Transitive Customer ID Resolution Across Transaction Data Sets
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing composite customer databases often inaccurately reflect transaction records due to randomly generated IDs and data entry inconsistencies across multiple platforms, leading to confusion and errors in customer tracking, particularly in large enterprises with numerous transactions and interactions.
Innovation Solution
The system employs an identification tag transformation module with a transitive relationship resolver and a reflective ID resolver to accurately determine transitive relationships between customer IDs across different databases, sorting them into mutually exclusive sets to enhance data fidelity and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple separate customer databases are used for different platforms, then each platform can maintain its own data format and syntax, but data inconsistency and customer tracking errors occur across the enterprise
Solution Approach 1:
The patent introduces a composite customer database as an intermediary layer that receives data from multiple platform databases through synchronization. This mediator resolves data inconsistencies by implementing a unified customer identification system that maps random platform-specific IDs to consistent composite customer IDs, thereby maintaining platform flexibility while ensuring enterprise-wide data reliability and accurate customer tracking.
2Reliability
If randomly generated IDs are assigned to composite customer transaction records, then data security is enhanced, but error resolution and data tracing become confusing and difficult
Solution Approach 1:
The patent segments the identification system into two distinct layers: composite customer IDs that provide secure, randomized identification for data protection, and platform-specific transaction IDs that maintain intuitive, traceable references. This segmentation allows the system to simultaneously achieve data security through randomized composite IDs while preserving ease of error resolution through the maintainable platform-specific ID mappings stored in the composite database.
3Quantity of substance
If data synchronization is performed across multiple platforms, then comprehensive customer transaction records are created, but data entry inconsistencies and formatting differences cause recording errors
Solution Approach 1:
The patent applies parameter changes by transforming heterogeneous data from multiple platforms into a standardized format within the composite customer database. The system implements unified data schemas, consistent identification protocols, and standardized field mappings that convert varying platform-specific data formats into consistent composite records, thereby maintaining comprehensive transaction coverage while significantly improving data entry accuracy and reducing formatting inconsistencies.
Data Source
AI summary
Techniques for fully or comprehensively resolving relationships within and among different data sets are disclosed. The techniques evaluate identification tags of respective transaction records to resolve transitive relationships between and among the identification tags, which vary across the different data sets. Based on this resolving, the techniques comprehensively sort the identification tags into mutually exclusive sets of related identification tags. Additionally, the techniques resolve and sort identification tags that are transitively unrelated to every other identification tag. At least these features enable comprehensive identification and intelligent sorting of related/unrelated transaction records, provide enhanced transaction tracking and security, more efficient and accurate transaction record storage (and in particular, of composite records), and enhanced tracking accuracy of accounts or types of units as compared to known techniques. The techniques utilize recursive resolution to comprehensively and accurately resolve all data relationships among the different data sets.