Blocking is a technique used in data analysis, particularly in record linkage and deduplication, to reduce the number of comparisons required between records. By dividing the dataset into smaller subsets or “blocks” based on specific attributes, you can significantly reduce the computational effort and increase the speed of analysis.
In the context of record linkage or deduplication, comparing each record to every other record in a dataset can be computationally expensive, especially when dealing with large datasets. The time complexity for such an approach is O(n^2), where n is the number of records. As the dataset grows, the number of comparisons increases quadratically, leading to longer processing times.
Blocking helps overcome this issue by grouping records with similar attributes, so that only the records within the same block are compared. This reduces the total number of comparisons, as you are no longer comparing every record to every other record in the dataset. Instead, you are only comparing records within their respective blocks.
For example, when comparing addresses in a dataset, you could create blocks based on the first three characters of the postal code. Records with the same first three characters in their postal code would be placed in the same block. This way, you would only need to compare records within the same block, as it is unlikely that two records with completely different postal codes represent the same entity.
By reducing the number of comparisons, blocking can significantly increase the speed of analysis. However, it’s essential to choose the blocking criteria carefully, as poor blocking choices can lead to missed matches or false positives. The ideal blocking criteria should maximize the chances of finding true matches within blocks while minimizing the risk of missing matches across blocks.