The data being collected by organisations is increasing relentlessly, but it still can give a misleading or fragmented view of the real world. A person could have multiple digital entities within the same database, due to typos, name changes, aggregation of different systems and so on. If we try to merge two databases, how do we match entities, when the ID systems might be different or contain errors?

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In-database machine learning for big data entity resolution

Supplied by on Tuesday, 26 October, 2021


The data being collected by organisations is increasing relentlessly, but it still can give a misleading or fragmented view of the real world. A person could have multiple digital entities within the same database, due to typos, name changes, aggregation of different systems and so on. If we try to merge two databases, how do we match entities, when the ID systems might be different or contain errors?

Learn about an efficient approach for the entity resolution problem. A native graph database with massive parallel computing capability is the best tool to implement the approach.


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