Logical design of multi-model data warehouses

Abstract

Multi-model DBMSs, which support different data models with a fully integrated backend, have been shown to be beneficial to data warehouses and OLAP systems. Indeed, they can store data according to the multidimensional model and, at the same time, let each of its elements be represented through the most appropriate model. An open challenge in this context is the lack of methods for logical design. Indeed, in a multi-model context, several alternatives emerge for the logical representation of dimensions and facts. The goal of this paper is to devise a set of guidelines for the logical design of multi-model data warehouses so that the designer can achieve the best trade-off between features such as querying, storage, and ETL. To this end, for each model considered (relational, document-based, and graph-based) and for each type of multidimensional element (e.g., non-strict hierarchy) we propose some solutions and carry out a set of intra-model and inter-model comparisons. The resulting guidelines are then tested on a case study that shows all types of multidimensional elements.

Towards a Scalable and Efficient ETL

“…In the context of multi-model data warehousing and and to improve the ETL performance, various options arise for representing dimensions and facts logically. Sandro Bimonte et al [20], propose guidelines for multimodel multidimensional design in MMDBMS-based data warehouses, validated through intra-model and intermodel comparisons, and illustrated with a case study. In [21], the authors consider the variety of data sources and the variety of data warehouse storage platforms and their impact on the efficiency and performance of the ETL process.…”

Os-ETL: A High-Efficiency, Open-Scala Solution for Integrating Heterogeneous Data in Large-Scale Data Warehousing

“…According to Stonebraker and Çetintemel [42], a polystore system is a database system having various heterogeneous data stores and diverse query interfaces. Due to this heterogeneity of the storage systems in the current Big Data ecosystem, many dissimilar backends or federated storage engines are required [2].…”

Workload-Aware Performance Tuning for Multimodel Databases Based on Deep Reinforcement Learning

“…Te emergence of multimodel database [3] (MMDB) provides a new solution to efectively address the shortcomings of traditional database. As a new trend in the feld of database management systems [4][5][6], multimodel database can store data of various structural forms in a single engine, without the need to deploy diferent databases for data of various structures. Multimodel database is also considered to be the next generation of data management system combining fexibility, scalability, and consistency [7,8].…”