Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A complex event query specification may be constructed that a complex event processing engine uses to analyze an event stream for an occurrence of a complex event. Event data stored in a distributed file system are mapped to respective instances of the complex event processing engine in respective distributed event streams. The distributed event streams are analyzed by the independently executing complex event processing engines in accordance with the complex event query specification. The occurrence of the complex event in any of the distributed event streams is indicated at the output of the complex event processing engines.