An automatic blocking mechanism for large-scale de-duplication tasks

De-duplication - identification of distinct records referring to the same real-world entity - is a well-known challenge in data integration. Since very large datasets prohibit the comparison of every pair of records, blocking has been identified as a technique of dividing the dataset for pairwise comparisons, thereby trading off recall of identified duplicates for efficiency. Traditional de-duplication tasks, while challenging, typically involved a fixed schema such as Census data or medical records. However, with the presence of large, diverse sets of structured data on the web and the need to organize it effectively on content portals, de-duplication systems need to scale in a new dimension to handle a large number of schemas, tasks and data sets, while handling ever larger problem sizes. In addition, when working in a map-reduce framework it is important that canopy formation be implemented as a hash function, making the canopy design problem more challenging. We present CBLOCK, a system that addresses these challenges.