究课题之一。它遵循数据库技术的发展，以评估和量化性能指标（例如，响应时间、能耗等）。因此，它使社区研究人员对生成的结果敏感。然而，重用和比较数据库成本模型需要从研究出版物中手动提取相关信息。此过程容易出错且耗时。不幸的是，许多研究人员声称很难调查和复制已经在多篇/期刊文章和/或报告中发表的成本模型。这种困难是由于缺乏正式描述成本模型本身及其使用背景的过程。本文介绍了一种方法，可以将成本模型信息（上下文、参数、特性等）提取为一组编排服务。这些服务是通过受 DevOps 实践启发的工作流管道使用自然语言处理和机器学习技术实现的。我们通过强调复制和自动化设施来说明我们在案例研究中的方法，以强调我们提案的可行性和好处。