Abstract: The present invention relates to a method, computer program product and system for de-identifying data, wherein a de-identification protocol is selectively mapped to a business rule at runtime via an ETL tool.

Abstract: The present invention relates to a method, computer program product and system for de-identifying data, wherein a de-identification protocol is selectively mapped to a business rule at runtime via an ETL tool.

Abstract: Insertion, in a data warehouse process, of a buffer operator between an upstream data operator and a downstream data operator. The processor processes a data record for the upstream data operator to pass to the downstream data operator. In response to determining that the downstream data operator is not ready to accept the data record from the upstream data operator, a buffer operator is inserted between the upstream data operator and the downstream data operator. The data record processed by the upstream data operator is passed to the buffer operator for storing into a buffer allocated for use by the buffer operator. The data record stored into the buffer is passed to the downstream data operator when the downstream data operator is ready to accept data. In another aspect, when the allocated buffer utilization decreases to a threshold value, the buffer operator is removed and the buffers are released.

Abstract: Provisioning and/or configuring machines (real and/or virtual) for multiple parallel execution of multiple test cases. The test cases correspond to different software configurations (such as different platform version and product version configurations) and are executed at substantially overlapping time(s), which is to say in “parallel.

Abstract: Handling extract-transform-load (ETL) job mismatches as “exceptions.” Exception handling may include the following steps: (i) determining a mismatch while running an extract-transform-load job with the mismatch being a mismatch of at least one of the following types: design time information mismatch, and/or operational metadata mismatch; and (ii) responsive to determining the mismatch, handling the mismatch as an exception.

Abstract: The present invention relates to a method, computer program product and system for masking sensitive data and, more particularly, to dynamically de-identifying sensitive data from a data source for a target application, including enabling a user to selectively alter an initial de-identification protocol for the sensitive data elements via an interface.

Abstract: The present invention relates to a method, computer program product and system for masking sensitive data and, more particularly, to dynamically de-identifying sensitive data from a data source for a target application, including enabling a user to selectively alter an initial de-identification protocol for the sensitive data elements via an interface.

Abstract: A transformation method and system is provided. The method includes generating a data hub application configured to embed extract, transform, and load (ETL) processes. The data hub application is linked to source tables and target tables. Meta data associated with the source and target tables is transferred from virtual views of the data hub application to an ETL work area of the ETL processes. An ETL job is generated and linked to the data hub application. ETL processes are executed and results are determined.

Abstract: A transformation method and system is provided. The method includes generating a data hub application configured to embed extract, transform, and load (ETL) processes. The data hub application is linked to source tables and target tables. Meta data associated with the source and target tables is transferred from virtual views of the data hub application to an ETL work area of the ETL processes. An ETL job is generated and linked to the data hub application. ETL processes are executed and results are determined.

Abstract: A transformation method and system is provided. The method includes generating a data hub application configured to embed extract, transform, and load (ETL) processes. The data hub application is linked to source tables and target tables. Meta data associated with the source and target tables is transferred from virtual views of the data hub application to an ETL work area of the ETL processes. An ETL job is generated and linked to the data hub application. ETL processes are executed and results are determined.

Abstract: A transformation method and system is provided. The method includes generating a data hub application configured to embed extract, transform, and load (ETL) processes. The data hub application is linked to source tables and target tables. Meta data associated with the source and target tables is transferred from virtual views of the data hub application to an ETL work area of the ETL processes. An ETL job is generated and linked to the data hub application. ETL processes are executed and results are determined.

Abstract: Methods for resolving a number of in-memory issues associated with parallel query execution of a database operation on a database utilizing a graphics processing unit (GPU) are presented including: tying a table choice to a number of accesses per second made to a table; and synchronizing threads in a same shared GPU multiprocessor to avoid compromising concurrency, and where the parallel query execution of the database operation is performed solely by the GPU. In some embodiments, methods further include storing data from the GPU to a disk to solve volatility; and enabling a user, at any time, to query the amount of memory being used by the table created by the user to monitor memory consumption.

Abstract: Methods for parallel query execution of a database operation on a database utilizing a graphics processing unit (GPU) are presented including: receiving query by a host, the query including database relations; starting a GPU kernel, where the GPU kernels include a GPU memory; hash partitioning the database relations by the GPU kernel; loading the partitioned database relations into the GPU memory; loading keyed partitions corresponding the hash partitioned database relations into the GPU memory; building a hash table for a smaller of the hash partitioned database relations; and executing the query. In some embodiments, methods further include returning a result of the query. In some embodiments, methods further include when the query is a long query including a number of operators, parsing the long query into a number of sub-queries; for each of the sub-queries, starting one of the GPU kernels such that the sub-queries are processed in parallel.

Abstract: The present invention relates to a method, computer program product and system for de-identifying data, wherein a de-identification protocol is selectively mapped to a business rule at runtime via an ETL tool.

Abstract: The present invention relates to a method, computer program product and system for masking sensitive data and, more particularly, to dynamically de-identifying sensitive data from a data source for a target application, including enabling a user to selectively alter an initial de-identification protocol for the sensitive data elements via an interface.

Abstract: The present invention relates to a method, computer program product and system for de-identifying data, wherein a de-identification protocol is selectively mapped to a business rule at runtime via an ETL tool.

Abstract: The present invention relates to a method, computer program product and system for masking sensitive data and, more particularly, to dynamically de-identifying sensitive data from a data source for a target application, including enabling a user to selectively alter an initial de-identification protocol for the sensitive data elements via an interface.

Abstract: Methods for parallel query execution of a database operation on a database utilizing a graphics processing unit (GPU) are presented including: receiving query by a host, the query including database relations; starting a GPU kernel, where the GPU kernels include a GPU memory; hash partitioning the database relations by the GPU kernel; loading the partitioned database relations into the GPU memory; loading keyed partitions corresponding the hash partitioned database relations into the GPU memory; building a hash table for a smaller of the hash partitioned database relations; and executing the query. In some embodiments, methods further include returning a result of the query. In some embodiments, methods further include when the query is a long query including a number of operators, parsing the long query into a number of sub-queries; for each of the sub-queries, starting one of the GPU kernels such that the sub-queries are processed in parallel.