Schema Matching System for Automated Rule Generation

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Solution Overview

Problem

Existing methods for generating correspondence rules to associate events from different systems processing transactions are error-prone and require manual user analysis, necessitating repeated efforts for each new system introduced into transaction processing.

Innovation Solution

A schema matching system processes training event data from multiple sources to determine correspondence rules using machine learning algorithms and similarity metrics, automatically matching fields across different schemas and generating normalization rules to improve matching accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual user analysis is used to generate correspondence rules, then the rules can be created for existing systems, but the process is error-prone and requires repeated manual effort for each new system

Engineering Contradiction:

Improveaccuracy of correspondence rulesVSAvoidtime required to generate correspondence rules

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs self-service by automatically generating correspondence rules through machine learning algorithms that analyze event data from multiple sources. The system learns patterns and relationships between different event schemas without requiring manual user analysis, thereby eliminating repeated manual effort while maintaining high accuracy through iterative learning and validation.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical manual analysis process with an automated computational system using machine learning algorithms. Instead of human users manually comparing and mapping event fields, the system uses computational models to automatically identify correspondences between event schemas from different sources, significantly reducing time and human effort while improving consistency and accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If manual correspondence rule generation is used, then correspondence can be established between events, but the process needs to be repeated for each new system introduced

Engineering Contradiction:

Improveability to handle new systemsVSAvoidefficiency of rule generation

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The system achieves universality by designing a machine learning model that can handle multiple event schemas from different sources simultaneously. The learned correspondence rules are generalizable and can be applied to new systems without requiring system-specific manual analysis, as the system has learned universal patterns of event relationships that transfer across different data sources and schemas.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system performs preliminary action by pre-processing and analyzing event data from multiple sources to learn correspondence patterns in advance. This preliminary learning phase enables the system to quickly adapt to new systems without repeated manual analysis, as the foundational correspondence rules have already been established through machine learning on diverse event data.

Inventive Principle:

Principle #10Preliminary action

3Ease of operation

If automated machine learning methods are used to generate correspondence rules, then manual effort is reduced, but the system requires training event data from multiple sources

Engineering Contradiction:

Improveease of rule generationVSAvoidcomplexity of data processing system

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The system uses an intermediary machine learning model that sits between the raw event data from multiple sources and the final correspondence rules. This intermediary layer processes and learns from the diverse event data, transforming it into standardized correspondence rules. The intermediary model handles the complexity of multi-source data integration, making the overall system easier to operate while managing the inherent complexity through structured learning processes.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS11436500B2Schema correspondence rule generation using machine learning

Publication Date: 2022.09.06 PEERNOVA INC

AI summary

A schema matching system processes training event data received from multiple sources to determine correspondence rules associating fields in the schemas of each source. To generate the correspondence rules, the schema matching system can use training event data from multiple sources comprising events associated with multiple schemas. Then, based on one or more similarity metrics between data entries of the training event data, the system matches individual events using a machine learning algorithm and, based on the pairs of matching events, corresponding schema fields across the multiple schemas. Based on the matching events and/or user feedback, the schema matching system can generate one or more correspondence rules based on the normalization rules and the corresponding fields of the schemas for later use by one or more transaction monitoring systems on incoming event data.