Frost: A Platform for Benchmarking and Exploring Data Matching Results

TL;DR

Frost is an open-source platform that enables comprehensive benchmarking and exploration of data matching solutions, integrating quality, cost, and effort metrics to improve data deduplication processes.

Contribution

It introduces a novel platform that combines benchmarks, metrics, and exploration tools for data matching, addressing gaps in existing evaluation methods.

Findings

Supports systematic exploration of matching results

Integrates multiple quality and cost metrics

Open-source implementation in Snowman

Abstract

"Bad" data has a direct impact on 88% of companies, with the average company losing 12% of its revenue due to it. Duplicates - multiple but different representations of the same real-world entities - are among the main reasons for poor data quality, so finding and configuring the right deduplication solution is essential. Existing data matching benchmarks focus on the quality of matching results and neglect other important factors, such as business requirements. Additionally, they often do not support the exploration of data matching results. To address this gap between the mere counting of record pairs vs. a comprehensive means to evaluate data matching solutions, we present the Frost platform. It combines existing benchmarks, established quality metrics, cost and effort metrics, and exploration techniques, making it the first platform to allow systematic exploration to understand matching results. Frost is implemented and published in the open-source application Snowman, which includes the visual exploration of matching results.