Amperity, Inc.

Other

Michael P Fikes

Yan Yan

Stephen Meyles

Dan Suciu

Mona Akmal

The present disclosure relates to optimizing one or more database tables that may include one or more redundant records. Records are clustered and assigned stable identifiers. In this manner, the underlying records within a cluster are not removed or deleted. As updates to the database are made, new clustering analyses are performed using the underlying records and any updates made. Newly identified clusters are reassigned stable identifiers.

Maintaining stable record identifiers in the presence of updated data records

The present disclosure relates to evaluating whether two data records reflect the same entity using a classifier in the absence of ground truth. Without ground truth, it is difficult to determine the precision or recall of a classifier. The present disclosure generates a list comprising a series of unique feature signatures and a set of sample record pairs for each unique feature signature. In some embodiments, users may provide labels for the set of sample record pairs for each unique feature signature.

Constructing ground truth when classifying data

The present disclosure relates to evaluating whether two data records reflect the same entity using a classifier in the absence of ground truth. Without ground truth, it is difficult to determine the precision or recall of a classifier. The present disclosure generates output data comprising a list of unique signatures generated from a set of records that are compared with each other. The output data may also comprise corresponding record pairs limited to a predetermined sample size for each unique feature signature.

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Seattle, WA, United States of America

Michael P Fikes