Detecting duplicate entities effectively necessitates strategies to minimize pairwise comparisons. The naive approach suffers from O(n²) complexity, making it impractical. Modern systems utilize candidate generation phases with blocking keys and hashing to focus on probable matches. Various blocking strategies, including standard and multi-pass methods, help in reducing comparisons while maintaining high recall. Standard Blocking groups records based on shared attributes, but may overlook duplicates in diverse datasets. Multi-pass methods enhance recall by utilizing multiple keys across passes, capturing more duplicate records in complex datasets.
Effective blocking dramatically cuts comparisons while still grouping true duplicates together. Several blocking strategies can be applied in multi-pass to improve recall.
Standard Blocking utilizes defined keys from record attributes to group records, allowing for focused comparisons and increased efficiency in duplication detection.
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