Hierarchical Tree Data Aggregation for Distributed Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional data warehousing approaches face bottlenecks in processing and storing large amounts of data, leading to delayed data availability, high complexity, expense, inflexibility, and poor performance, especially in environments with numerous data-generating devices.
Innovation Solution
A hierarchical tree structure is implemented, comprising multiple tree node entities with storage and computing resources that allow for distributed data collection, processing, and aggregation, enabling contemporaneous data availability and decoupling of raw and aggregated data access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional centralized data warehousing approach is used, then data can be collected and processed, but processing time increases significantly and data availability becomes delayed
Solution Approach 1:
The patent divides the centralized data processing system into a hierarchical tree structure with multiple levels of node entities. Leaf node entities collect data from sources, intermediate nodes aggregate data, and root nodes provide centralized access. This segmentation distributes processing tasks across the hierarchy, reducing the bottleneck of centralized processing and enabling faster data availability while maintaining systematic organization.
2Productivity
If traditional centralized data warehousing approach is used, then data can be processed, but system complexity and cost increase
Solution Approach 1:
The patent implements node entities that serve multiple functions within the hierarchical tree. Each node entity can collect data, aggregate data, store data, and provide access to multiple levels of the hierarchy. This multi-functionality reduces the need for separate specialized components, simplifying the overall system architecture while maintaining robust data processing capabilities.
3Quantity of substance
If traditional approach processes large amounts of data, then data can be aggregated, but scalability becomes difficult
Solution Approach 1:
The hierarchical tree structure provides dynamic scalability through its layered architecture. New node entities can be added at any level of the hierarchy to handle increased data volumes. The structure naturally accommodates growth by allowing intermediate nodes to aggregate data from multiple leaf nodes, enabling the system to scale with data quantity while maintaining processing efficiency.
4Quantity of substance
If traditional approach processes large amounts of data, then data can be collected, but data quality and completeness decrease
Solution Approach 1:
The patent performs data aggregation at intermediate nodes before data reaches the root node or is made available to users. This preliminary aggregation ensures that data is processed, validated, and organized in advance, maintaining data quality and completeness even as data volumes increase. The hierarchical structure allows for progressive data refinement that prevents information loss.
Data Source
AI summary
A hierarchial tree is provided to harvest data from data sources. The hierarchial tree includes multiple tree node entities arranged in multiple levels. In one case, leaf node entities of the hierarchial tree are used to collect data from the data sources. The hierarchial tree includes storage resources for storing the collected data. The hierarchial tree further includes computing resources for aggregating the collecting data in one or more aggregation operations and performing other processing operations. A receiving entity can selectively and independently receive parts of the collected data and aggregated data.