Scoring Engine for Vehicle Data Correlation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In the vehicle sales industry, existing systems face challenges in accurately determining which user data provided by vehicle data systems leads to actual sales, making it difficult for operators to bill dealers effectively and efficiently.
Innovation Solution
A vehicle data system with a scoring engine that correlates and enhances data from distributed sources across a network, providing confidence scores for user-sale matches to accurately identify sales-user pairs, even in the absence of historical records, and efficiently filters potential pairs to detect valid sales in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is collected from multiple distributed sources, then the quantity and variety of data increases, but the difficulty of correlating and matching data across sources increases
Solution Approach 1:
The system segments the data correlation task by creating separate data stores for different data types (user data, vehicle data, sales data) and using a centralized processing system to correlate them. This modular approach allows complex data to be organized into manageable segments while maintaining correlation capabilities through standardized interfaces and processing pipelines.
2Speed
If real-time data processing is implemented, then the speed of sales match determination improves, but the computational resources and processing complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing data from multiple sources in structured data stores before the actual correlation task. User data, vehicle data, and sales data are collected, cleaned, and organized in advance, allowing the real-time matching process to operate on pre-prepared data rather than raw data, thus reducing computational complexity during real-time processing.
Solution Approach 2:
The processing system acts as an intermediary between the distributed data sources and the correlation task. It receives data from multiple sources, standardizes the data formats, stores them in a centralized data store, and then performs the correlation matching. This intermediary layer simplifies the complexity by centralizing the processing logic and providing a unified interface for data correlation.
3Measurement precision
If comprehensive data correlation is performed, then the accuracy of sales-user match identification improves, but the time required for processing increases
Solution Approach 1:
The correlation process is segmented into distinct stages: data collection from multiple sources, data storage in organized data stores, and correlation matching. This segmentation allows the system to process data in manageable batches and use efficient matching algorithms on pre-organized data, improving accuracy while controlling processing time through structured approach.
Solution Approach 2:
The system creates copies of data from distributed sources and stores them in a centralized data store. These copies can be processed independently and efficiently, allowing comprehensive correlation to be performed on replicated data without the time penalties of processing unique, unorganized data streams from multiple sources simultaneously.
Data Source
AI summary
Systems and method herein provide for sophisticated and efficient matching between users of a vehicle data system and data on sales that occur, where that data was obtained from distributed sources across a computer network. In particular, embodiments may obtain data from a variety of data sources across a distributed network and enhance data records by correlating the data obtained from these distributed sources. Data on sales thus obtain can be correlated with data on online users of the vehicle data system using a scoring engine to provide confidence scores to potential user-sale matches.