Dynamic Data Structures for Flexible Database Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional databases have rigid data models that require programmers to define data objects, leading to resource wastage and user frustration as users are forced to use unnecessary services.
Innovation Solution
Dynamic data structures allow for runtime addition and removal of traits to data objects, enabling flexible database models without pre-defined rigid structures, where traits can expose services and methods, and their definitions are stored in XML documents with event configurations and Java class configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If conventional databases use rigid pre-defined data models, then data structure stability is improved, but adaptability deteriorates
Solution Approach 1:
The patent implements dynamic data models where the data structure can be modified at runtime through trait additions and removals. The system transitions from static pre-defined schemas to dynamic configurations that adapt to user needs while maintaining structural integrity through controlled modification mechanisms.
Solution Approach 2:
The patent segments data objects into modular traits that can be independently added or removed. Each trait represents a discrete functional unit (e.g., authentication, billing) that can be selectively applied to data objects, enabling flexible composition without compromising overall structure stability.
2Manufacturing precision
If programmers define all data object parameters in advance, then manufacturing precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent enables end-users to self-customize data objects by selecting and applying traits according to their specific needs. Users can modify data structures without programmer intervention, allowing precise control over data models while simplifying operations through intuitive trait-based configuration.
Solution Approach 2:
The patent pre-defines standardized traits with established schemas and validation rules. These pre-configured traits maintain data definition precision while allowing users to easily combine them in custom configurations, eliminating the need for users to define complex data structures from scratch.
3Adaptability or versatility
If data objects include all possible services, then adaptability is improved, but loss of substance deteriorates
Solution Approach 1:
The patent extracts only the necessary services into selectable traits that are added to data objects on-demand. Instead of embedding all possible services in every data object, the system allows users to extract and include only the specific traits needed for their use case, eliminating resource wastage while maintaining adaptability.
Solution Approach 2:
The patent implements dynamic service inclusion where data objects can add or remove traits at runtime based on actual needs. This dynamic approach ensures that services are active only when required, preventing resource wastage from unnecessary service executions while maintaining full adaptability when services are needed.
4Device complexity
If users are forced to use pre-defined data models, then device complexity is reduced, but ease of operation deteriorates
Solution Approach 1:
The patent creates a universal trait system that can be applied across multiple data objects and use cases. The same set of standardized traits serves diverse functionality needs, reducing the number of unique data model definitions required while enabling users to easily customize objects by selecting from this universal trait library.
Data Source
AI summary
A method, article of manufacture, and apparatus for managing a cloud computing environment. In some embodiments, this includes identifying a namespace, identifying an event handler, identifying a java class configuration, and storing the namespace, event handler, and java class configuration in an XML document.