Logical Schema for Data Warehouse on Column-Oriented NoSQL Databases

Boussahoua, Mohamed; Boussaïd, Omar; Bentayeb, Fadila

doi:10.1007/978-3-319-64471-4_20

Cited by 15 publications

(11 citation statements)

References 9 publications

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“…Traditional DW architectures rely on a single, relational DBMS for storage and querying 1 . To offer better support to volume while maintaining velocity, some recent works propose the usage of NoSQL DBMSs; for example, [7] relies on a document-based DBMS, and [8] on a column-based DBMS. However, all NoSQL proposals for DWs are based on a single data model, and all data must be transformed to fit that model.…”

Section: Introductionmentioning

confidence: 99%

Data variety, come as you are in multi-model data warehouses

Bimonte

Gallinucci

Marcel

et al. 2022

Information Systems

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Section: Introductionmentioning

confidence: 99%

Data variety, come as you are in multi-model data warehouses

Bimonte

Gallinucci

Marcel

et al. 2022

Information Systems

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“…These approaches allow data warehouses to be implemented under column-oriented NoSQL systems. In [20], [24] authors have proposed a set of transformation rules to convert facts, measures, dimensions, and attributes to columnar concepts. More precisely, facts and dimensions are transformed into column families where measures and attributes are stored in columns.…”

Section: A Column-based Approachesmentioning

confidence: 99%

Performance of NoSQL Graph Implementations of Star vs. Snowflake Schemas

et al. 2022

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Nowadays, the data used for decision-making come from a wide variety of sources which are difficult to manage using relational databases. To address this problem, many researchers have turned to Not only SQL (NoSQL) databases to provide scalability and flexibility for On-Line Analytical Processing (OLAP) systems. In this paper, we propose a set of formal rules to convert a multidimensional data model into a graph data model (MDM2G). These rules allow conventional star and snowflake schemas to fit into NoSQL graph databases. We apply the proposed rules to implement star-like and snowflake-like graph data warehouses. We compare their performances to similar relational ones focusing on the data model, dimensionality, and size. The experimental results show large differences between relational and graph implementations of a data warehouse. A relational implementation performs better for queries on a couple of tables, but conversely, a graph implementation is better when queries involve many tables. Surprisingly the performances of a star-like and snowflake-like graph data warehouses are very close. Hence a snowflake schema could be used in order to easily consider new sub-dimensions in a graph data warehouse.

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“…Modeling IDR with conventional data warehouse (DW) involves specific infrastructure and setups for extract, transform, and load (ETL) procedures and online analytical processing (OLAP) processes as well as reporting tools; all these components are difficult to build and require a lot of organizational resources for implementation and training purposes [25,26]. Although the majority of warehouses are implemented using relational technologies [23], high consistency and availability, their performance decreases with data growth and they face scalability constraints as they are impossible to measure horizontally, and their vertical growth is limited [27].…”

Section: Physical Integrationmentioning

confidence: 99%

GENE2D: A NoSQL Integrated Data Repository of Genetic Disorders Data

Samra

Soh

2020

Healthcare

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There are few sources from which to obtain clinical and genetic data for use in research in Saudi Arabia. Numerous obstacles led to the difficulty of integrating these data from silos and scattered sources to provide standardized access to large data sets for patients with common health conditions. To this end, we sought to contribute to this area and offer a practical and easy-to-implement solution. In this paper, we aim to design and implement a “not only SQL” (NoSQL) based integration framework to generate an Integrated Data Repository of Genetic Disorders Data (GENE2D) to integrate data from various genetic clinics and research centers in Saudi Arabia and provide an easy-to-use query interface for researchers to conduct their studies on large datasets. The major components involved in the GENE2D architecture consists of the data sources, the integrated data repository (IDR) as a central database, and the application interface. The IDR uses a NoSQL document store via MongoDB (an open source document-oriented database program) as a backend database. The application interface called Query Builder provides multiple services for data retrieval from the database using a custom query to answer simple or complex research questions. The GENE2D system demonstrates its potential to help grow and develop a national genetic disorders database in Saudi Arabia.

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Logical Schema for Data Warehouse on Column-Oriented NoSQL Databases

Cited by 15 publications

References 9 publications

Data variety, come as you are in multi-model data warehouses

Data variety, come as you are in multi-model data warehouses

Performance of NoSQL Graph Implementations of Star vs. Snowflake Schemas

GENE2D: A NoSQL Integrated Data Repository of Genetic Disorders Data

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