
A Survey of Holistic Approaches for Distributed Database Systems: From Conceptual Model to Deployment
Author(s) -
Goncalo Carvalho,
Jorge Bernardino,
Bruno Cabral,
Vasco Pereira
Publication year - 2025
Publication title -
ieee access
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 0.587
H-Index - 127
eISSN - 2169-3536
DOI - 10.1109/access.2025.3587670
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Database modeling defines the logical design, structure, and specifications for data storage and access within a DataBase Management System (DBMS), a core component of any Information System (IS). While conceptual database models (e.g., Entity-Relationship (ER) diagrams, Unified Modeling Language (UML)) facilitate the transition to physical models, they still fall short when it comes to supporting distributed and multi-layered architectures. Current approaches lack unified modeling abstractions, forcing manual reconciliation between high-level designs and their distributed implementations, leading to inefficiencies, deployment risks, and reliance on specialized teams. Existing tools (e.g., Kubernetes, Terraform) automate infrastructure but remain disconnected from Conceptual Models (CMs), requiring error-prone manual translation. This paper assesses the current state of the art in holistic approaches that aim to integrate modeling and deployment, identifies critical gaps in end-to-end automation, classifies the available deployment tools, and highlights advances that could enable building a distributed data management system based solely on its CM, potentially leading to greater agility and reduced operational complexity. Finally, the paper discusses open questions and research directions that indicate promising areas for future investigation.
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