Authors:
Viet Nguyen
1
;
2
;
Vu Bui
1
;
2
and
Thu Minh Tran Nguyen
1
;
2
Affiliations:
1
Faculty of Information Technology, University of Science, Ho Chi Minh City, Vietnam
;
2
Viet Nam National University, Ho Chi Minh City, Vietnam
Keyword(s):
Data Warehouse, NoSQL Databases, Graph Data Warehouse, NoSQL-Based Data Warehouse Modeling, Methodology.
Abstract:
Today, organizations leverage big data analytics for insights and decision-making, handling vast amounts of structured and unstructured data. Traditional data warehouses (TDW) are suboptimal for such analytics, creating a demand for NoSQL-based modern data warehouses (DWs) that offer improved storage, scalability, and unstructured data processing. Graph-based data models (GDMs), a common NoSQL data model, are considered the next frontier in big data modeling. They organize complex data points based on relationships, enabling analysts to see connections between entities and draw new conclusions. This paper provides a comprehensive methodology for graph-based data warehouse (GDW) design, encompassing conceptual, logical, and physical phases. In the conceptual stage, we propose a high-abstraction data model for NoSQL DW, suitable for GDM and other NoSQL models. During the logical phase, GDM is used as the logical DW model, with a solution for mapping the conceptual DW model to GDW. We i
llustrate the GDW design phases with a use case for learning path recommendations based on career goals. Finally, we carried out the physical implementation of the logical DW model on the Neo4j platform to demonstrate its efficiency in managing complex queries and relationships, and showcase the applicability of the proposed model.
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