Abstract: A computer-implemented method, computer program product and system for identifying pseudonymized data within data sources. One or more data repositories within one or more of the data sources are selected. One or more privacy data models are provided, where each of the privacy data models includes pattern(s) and/or parameter(s). One or more of the one or more privacy data models are selected. Data identification information is generated, where the data identification information indicates a presence or absence of pseudonymized data and of non-pseudonymized data within the one or more of the data sources. The data identification information is generated utilizing the pattern(s) and/or the parameter(s) to determine pseudonymized data.

Abstract: Embodiments of the present invention provide a method, system and computer program product for test data generation using unique common factor sequencing. In an embodiment of the invention, a method for test data generation using unique common factor sequencing is provided. The method includes loading a table for population with test data in a test data generation tool executing in memory of a computer. A column set of multiple columns in the table associated with a key to the table can be selected for processing and different cardinality sequence values are assigned to the columns in the set such that the cardinality sequence values do not share a common factor except for unity as in the case of prime numbers.

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: As disclosed herein, a method includes receiving a plurality of datasets from a database, wherein each dataset comprises one or more data fields represented in a single data format, and wherein the data fields from at least two of the datasets are represented in different data formats, combining the plurality of datasets to provide a created data column corresponding to all of the data fields from the plurality of datasets, organizing the data column into data clusters, wherein each data cluster includes data fields represented in a single data format, and wherein each data field belongs to a data cluster, providing a key-value map referencing data fields with respect to their corresponding data formats, and verifying the database with respect to the created column. A corresponding computer program product and computer system are also disclosed.

Abstract: As disclosed herein, a method includes receiving a plurality of datasets from a database, wherein each dataset comprises one or more data fields represented in a single data format, and wherein the data fields from at least two of the datasets are represented in different data formats, combining the plurality of datasets to provide a created data column corresponding to all of the data fields from the plurality of datasets, organizing the data column into data clusters, wherein each data cluster includes data fields represented in a single data format, and wherein each data field belongs to a data cluster, providing a key-value map referencing data fields with respect to their corresponding data formats, and verifying the database with respect to the created column. A corresponding computer program product and computer system are also disclosed.

Abstract: Data structures stored on a source database are migrated to a destination database in which the data are structured in a different format than that of the source database. Dictionaries are stored that are based on representations of the data structures stored on the source database that are formatted in other than the structural format used on the source database for the data structures. One of the data structures and a corresponding one of the dictionaries are transferred from the source database to a destination database. The transferred data structure is loaded onto the destination database in accordance with the transferred dictionary.

Abstract: A computer-implemented method, computer program product and system for identifying pseudonymized data within data sources. One or more data repositories within one or more of the data sources are selected. One or more privacy data models are provided, where each of the privacy data models includes pattern(s) and/or parameter(s). One or more of the one or more privacy data models are selected. Data identification information is generated, where the data identification information indicates a presence or absence of pseudonymized data and of non-pseudonymized data within the one or more of the data sources. The data identification information is generated utilizing the pattern(s) and/or the parameter(s) to determine pseudonymized data.

Abstract: A computer-implemented method, computer program product and system for identifying pseudonymized data within data sources. One or more data repositories within one or more of the data sources are selected. One or more privacy data models are provided, where each of the privacy data models includes pattern(s) and/or parameter(s). One or more of the one or more privacy data models are selected. Data identification information is generated, where the data identification information indicates a presence or absence of pseudonymized data and of non-pseudonymized data within the one or more of the data sources. The data identification information is generated utilizing the pattern(s) and/or the parameter(s) to determine pseudonymized data.

Abstract: A computer-implemented method, computer program product and system for identifying pseudonymized data within data sources. One or more data repositories within one or more of the data sources are selected. One or more privacy data models are provided, where each of the privacy data models includes pattern(s) and/or parameter(s). One or more of the one or more privacy data models are selected. Data identification information is generated, where the data identification information indicates a presence or absence of pseudonymized data and of non-pseudonymized data within the one or more of the data sources. The data identification information is generated utilizing the pattern(s) and/or the parameter(s) to determine pseudonymized data.

Abstract: Data structures stored on a source database are migrated to a destination database in which the data are structured in a different format than that of the source database. Dictionaries are stored that are based on representations of the data structures stored on the source database that are formatted in other than the structural format used on the source database for the data structures. One of the data structures and a corresponding one of the dictionaries are transferred from the source database to a destination database. The transferred data structure is loaded onto the destination database in accordance with the transferred dictionary.

Abstract: Embodiments of the present invention provide a method, system and computer program product for test data generation using unique common factor sequencing. In an embodiment of the invention, a method for test data generation using unique common factor sequencing is provided. The method includes loading a table for population with test data in a test data generation tool executing in memory of a computer. A column set of multiple columns in the table associated with a key to the table can be selected for processing and different cardinality sequence values are assigned to the columns in the set such that the cardinality sequence values do not share a common factor except for unity as in the case of prime numbers.

Abstract: A computer-implemented method, computer program product and system for identifying pseudonymized data within data sources. One or more data repositories within one or more of the data sources are selected. One or more privacy data models are provided, where each of the privacy data models includes pattern(s) and/or parameter(s). One or more of the one or more privacy data models are selected. Data identification information is generated, where the data identification information indicates a presence or absence of pseudonymized data and of non-pseudonymized data within the one or more of the data sources. The data identification information is generated utilizing the pattern(s) and/or the parameter(s) to determine pseudonymized data.

Abstract: Embodiments of the present invention provide a method, system and computer program product for test data generation using unique common factor sequencing. In an embodiment of the invention, a method for test data generation using unique common factor sequencing is provided. The method includes loading a table for population with test data in a test data generation tool executing in a memory of a computer. A column set of multiple columns in the table associated with a key to the table is selected for processing and different cardinality sequence values are assigned to the columns in the set such that the cardinality sequence values do not share a common factor except for unity as in the case of prime numbers.

Abstract: A computer-implemented method, computer program product and system for identifying pseudonymized data within data sources. One or more data repositories within one or more of the data sources are selected. One or more privacy data models are provided, where each of the privacy data models includes pattern(s) and/or parameter(s). One or more of the one or more privacy data models are selected. Data identification information is generated, where the data identification information indicates a presence or absence of pseudonymized data and of non-pseudonymized data within the one or more of the data sources. The data identification information is generated utilizing the pattern(s) and/or the parameter(s) to determine pseudonymized data.

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: Data structures stored on a source database are migrated to a destination database in which the data are structured in a different format than that of the source database. Dictionaries are stored that are based on representations of the data structures stored on the source database that are formatted in other than the structural format used on the source database for the data structures. One of the data structures and a corresponding one of the dictionaries are transferred from the source database to a destination database. The transferred data structure is loaded onto the destination database in accordance with the transferred dictionary.

Abstract: Data structures stored on a source database are migrated to a destination database in which the data are structured in a different format than that of the source database. Dictionaries are stored that are based on representations of the data structures stored on the source database that are formatted in other than the structural format used on the source database for the data structures. One of the data structures and a corresponding one of the dictionaries are transferred from the source database to a destination database. The transferred data structure is loaded onto the destination database in accordance with the transferred dictionary.

Abstract: As disclosed herein, a method includes receiving a plurality of datasets from a database, wherein each dataset comprises one or more data fields represented in a single data format, and wherein the data fields from at least two of the datasets are represented in different data formats, combining the plurality of datasets to provide a created data column corresponding to all of the data fields from the plurality of datasets, organizing the data column into data clusters, wherein each data cluster includes data fields represented in a single data format, and wherein each data field belongs to a data cluster, providing a key-value map referencing data fields with respect to their corresponding data formats, and verifying the database with respect to the created column. A corresponding computer program product and computer system are also disclosed.

Abstract: As disclosed herein, a method includes receiving a plurality of datasets from a database, wherein each dataset comprises one or more data fields represented in a single data format, and wherein the data fields from at least two of the datasets are represented in different data formats, combining the plurality of datasets to provide a created data column corresponding to all of the data fields from the plurality of datasets, organizing the data column into data clusters, wherein each data cluster includes data fields represented in a single data format, and wherein each data field belongs to a data cluster, providing a key-value map referencing data fields with respect to their corresponding data formats, and verifying the database with respect to the created column. A corresponding computer program product and computer system are also disclosed.