Schema-Derived Logic Generation Without Physical Dataset Access
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Solution Overview
Problem
Existing technologies face challenges in efficiently generating logic from a schema, particularly in accessing and processing datasets without physically accessing the underlying data, which can lead to increased computational resources and security concerns.
Innovation Solution
A method is implemented that accesses a schema to identify relationships among datasets, generates logical data representing the attributes and relationships of the datasets, and provides this logical data to a development environment. This allows for the generation of a computer program that can perform operations by accessing specific datasets without needing to access all datasets physically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physical datasets are accessed for data processing, then complete data access is achieved, but computational resources increase and security risks arise
Solution Approach 1:
The patent creates logical data as a copy or representation of physical datasets, allowing operations to be performed on the logical data structure without accessing the actual physical data. This reduces computational resource usage and security risks while maintaining the ability to perform required operations.
Solution Approach 2:
The patent introduces logical data as an intermediary layer between the query system and physical datasets. This intermediary allows the system to determine data access requirements without directly accessing physical datasets, reducing unnecessary computational resources and security exposures.
2Productivity
If all datasets are accessed physically, then complete data availability is ensured, but access time and computational overhead increase
Solution Approach 1:
The patent extracts only the necessary data access information from the complete dataset schema, creating a streamlined logical data representation. This allows the system to work with a subset of required data characteristics rather than accessing entire physical datasets, significantly reducing access time while maintaining productivity.
Solution Approach 2:
The patent implements partial action by accessing only the specific logical data elements needed for a given operation rather than accessing all physical datasets. This selective approach reduces access time while providing sufficient data for productive operations.
3Reliability
If logical data is generated to represent datasets, then security is enhanced and resources are reduced, but system complexity increases
Solution Approach 1:
The patent creates a universal logical data structure that can represent multiple different physical datasets through a common interface. This multi-functional approach allows the same logical data mechanisms to handle various data types and operations, reducing overall system complexity while maintaining security and resource efficiency.
Data Source
AI summary
A method includes accessing a schema that specifies relationships among datasets, computations on the datasets, or transformations of the datasets, selecting a dataset from among the datasets, and identifying, from the schema, other datasets that are related to the selected dataset. Attributes of the datasets are identified, and logical data representing the identified attributes and relationships among the attributes is generated. The logical data is provided to a development environment, which provides access to portions of the logical data representing the identified attributes. A specification that specifies at least one of the identified attributes in performing an operation is received from the development environment. Based on the specification and the relationships among the identified attributes represented by the logical data, a computer program is generated to perform the operation by accessing, from storage, at least one dataset having the at least one of the attributes specified in the specification.