Abstract: Examples of an intelligent data quality application are defined. In an example, the system receives a data quality requirement from a user. The system obtains target data from a plurality of data sources. The system implements an artificial intelligence component sort the target data into a data cascade. The data cascade may include a plurality of attributes associated with the data quality requirement. The system may evaluate the data cascade to identify a data pattern model for each of the attributes. The system may implement a first cognitive learning operation to determine a mapping context from the data cascade and a conversion rule from the data pattern model. The system may establish a data harmonization model corresponding to the data quality requirement by performing a second cognitive learning operation. The system may generate a data cleansing result corresponding to the data quality requirement.

Abstract: Examples of an intelligent data quality application are defined. In an example, the system receives a data quality requirement from a user. The system obtains target data from a plurality of data sources. The system implements an artificial intelligence component sort the target data into a data cascade. The data cascade may include a plurality of attributes associated with the data quality requirement. The system may evaluate the data cascade to identify a data pattern model for each of the attributes. The system may implement a first cognitive learning operation to determine a mapping context from the data cascade and a conversion rule from the data pattern model. The system may establish a data harmonization model corresponding to the data quality requirement by performing a second cognitive learning operation. The system may generate a data cleansing result corresponding to the data quality requirement.

Abstract: Examples of an intelligent data quality application are defined. In an example, the system receives a data quality requirement from a user. The system obtains target data from a plurality of data sources. The system implements an artificial intelligence component sort the target data into a data cascade. The data cascade may include a plurality of attributes associated with the data quality requirement. The system may evaluate the data cascade to identify a data pattern model for each of the attributes. The system may implement a first cognitive learning operation to determine a mapping context from the data cascade and a conversion rule from the data pattern model. The system may establish a data harmonization model corresponding to the data quality requirement by performing a second cognitive learning operation. The system may generate a data cleansing result corresponding to the data quality requirement.

Abstract: Examples of an intelligent data quality application are defined. In an example, the system receives a data quality requirement from a user. The system obtains target data from a plurality of data sources. The system implements an artificial intelligence component sort the target data into a data cascade. The data cascade may include a plurality of attributes associated with the data quality requirement. The system may evaluate the data cascade to identify a data pattern model for each of the attributes. The system may implement a first cognitive learning operation to determine a mapping context from the data cascade and a conversion rule from the data pattern model. The system may establish a data harmonization model corresponding to the data quality requirement by performing a second cognitive learning operation. The system may generate a data cleansing result corresponding to the data quality requirement.

Abstract: An extract, transform and load (ETL) data construction system provides an extensible, interim platform for transitioning data from a legacy database or other data sources to a target database. The data construction system performs data construction operations which may include creating data that can be used for data transformation in ETL operations. The data construction system provides check-in-check-out procedures for controlling multi-user data access and data editing, and also provides multi-level user access checks.

Abstract: An extract, transform and load (ETL) data construction system provides an extensible, interim platform for transitioning data from a legacy database or other data sources to a target database. The data construction system performs data construction operations which may include creating data that can be used for data transformation in ETL operations. The data construction system provides check-in-check-out procedures for controlling multi-user data access and data editing, and also provides multi-level user access checks.