Cloud Rules Conflict Checking for Cross-System Data Validation
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Solution Overview
Problem
Inconsistent data validations across multiple systems lead to undetected data errors, making it difficult to identify and fix errors introduced at unknown points, increasing complexity and time, and negatively impacting other computer systems.
Innovation Solution
A data integrity and validation (DIV) system that validates data messages against a plurality of rules for multiple destination systems, identifying and correcting errors before transmission, and generating data messages that conform to the requirements of each system, providing a holistic view of data quality and error logging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If data is transmitted to multiple destination systems without centralized validation, then system complexity is reduced and ease of operation is improved, but data validation consistency deteriorates and errors increase
Solution Approach 1:
A centralized data validation and conflict checking system is introduced as an intermediary between data sources and multiple destination systems. This mediator receives data, validates it against rules from all destination systems, checks for conflicts, and ensures consistent data delivery to each system according to its specific requirements.
2Reliability
If centralized data validation is implemented for multiple destination systems, then data validation consistency is improved, but system complexity increases
Solution Approach 1:
The validation system is segmented into modular components: a rules management module that stores validation rules from multiple destination systems, a conflict detection module that identifies conflicts between rules, a validation engine that applies rules to data, and a communication interface that delivers validated data to destination systems. This segmentation reduces overall system complexity by dividing functions into manageable units.
3Device complexity
If data errors are allowed to propagate to destination systems, then system simplicity is maintained, but harmful effects increase and other systems are negatively impacted
Solution Approach 1:
The system performs preliminary validation and conflict checking before data is transmitted to destination systems. By validating data against rules from all destination systems in advance and detecting conflicts beforehand, the system prevents erroneous data from propagating to other systems, thereby eliminating harmful effects before they can occur.
4Measurement precision
If validation rules from multiple destination systems are collected and compared, then data accuracy is improved, but time consumption increases
Solution Approach 1:
Validation rules from multiple destination systems are collected, stored, and pre-processed in advance. The conflict detection module identifies and resolves conflicts between rules beforehand, creating a optimized validation framework. This preliminary action reduces validation time during actual data transmission by avoiding real-time conflict resolution.
Data Source
AI summary
A computer system for performing cloud-based enhanced rules conflict checking is provided. The computer system is programmed to store a plurality of rules for transmitting to a plurality of destination systems and receive a data message for transmission to the plurality of destination systems from a first requesting system. The computer system is programmed to compare the data message to a first set of rules to validate the data message and if the data message is validated for the first set of rules, instruct the first requesting system to transmit the data message to the plurality of destination systems. The computer system is further programmed to receive the data message for transmission to one or more remaining destination systems from a second requesting system and compare the data message to a second set of rules for validating the data message.