Recursive Rule Language for Schema Transformation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The complexity of integrating and converting data from different sources due to varying formats and schemas, which often requires technical knowledge and can be cumbersome, especially when non-technical users need to perform data transformations.
Innovation Solution
A programming-language independent language, referred to as Recursive Rule Language (RRL), is used to define data transformation specifications in the form of first-order logic statements that can be recursively referenced, allowing for straightforward definition and execution of complex schema transformations, facilitating data transformations by non-technical users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If data transformations are performed using traditional technical methods (SQL statements, spreadsheets, first order logic), then the transformation can be accomplished, but it requires technical knowledge and is cumbersome for non-technical users
Solution Approach 1:
The patent introduces a natural language processing intermediary that translates user-friendly natural language queries into executable SQL statements. This mediator layer allows non-technical users to interact with the system using simple English sentences rather than requiring knowledge of SQL syntax or complex transformation rules, thereby resolving the contradiction between ease of operation and technical complexity
2Adaptability or versatility
If complex schema transformations are implemented, then data integration capability is improved, but the process becomes more cumbersome and time-consuming
Solution Approach 1:
The patent implements preliminary action by pre-compiling and storing transformation rules in an optimized format during system initialization or setup phases. When actual data transformations are needed, the system retrieves and executes these pre-prepared rules rather than computing transformations from scratch, significantly reducing execution time while maintaining comprehensive schema transformation capabilities
3Manufacturing precision
If data transformations require detailed understanding of both technical aspects and semantic meaning, then transformation accuracy is improved, but it creates difficulty for users who may have one type of understanding but not the other
Solution Approach 1:
The natural language processing system serves as an intermediary that bridges the gap between semantic understanding and technical implementation. Users can express their intent in natural language (semantic layer), and the system automatically generates the appropriate technical transformation logic, thereby achieving high transformation accuracy without requiring users to possess both semantic and technical expertise simultaneously
Data Source
AI summary
Techniques and solutions are described for defining transformation specifications in a programming-language independent language and converting such specifications to one or more executable formats. The language can provide for defining rules and actions. Rules can refer to (e.g., be based at least in part on) data targets, such as attributes of a schema, whose identifiers are to be read or updated, or to other rules. Rules can be reused, and can recursively refer to one another, such that a large number of complex schema transformations can be accomplished using a series of first order logic statements. Actions can define what, and how, values will be changed when a predicate rule is satisfied. A transformation specification in the language can be parsed and selectively complied to one or more executable formats, including in programming languages such as the structured query language. Disclosed technologies can facilitate data transformations by non-technical users.