Abstract: Disclosed are techniques for trimming large clusters of related records. In one embodiment, a method is disclosed comprising receiving a set of clusters, each cluster in the clusters including a plurality of records. The method extracts an oversized cluster in the set of clusters and performs a breadth-first search (BFS) on the oversized cluster to generate a list of visited records. The method terminates the BFS upon determining that the size of the list of visited records exceeds a maximum size and generates a new cluster from the list of visited records and adding the new cluster to the set of clusters. By recursively performing BFS traverse over the oversized cluster and extracting smaller new clusters from it, the oversized cluster is eventually partitioned into a set of sub-clusters with the size smaller than the predefined threshold.

Abstract: The present disclosure relates to merging database tables. Systems and methods may involve performing a comparison between the first set of records and the second set of records and identifying a plurality of record pairs based on the comparison. Each record pair may comprise a record in the first set of records and a record in the second set of records. In addition, A feature signature may be generated for each record pair by comparing field values in each record pair. The feature signature may be classified to identify at least one related record pair. A merged database table may be generated such that it comprises the at least one related record pair and comprises a set of unique records among selected from the first set of records and the second set of records.

Abstract: The present disclosure relates to methods and systems for applying version control of configurations to a software application, such as, a cloud-based application. Each version may be stored as a plurality of configuration nodes within a configuration tree structure. Version changes may lead to the creation or modification of configuration nodes. Configurations may be tested in a sandbox and undergo validation checks before being applied to the software application.

Abstract: Disclosed are techniques for generating features to train a predictive model to predict a customer lifetime value or churn rate. In one embodiment, a method is disclosed comprising receiving a record that includes a plurality of fields and selecting a value associated with a selected field in the plurality of fields. The method then queries a lookup table comprising a mapping of values to aggregated statistics using the value and receives an aggregated statistic based on the querying. Next, the method generates a feature vector by annotating the record with the aggregated statistic. The method uses this feature vector as an input to a predictive model.

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: Disclosed are techniques for trimming large clusters of related records. In one embodiment, a method is disclosed comprising receiving a set of clusters, each cluster in the clusters including a plurality of records. The method extracts an oversized cluster in the set of clusters and performs a breadth-first search (BFS) on the oversized cluster to generate a list of visited records. The method terminates the BFS upon determining that the size of the list of visited records exceeds a maximum size and generates a new cluster from the list of visited records and adding the new cluster to the set of clusters. By recursively performing BFS traverse over the oversized cluster and extracting smaller new clusters from it, the oversized cluster is eventually partitioned into a set of sub-clusters with the size smaller than the predefined threshold.

Abstract: The present disclosure relates to methods and systems for applying version control of configurations to a software application, such as, a cloud-based application. Each version may be stored as a plurality of configuration nodes within a configuration tree structure. Versions are tracked in a configuration version history. Different versions may be merged together and applied to the software application.

Abstract: The present disclosure relates to fuse multiple database tables together. The fields of the database tables may be normalized using semantic fields. Under a first approach, database tables are deduplicated by consolidating redundant records. This may be done by performing pairwise comparisons to identify related pairs of records and then clustering the related pairs of records. Then, the deduplicated database tables are merged by performing another pairwise comparison. Under a second approach, the database tables may be concatenated. Thereafter, records are subject to pairwise comparisons and then clustered to create a merged database table.

Abstract: The present disclosure relates to merging database tables. Systems and methods may involve performing a comparison between the first set of records and the second set of records and identifying a plurality of record pairs based on the comparison. Each record pair may comprise a record in the first set of records and a record in the second set of records. In addition, A feature signature may be generated for each record pair by comparing field values in each record pair. The feature signature may be classified to identify at least one related record pair. A merged database table may be generated such that it comprises the at least one related record pair and comprises a set of unique records among selected from the first set of records and the second set of records.

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 methods and systems for applying version control of configurations to a software application, such as, a cloud-based application. Each version may be stored as a plurality of configuration nodes within a configuration tree structure. Versions are tracked in a configuration version history. Different versions may be merged together and applied to the software application.

Abstract: The present disclosure relates to methods and systems for applying version control of configurations to a software application, such as, a cloud-based application. Each version may be stored as a plurality of configuration nodes within a configuration tree structure. Version changes may lead to the creation or modification of configuration nodes. Configurations may be tested in a sandbox and undergo validation checks before being applied to the software application.

Abstract: The present disclosure relates clustering similar data records together in a hierarchical clustering scheme. Each tier in a cluster corresponds to a minimal match score, which reflects a degree of confidence. In this respect, a higher confidence may lead to smaller sized clusters while a lower confidence may lead to larger sized clusters. Ordinal classification may be used to generate hierarchical clusters. In some embodiments, hierarchical clustering with conflict resolution is used to resolve user-defined hard conflicts in each tier of the clustering results.

Abstract: The present disclosure relates clustering similar data records together in a hierarchical clustering scheme. Each tier in a cluster corresponds to a minimal match score, which reflects a degree of confidence. A hierarchical cluster ID is generated for respective data records. The hierarchical cluster ID may be made up of a series of values, wherein each value reflects a tier within the hierarchical clustering scheme. A user may enter a partial hierarchical cluster ID to select clusters associated with a lower confidence. Thus, in some embodiments, the hierarchical cluster ID is variable in length in a manner that corresponds to the tiers in the hierarchical clustering scheme.

Abstract: The present disclosure relates to fuse multiple database tables together. The fields of the database tables may be normalized using semantic fields. Under a first approach, database tables are deduplicated by consolidating redundant records. This may be done by performing pairwise comparisons to identify related pairs of records and then clustering the related pairs of records. Then, the deduplicated database tables are merged by performing another pairwise comparison. Under a second approach, the database tables may be concatenated. Thereafter, records are subject to pairwise comparisons and then clustered to create a merged database table.

Abstract: The present disclosure relates to dynamically merging database tables according to user specified parameters. A user may specify a threshold confidence level that relates to a likelihood that two database records represent the same real-world entity. In addition, a user may specify a merge rule such as desired fields or a manner for consolidating the variations of the information in desired fields from the related records. The original database tables are preserved so that users can iteratively create new dynamically merged database tables by varying the parameters.

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.