The complexity of database systems has increased significantly along with the continuous growth of data, resulting in NoSQL systems and forcing Information Systems (IS) architects to constantly adapt their data models (i.e., the data structure of information stored in the database) and carefully choose the best option(s) for storing and managing data. In this context, we propose %in this paper an automatic global approach for leading data models' transformation process. This approach starts with the generation of all possible solutions. It then relies on a cost model that helps to compare these generated data models in a logical level to finally choose the best one for the given use case. This cost model integrates both data model and queries cost. It also takes into consideration the environmental impact of a data model as well as its financial and its time costs. This work presents for the first time a multidimensional cost model encompassing time, environmental and financial constraints, which compares data models leading to the choice of the optimal one for a given use case. In addition, a simulation for data model's transformation and cost computation has been developed based on our approach.

翻译：数据库系统的复杂性随着数据的持续增长而显著增加，催生了NoSQL系统，并迫使信息系统（IS）架构师不断调整其数据模型（即数据库中存储信息的数据结构），审慎选择存储和管理数据的最佳方案。在此背景下，我们提出了一种自动化的全局方法，用于主导数据模型的转换过程。该方法首先生成所有可能的解决方案，然后基于一个成本模型，在逻辑层面对这些生成的数据模型进行对比，最终为特定用例选出最优方案。该成本模型整合了数据模型成本与查询成本，同时考虑了数据模型的环境影响及其财务成本和时间成本。本研究首次提出了一种涵盖时间、环境和财务约束的多维成本模型，通过比较数据模型从而为特定用例选择最优方案。此外，基于我们的方法，开发了一个用于数据模型转换和成本计算的仿真系统。