Hierarchical Tree Data Storage for Project Management
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Solution Overview
Problem
Traditional project management systems face challenges in scaling and maintaining accurate data access across distributed servers, leading to performance issues as data volume increases, particularly in cloud implementations with large numbers of issues and comments.
Innovation Solution
A method is implemented to create sibling nodes in a database structure for project management systems, allowing for efficient data storage and retrieval by limiting nodes associable with an issue to be readable within three database operations, using a tree structured storage arrangement that includes root, child, and sibling nodes, with size constraints to manage large data volumes effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional project management systems store all issue data in a flat database structure, then data completeness is maintained, but system performance deteriorates and scalability is limited as data volume increases
Solution Approach 1:
The patent segments issue data into hierarchical nodes (root nodes, child nodes, and sibling nodes) within a tree structure. Each node contains only the data necessary for that specific level, with root nodes holding issue key information, child nodes storing comment data, and sibling nodes managing additional information. This segmentation allows the system to maintain data completeness while improving performance by enabling targeted data retrieval operations that read only necessary nodes rather than entire flat datasets.
2Quantity of substance
If the database structure allows unlimited data growth to accommodate expanding organizations, then data capacity increases, but access reliability and accuracy deteriorate across distributed servers
Solution Approach 1:
The patent implements a nested tree structure where root nodes contain issue information, child nodes contain comments, and sibling nodes contain additional information. Each node type is nested within the hierarchical structure, allowing unlimited data capacity growth while maintaining reliable access through the nested organization. The nesting enables distributed servers to access specific nested levels independently, ensuring data access reliability even as the overall system scales to accommodate expanding organizations.
3Quantity of substance
If traditional systems increase data storage capacity to service expanding platforms, then data volume capacity increases, but performance issues arise due to inefficient data retrieval
Solution Approach 1:
The patent transitions from a two-dimensional flat database structure to a three-dimensional hierarchical tree structure with multiple levels (root, child, sibling nodes). This dimensional change enables the system to accommodate unlimited data volume capacity while maintaining efficient retrieval performance. The additional hierarchical dimension allows the database to organize data spatially, enabling targeted reads of specific node levels rather than scanning entire flat datasets, thus resolving the performance degradation that occurs with increased data volume in traditional systems.
4Productivity
If the database enforces strict size constraints on data entities, then data storage efficiency improves, but data completeness and information retention are compromised
Solution Approach 1:
The patent segments data entities into multiple specialized nodes (root nodes for issue data, child nodes for comments, sibling nodes for additional information), each subject to size constraints. This segmentation allows the system to enforce storage efficiency at the node level while maintaining overall data completeness through the collective hierarchy. Each node type is optimized for its specific data category, enabling efficient storage of large volumes of diverse data without compromising information retention, as the segmentation distributes data across multiple constrained nodes rather than requiring a single large node.
Data Source
AI summary
Described herein is a computer implemented method and computational system for a project management service. Issues of a project are defined by data entities stored in a tree structure including a root node. The tree structure may include one or more siblings to the root node, one or more child nodes and one or more siblings to the child node. The root node references each child node and references each of its sibling nodes. Each child node references each of its sibling nodes.