Ambiguous Data Matching Engine for Source Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Organizations face inefficiencies in determining the source of ambiguous data, such as firmographic data, which hinders the positive identification of business partners and retrieval of relevant contextual data, leading to suboptimal resource utilization.
Innovation Solution
A system and method involving a matching platform that receives ambiguous data, persists it in a data store, uses a matching engine with a machine learning model to identify a source identifier, and provides it to a user device, leveraging interfaces like graphical forms, APIs, and event streaming platforms for data processing and storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional queries are used to search for source identifiers using ambiguous data, then the search process can be performed, but resource utilization becomes inefficient and the process is time-consuming
Solution Approach 1:
The system pre-computes and stores similarity scores between ambiguous data and production data in advance. When a query is received, the pre-computed scores are retrieved and used to quickly identify the source identifier without performing time-consuming similarity calculations at query time, thus resolving the contradiction between search efficiency and time consumption
Solution Approach 2:
The system combines multiple data attributes (firmographic data, business process data, contextual data) into a unified similarity score. This merging of multiple data sources and attributes into a single composite metric enables efficient comparison and quick identification of source identifiers, improving productivity while reducing the time required for source identification
2Loss of information
If comprehensive firmographic data is stored for all partner organizations, then complete information is available, but the complexity of managing and querying this data increases
Solution Approach 1:
The system introduces a matching engine as an intermediary layer between the production data store and query interfaces. This matching engine pre-computes similarity scores and maintains indexed relationships between ambiguous data and source identifiers, acting as a mediator that simplifies query operations while preserving access to comprehensive firmographic data, thus resolving the contradiction between information availability and management complexity
Solution Approach 2:
The system segments the comprehensive firmographic data into structured components (ambiguous data, contextual data, source identifiers) and organizes them in a hierarchical data store structure. This segmentation enables efficient indexing and retrieval operations, reducing the complexity of managing large volumes of data while maintaining complete information availability
Data Source
AI summary
In some aspects, the techniques described herein relate to a method including: receiving ambiguous data at an interface of a matching platform; persisting the ambiguous data to a receiving data store of a matching platform; providing the ambiguous data as input to a matching engine; matching, by the matching engine, the ambiguous data to data in a production data store; retrieving, by the matching engine, a source identifier associated with the data in the production data store; and providing the source identifier to a user device in operative communication with the matching platform.