Data Quality Framework Module for Protocol Buffers
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Solution Overview
Problem
In conventional publish-subscribe messaging environments, low-quality data is often published before being consumed by downstream systems, leading to potential errors and inefficiencies, as corrections require reprocessing across multiple systems.
Innovation Solution
Implementing a data quality framework module that uses natural rule language to model and embed data quality rules within protocol buffer classes, allowing for early detection and prevention of low-quality data publication, with threshold-based acceptance or rejection mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is published immediately without quality validation, then publishing speed is improved, but data quality deteriorates
Solution Approach 1:
The patent applies preliminary action by validating data quality before publication. The system checks data against predefined quality rules and thresholds prior to publishing, ensuring only high-quality data enters the messaging bus. This prevents low-quality data from causing downstream issues while maintaining efficient publishing operations.
2Manufacturing precision
If data quality validation is performed before publication, then data quality is improved, but publishing time is increased
Solution Approach 1:
The patent applies parameter changes by implementing configurable quality thresholds and validation parameters. The system allows dynamic adjustment of quality criteria and validation intensity based on business needs, enabling optimization between data quality requirements and publishing speed. This flexibility allows the system to adapt validation strictness to minimize time loss while maintaining adequate quality.
3Productivity
If low quality data is published, then publishing efficiency is improved, but downstream system reliability deteriorates
Solution Approach 1:
The patent applies feedback mechanisms by implementing real-time data quality validation that provides immediate feedback on data quality status. The system continuously monitors published data against quality rules and can reject or correct low-quality data before it affects downstream systems, thereby maintaining both publishing efficiency and system reliability.
4Manufacturing precision
If data quality rules are embedded in protocol buffer classes, then data quality control is improved, but system complexity is increased
Solution Approach 1:
The patent applies merging by integrating data quality validation rules directly into the protocol buffer class definitions. This combines the data structure definition with quality validation logic in a unified component, eliminating the need for separate validation systems and reducing overall architectural complexity despite adding quality control capabilities.
Data Source
AI summary
Various methods, apparatuses, and media for implementing a data quality framework (DQ rules) module are provided. A processor is configured to model data quality rules using a natural rule language (NRL) as constraints on a plurality of communication models. The processor generates protocol buffer definitions from the plurality of communication models to create a protocol buffer class. The protocol buffer class is utilized to create a message by a publication application. The message is to be transmitted over a publish-subscribe messaging bus to a server. The processor embeds the data quality rules within the protocol buffer class and determines a quality of the message by evaluating the data quality rules against the message.