Abstract: Disclosed is an approach comprising a column partitioned into a plurality of partitions including an empty partition and a plurality of filled partitions each comprising data entries associated with a set of parameters having parameter values, the data entries compressed in accordance with a compression dictionary. The approach comprises receiving forecasted parameter values for an expected set of data entries to be stored in an empty partition; predicting a recurrence frequency of the data entries in the expected set using the forecasted parameter values by evaluating the respective compression dictionaries of the filled partitions with a machine learning algorithm; generating a predictive compression dictionary for the expected set of data entries based on the predicted recurrence frequency of the data entries in the expected set; receiving the expected set of data entries; and compressing at least part of the received expected set of data entries using the predictive compression dictionary.

Abstract: Disclosed is an approach comprising a column partitioned into a plurality of partitions including an empty partition and a plurality of filled partitions each comprising data entries associated with a set of parameters having parameter values, the data entries compressed in accordance with a compression dictionary. The approach comprises receiving forecasted parameter values for an expected set of data entries to be stored in an empty partition; predicting a recurrence frequency of the data entries in the expected set using the forecasted parameter values by evaluating the respective compression dictionaries of the filled partitions with a machine learning algorithm; generating a predictive compression dictionary for the expected set of data entries based on the predicted recurrence frequency of the data entries in the expected set; receiving the expected set of data entries; and compressing at least part of the received expected set of data entries using the predictive compression dictionary.

Abstract: Disclosed is a computer-implemented method of compressing data in a columnar database comprising at least one column partitioned into a plurality of partitions including at least one empty partition and a plurality of filled partitions each comprising data entries associated with a set of parameters having parameter values relevant to the recurrence frequency of the data entry in the partition, the data entries being compressed in accordance with a compression dictionary based on the respective recurrence frequencies of the data entries in the filled partition.

Abstract: In an exemplary embodiment of this disclosure, a method for loading data from a backup image of a database includes selecting a subset statement defining a subset of the data in the database. Tables of the database are identified based on metadata of the database. A target database is written having the structure but not the data of the identified tables. One or more table statements are constructed, by a computer processor, defining a subset of each identified table based on the subset statement. Selected data is unloaded from a backup image into the target database using respective table statements as filters.

Abstract: Disclosed is a computer-implemented method of compressing data in a columnar database comprising at least one column partitioned into a plurality of partitions including at least one empty partition and a plurality of filled partitions each comprising data entries associated with a set of parameters having parameter values relevant to the recurrence frequency of the data entry in the partition, the data entries being compressed in accordance with a compression dictionary based on the respective recurrence frequencies of the data entries in the filled partition.

Abstract: In an exemplary embodiment of this disclosure, a method for loading data from a backup image of a database includes selecting a subset statement defining a subset of the data in the database. Tables of the database are identified based on metadata of the database. A target database is written having the structure but not the data of the identified tables. One or more table statements are constructed, by a computer processor, defining a subset of each identified table based on the subset statement. Selected data is unloaded from a backup image into the target database using respective table statements as filters.

Abstract: In an exemplary embodiment of this disclosure, a method for loading data from a backup image of a database includes selecting a subset statement defining a subset of the data in the database. Tables of the database are identified based on metadata of the database. A target database is written having the structure but not the data of the identified tables. One or more table statements are constructed, by a computer processor, defining a subset of each identified table based on the subset statement. Selected data is unloaded from a backup image into the target database using respective table statements as filters.

Abstract: In an exemplary embodiment of this disclosure, a method for loading data from a backup image of a database includes selecting a subset statement defining a subset of the data in the database. Tables of the database are identified based on metadata of the database. A target database is written having the structure but not the data of the identified tables. One or more table statements are constructed, by a computer processor, defining a subset of each identified table based on the subset statement. Selected data is unloaded from a backup image into the target database using respective table statements as filters.