The OSS ER Bargain: What Entity Resolution Actually Costs You

The benchmark and comparison between GoldenMatch and dedupe for entity resolution (ER) on a large dataset like NPPES highlights several important aspects of using machine learning libraries for complex tasks such as duplicate detection. Here are the key points to consider:

1. Scalability:

   • GoldenMatch: Demonstrates linear scalability with respect to data size, completing a 500k row task in under 4 minutes and consuming around 2GB of RAM.
   • dedupe: Fails to produce meaningful results on the same dataset within a reasonable timeframe. The tool likely consumes significantly more resources but does not provide useful output.

2. Configuration Sensitivity:

   • GoldenMatch: Shows sensitivity to configuration parameters like threshold, which is expected and indicates that these knobs have real control over the outcome.
   • dedupe: Lacks meaningful sensitivity analysis because it produces trivial outputs (all singletons) regardless of parameter changes due to insufficient positive training data.

3. Holistic Approach:

   • GoldenMatch: Takes a holistic approach by handling internal complexities like indexing, fallback strategies for oversized blocks, and decision-making without requiring user intervention.
   • dedupe: Requires the user to manage

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