Dynamic Data Structures for Flexible Cloud Object Models
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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, with traits defining data models and exposing services and methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If a rigid data model with pre-defined objects is used, then data structure stability is improved, but adaptability deteriorates
Solution Approach 1:
The patent implements dynamic data models where data objects can be modified at runtime through trait addition and removal. The system allows users to add new traits to existing data objects without redefining the entire object structure, enabling the data model to adapt dynamically to changing requirements while maintaining the stability of the core object framework.
2Adaptability or versatility
If all services are included in a data object, then functionality is improved, but resource consumption increases
Solution Approach 1:
The patent extracts services from data objects into separate callable traits. Instead of embedding all services within each data object, the system allows services to be defined as independent traits that can be selectively added or removed. This enables users to include only the services they need, reducing resource consumption while maintaining full functionality when required.
3Reliability
If programmers define all data objects, then data model consistency is improved, but user flexibility deteriorates
Solution Approach 1:
The patent segments data objects into core objects and optional traits. Programmers define the core object structure to ensure consistency and reliability, while users can independently add or remove traits to customize functionality. This segmentation allows both programmers and users to work at appropriate levels of abstraction without compromising either consistency or flexibility.
4Adaptability or versatility
If custom data models are allowed, then user needs satisfaction is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal trait system that can be applied to any data object type. Instead of creating complex custom models for each user need, the system provides a set of reusable traits that can be combined in various ways to satisfy different requirements. This universal approach reduces system complexity by avoiding the need to create and maintain separate complex models for each customization scenario.
Data Source
AI summary
A method, article of manufacture, and apparatus for managing a cloud computing environment. In some embodiments, this includes creating an event configuration, wherein the event configuration determines the order in which event handlers of a trait are called, identifying a java class configuration, wherein the java class configuration determines the composition of an instance of an object, identifying a set of required traits, and storing the event configuration, the java class configuration, and the set of required traits in an XML document.