Data models, storage and indexing

The field of data models, storage and indexing research focuses on the theoretical frameworks and practical methods used to organize, store, and access data efficiently. This category covers various types of data models with examples, including conceptual, logical, and physical models, essential for database management systems (DBMS) and data warehouses. Understanding indexing in data storage further enhances retrieval performance. As a critical part of INFORMATION AND COMPUTING SCIENCES, this research area advances data management and data science. JoVE Visualize enriches comprehension by pairing PubMed articles with JoVE’s experiment videos, offering a clearer view of research methods and findings.

Key Methods & Emerging Trends

Core Methods in Data Models, Storage, and Indexing

Established methods in this field include the use of conceptual, logical, and physical data models to define and structure data for efficient management. Common types of data models with examples include the relational, hierarchical, and network models, fundamental to DBMS design. Storage techniques focus on optimizing data organization on physical media, while indexing methods—such as B-trees and hash indexing—improve data retrieval speed. These foundational approaches support many applications in data warehousing and enterprise databases, enabling reliable and scalable data management.

Emerging and Innovative Techniques

Recent advancements explore novel types of data modeling techniques and indexing approaches designed to handle the growing complexity and volume of data. Innovations include graph-based models for representing relationships in big data, and multi-dimensional indexing in data warehouses to enhance query performance. Additionally, new storage models leveraging distributed and cloud architectures are gaining traction, addressing scalability and fault tolerance. Research also investigates adaptive indexing and machine learning-assisted data management strategies, reflecting the evolving landscape of data models, storage, and indexing.