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 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 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 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 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 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 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.

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: Some implementations may provide a computer-assisted method for master data management, the method including: receiving configuration information defining a model of entities, each entity encoding attributes of a prescriber of one or more healthcare products; receiving specification information defining mapping logic, searching logic, and matching logic, and merging logic for processing base entities and related entities of the model; receiving data from more than one source customer databases, the customer database including data encoding prescribers of healthcare products and being maintained by more than one organizations; translating the received data into staging data according to the mapping logic in the received specification information; generating master data by processing the staging data according to the searching logic, matching logic, and merging logic in the received specification information; and synchronizing at least a portion of the master data to at least one of the source customer database

Abstract: The disclosure generally describes computer-implemented methods, software, and systems for handling data access request, including: receiving a request submitted by a user to access data entries stored at a primary data server; determining whether the received request is supported by a fast query service engine, the fast query service engine being different from the primary data server; in response to determining that the received request is supported by the fast query service engine, directing the request to the fast query service engine; receiving query results from the fast query service engine, the query results composed from more than one answer sets maintained in in-memory data store at the fast query service engine, the answer sets the in-memory data-store being synchronized with the primary data server; and providing the query results to the user submitting the request.

Abstract: The disclosure generally describes computer-implemented methods, software, and systems for receiving and aggregating anonymized data reports about when prescribers provide wireless device applications to patients. The disclosure discusses ways of analyzing the data reports in combination with other electronic medical information to generate useful conclusions about scenarios in the health care process.

Abstract: The disclosure generally describes computer-implemented methods, software, and systems for receiving and aggregating anonymized data reports about when prescribers provide wireless device applications to patients. The disclosure discusses ways of analyzing the data reports in combination with other electronic medical information to generate useful conclusions about scenarios in the health care process.

Abstract: Some implementations may provide a computer-assisted method for master data management, the method including: receiving configuration information defining a model of entities, each entity encoding attributes of a prescriber of one or more healthcare products; receiving specification information defining mapping logic, searching logic, and matching logic, and merging logic for processing base entities and related entities of the model; receiving data from more than one source customer databases, the customer database including data encoding prescribers of healthcare products and being maintained by more than one organizations; translating the received data into staging data according to the mapping logic in the received specification information; generating master data by processing the staging data according to the searching logic, matching logic, and merging logic in the received specification information; and synchronizing at least a portion of the master data to at least one of the source customer database

Abstract: Some implementations may provide a computer-assisted method for master data management, the method including: receiving configuration information defining a model of entities, each entity encoding attributes of a prescriber of one or more healthcare products; receiving specification information defining mapping logic, searching logic, and matching logic, and merging logic for processing base entities and related entities of the model; receiving data from more than one source customer databases, the customer database including data encoding prescribers of healthcare products and being maintained by more than one organizations; translating the received data into staging data according to the mapping logic in the received specification information; generating master data by processing the staging data according to the searching logic, matching logic, and merging logic in the received specification information; and synchronizing at least a portion of the master data to at least one of the source customer database