Clode Core Data Registry for Bandwidth Optimization
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Solution Overview
Problem
Modern cloud computing faces challenges in organizing and classifying data sets due to non-uniform commands and repetitive data transmissions, leading to increased bandwidth and processing demands, as well as inefficiencies in managing data relationships across different platforms.
Innovation Solution
A function and memory mapping registry with reactive management events, known as the Clode core, is introduced to classify and compare data, leveraging relationships and classifications to optimize data flow and consumption, while minimizing CPU bandwidth and enabling seamless integration across various programming languages and platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional data management methods are used in cloud computing, then data can be stored and accessed, but repetitive data transmissions increase bandwidth consumption and processing demands
Solution Approach 1:
The patent implements preliminary action by pre-classifying data into hierarchical groups and categories before transmission occurs. The data management system organizes data structures with metadata tags and relationships in advance, allowing the system to retrieve and transmit only relevant data subsets rather than repeatedly transmitting entire datasets. This pre-organization significantly reduces bandwidth consumption and processing demands during actual data operations.
Solution Approach 2:
The patent applies segmentation by dividing data into hierarchical segments with different levels of classification (groups, categories, sub-categories). Each data element is tagged with metadata indicating its hierarchical position and relationships. This segmentation allows the system to transmit only the necessary data portions rather than complete datasets, reducing repetitive transmissions and improving transmission efficiency while maintaining data accessibility.
2Productivity
If data is organized with detailed classifications and relationships, then data management efficiency improves, but system complexity increases
Solution Approach 1:
The patent implements universality by creating a hierarchical data classification system that serves multiple functions simultaneously. The same hierarchical structure and metadata tagging mechanism supports data organization, relationship management, retrieval optimization, and transmission efficiency. This multi-functional approach improves data management efficiency without proportionally increasing system complexity, as a single unified system handles multiple data management tasks.
Solution Approach 2:
The patent uses metadata as an intermediary layer between raw data and the data management system. Metadata tags and hierarchical classifications act as mediators that enable efficient data organization and retrieval without requiring complex direct management of the underlying data structures. This intermediary layer simplifies the system architecture while maintaining high data management efficiency through structured organization.
3Reliability
If real-time data updates are implemented across multiple systems, then data currency is improved, but processing demands and CPU bandwidth increase
Solution Approach 1:
The patent applies preliminary action by pre-establishing hierarchical data relationships and metadata structures before real-time updates are needed. When data changes occur, the system uses the pre-built hierarchical framework to efficiently identify and propagate only the affected data subsets across systems. This approach maintains real-time data currency while minimizing CPU bandwidth consumption by avoiding unnecessary full-system updates.
Solution Approach 2:
The patent implements partial action by updating only the specific data portions that have changed rather than performing complete system-wide synchronization. The hierarchical classification system enables the identification of minimal update sets, transmitting and processing only the necessary data changes. This partial update approach maintains data currency across multiple systems while significantly reducing processing demands and CPU bandwidth usage compared to full system updates.
Data Source
AI summary
A system for managing data content and data content relationships through resource efficient process structures for cloud and network environments includes a Clode core. The Clode core creates an object/display/process based on a defined order of processes determined by the object's tags and/or relationships to a function map and/or other objects in the system. This is used in Clode tag modules such as ‘clode:autopublish’ to create a data publication on the server, by ‘clode:autosubscribe’ to make client's automatically subscribe to data related to the tagged object, and by ‘clode:surface’ to manage an object on a display; just to name a few use cases. Tags can then be used to manage third party environments such as a docker container that would be classified as having the tag ‘on’, but when removed could cause the container to shut off. Tag management functions could also change the classifications as to say in the previous example to cause a tag to be added called ‘off’.