Towards Quality Analysis for Document Oriented Bases

Abstract

Document-oriented bases allow high flexibility in data representation which facilitates a rapid development of applications and enables many possibilities for data structuring. Nevertheless, the structural choices remain crucial because of their impact on several aspects of the document base and application quality, e.g, memory print, data redundancy, readability and maintainability. Our research is motivated by quality issues of document-oriented bases. We aim at facilitating the study of the possibilities of data structuring and providing objective metrics to better reveal the advantages and disadvantages of each solution with respect to user needs. In this paper, we propose a set of structural metrics for a 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 capitalizes on experiences with MongoDB, XML and software complexity metrics. The paper presents the definition of the metrics together with a validation scenario where we discuss how to use the results in a schema recommendation perspective.

Data Modeling and Design Implementation for CouchDB Database

“…While, In reference relationship, an arrow must use from the current document directed to the referenced document, note that the current document can reference many documents, the bidirectional arrow is used to represent the many to many reference relationship, figure3. [4] On 2018 Paola Gomez et al [16] suggest several criteria that can be adapted to measure the model quality of a document-oriented database, they are called Structural Metrics. The suggested criteria are based on the structure of schema in the Document-Oriented NoSQL model.…”

“…The reason is due to the increase in the complexity of accessing the information hidden in the lower levels of the document structure, especially if this information is not required continuously with the information at the higher level in the document structure. There are several formulas for measuring Nesting depth, including Collection Depth and Global Depth [16]:…”

A Workload-Driven Document Database Schema Recommender (DBSR)

“…In general, the different options to denormalize NoSQL database schemas vary widely [3,11,14], and each single schema design decision will in effect represent a trade-off between aspects of data locality, storage usage, consistency, performance, and scalability. Making appropriate trade-off decisions requires indepth knowledge of the application data model and its workload [19], the capabilities of the target database, and thus requires extensive expert knowledge.…”