Document-oriented bases allow high flexibility in data representation which facilitates a rapid development of applications and enables many possibilities for data structuring. Unfortunately, in many cases, due to this flexibility and the absence of data modelling, the choice of a data representation is neglected by developers leading to many issues on several aspects of the document base and application quality; e.g., memory print, data redundancy, readability and maintainability. We aim at facilitating the study of data structuring alternatives and providing objective metrics to better reveal the advantages and disadvantages of a structure with respect to user needs. The main contributions of our approach are twofold. First of all, the semi-automatic generation of many suitable alternatives for data structuring given an initial UML model. Second, the automatic computation of structural metrics, allowing a comparison of the alternatives for JSON-compatible schema abstraction. These metrics reflect the complexity of the structure and are intended to be used in decision criteria for schema analysis and design process. This work capitalises on experiences with MongoDB, XML and software complexity metrics. The paper presents the schema generation and the metrics together with a validation scenario where we discuss how to use the results in a schema recommendation perspective.

面向文档的基础允许数据表示的高度灵活性，这促进了应用程序的快速开发，并为数据结构化提供了许多可能性。不幸的是，在许多情况下，由于这种灵活性和缺乏数据建模，开发人员忽略了数据表示的选择，从而导致文档库和应用程序质量的多个方面出现许多问题；例如，内存打印、数据冗余、可读性和可维护性。我们旨在促进对数据结构替代方案的研究，并提供客观指标，以更好地揭示结构在用户需求方面的优缺点。我们的方法的主要贡献是双重的。首先，在给定初始 UML 模型的情况下，半自动生成许多适用于数据结构的替代方案。第二，结构度量的自动计算，允许比较 JSON 兼容模式抽象的替代方案。这些度量反映了结构的复杂性，旨在用于模式分析和设计过程的决策标准。这项工作充分利用了 MongoDB、XML 和软件复杂性指标方面的经验。本文介绍了模式生成和指标以及验证场景，我们将在其中讨论如何在模式推荐的角度使用结果。XML 和软件复杂性指标。本文介绍了模式生成和指标以及验证场景，我们将在其中讨论如何在模式推荐的角度使用结果。XML 和软件复杂性指标。本文介绍了模式生成和指标以及验证场景，我们将在其中讨论如何在模式推荐的角度使用结果。