Automated Data Translation Using Extent Meta-Structure
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Solution Overview
Problem
Existing data-type conversion techniques for database management systems are time-consuming, prone to errors, and require human intervention, limiting the automation of data replication processes due to their reliance on canonical APIs or data definitions, which do not account for variations in data types across different DBMS applications.
Innovation Solution
The introduction of a meta-structure called 'Extent' that uses a declarative system for data-type conversions, enabling automated processes to handle both common and unusual data types with full fidelity by encoding structural information and providing a meta-type system that allows for optimal data format determination without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data-type conversion techniques are used, then data fidelity can be maintained, but the process becomes time-consuming and requires human intervention
Solution Approach 1:
The system employs automated reflection processes and type-matching algorithms that enable the data conversion system to self-determine appropriate target data types without human intervention. The reflection process automatically inspects source data type properties and matches them with compatible target data types, eliminating the need for manual conversion while maintaining data fidelity through systematic type equivalence determination.
Solution Approach 2:
The invention changes the fundamental parameters of the conversion process by introducing a declarative type system with structured type definitions that include properties such as size, precision, and scale. By parameterizing data types with these explicit attributes, the system can automatically compare and match types across different DBMS platforms, enabling fast and accurate conversion without manual intervention.
2Productivity
If automated data replication processes are implemented, then productivity increases, but errors occur due to lack of understanding of data type equivalences
Solution Approach 1:
The system introduces an intermediary layer consisting of structured type definitions and reflection mechanisms that mediate between source and target DBMS systems. This intermediary automatically analyzes source data types, determines their semantic meaning through reflection, and matches them with appropriate target data types using predefined equivalence rules, thereby enabling automated conversion while maintaining high accuracy through systematic type mapping.
Solution Approach 2:
The invention segments the data type conversion process into distinct, manageable components: reflection phase (inspecting source type properties), matching phase (comparing with target type definitions), and conversion phase (executing the actual transformation). This segmentation allows each component to be optimized independently and facilitates automated execution while maintaining conversion accuracy through systematic processing of type equivalences.
3Manufacturing precision
If human intervention is used for data-type conversions, then conversion accuracy is maintained, but device complexity increases due to manual processes
Solution Approach 1:
The system replaces manual human processes with automated computational mechanisms including reflection algorithms and type-matching logic. These mechanical substitutes automatically inspect data type properties, compare them against predefined type definitions, and determine appropriate conversions without human intervention, thereby maintaining conversion precision while reducing process complexity through systematic automation.
4Ease of operation
If canonical APIs are used for data conversion, then ease of operation is improved, but adaptability decreases due to inability to handle unusual data types
Solution Approach 1:
The invention creates a universal type system that can handle both common and unusual data types across different DBMS platforms. The structured type definitions include extensible properties that accommodate specialized data types, while the reflection mechanism provides a unified approach to inspecting and matching any data type. This universal framework maintains ease of operation through consistent automated processes while achieving broad adaptability to handle diverse data type scenarios.
Data Source
AI summary
An embodiment includes locating source data associated with a data replication request, and identifying a property of a data value of the source data using a reflection process. The embodiment determines a meta-type for the data value using an automaton that identifies a matching extent class from among an indexed set of extent classes that define respective accessors that are invoked to locate an extent class that corresponds with the property of the data value. The embodiment generates target data having the data value and a data type that corresponds with the matching extent class. The embodiment generates a changes feed that includes the generated target data, evaluates data in the changes feed to determine whether a specified condition has been satisfied and, if so, generates a trigger signal that initiates an event, where the trigger signal comprising information that satisfied the specified condition.