Capitalizing the database cost models process through a service‐based pipeline. (11th July 2021)

• Record Type: Journal Article
• Authors: Ouared, Abdelkader; Ouhammou, Yassine
• Other Names: Bordin Jacir Luiz guestEditor.; Ito Yasuaki guestEditor.; Wakrime Abderrahim Ait guestEditor.; Sellami Mohamed guestEditor.; Halima Riadh Ben guestEditor.
• Is Part Of: Concurrency and computation. Volume 35:Number 11(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-07-11
• Subjects: continuous integration -- cost model context -- database -- DevOps -- machine learning -- natural language processing; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6463
• Languages: English
• ISSNs: 1532-0626

Abstract

Abstract: 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.