In-Memory Depth-First Tree Construction of Hierarchical Data in a RDBMS

This paper presents a novel approach for in-memory depth-first tree construction of hierarchical data within a Relational Database Management System (RDBMS). Hierarchical data structures are commonly used in various applications such as file systems [16], organizational charts, and nested categories. However, processing and traversing large hierarchical datasets in traditional RDBMS environments can be computationally expensive and time-consuming due to the reliance on recursive operations. To address this challenge, an efficient in-memory depth-first tree construction algorithm has been proposed that leverages the capabilities of modern RDBMS to optimize hierarchical data retrieval and manipulation. The algorithm utilizes in-memory data structures to construct the tree representation in a depth-first manner. The usefulness of this approach has been demonstrated through a series of experiments conducted on a real world hierarchical database. The results show significant improvements in the construction and traversal speed compared to traditional recursive approaches which involve a significant number of database hits. The in-memory depth-first tree construction offers faster response times, reduced computational overhead, and improved scalability for handling larger hierarchical datasets. The proposed approach contributes to advancing the field of database management and provides a valuable tool for developers and researchers working with hierarchical data structures.