Abstract: A mechanism is provide for preparing a high-quality data repository. Data and related metadata from a set of data sources are ingested thereby forming a set of unprepared data. The set of unprepared data is transformed based on a set of functions into a set of transformed data. A set of semantic text descriptions that detail the transformation of the set of unprepared data to the set of transformed data is generated using a first set of semantic associations, a second set of semantic associations, and a set of semantic transformation associations. The set of transformed data is tested against one or more governance policies that tracks data lineage to ultimately show that prepared data is in compliance. Responsive to the set of transformed data adhering to the one or more governance policies, a high-quality data repository is automatically built using the transformed data.

Abstract: A mechanism is provided for preparing a high-quality data repository. Data and related metadata from a set of data sources are ingested thereby forming a set of unprepared data. The set of unprepared data is transformed based on a set of functions into a set of transformed data. A set of semantic text descriptions that detail the transformation of the set of unprepared data to the set of transformed data is generated using a first set of semantic associations, a second set of semantic associations, and a set of semantic transformation associations. The set of transformed data is tested against one or more governance policies that tracks data lineage to ultimately show that prepared data is in compliance. Responsive to the set of transformed data adhering to the one or more governance policies, a high-quality data repository is automatically built using the transformed data.

Abstract: A mechanism is provided for preparing a high-quality data repository. Data and related metadata from a set of data sources are ingested thereby forming a set of unprepared data. The set of unprepared data is transformed based on a set of functions into a set of transformed data. A set of semantic text descriptions that detail the transformation of the set of unprepared data to the set of transformed data is generated using a first set of semantic associations, a second set of semantic associations, and a set of semantic transformation associations. The set of transformed data is tested against one or more governance policies that tracks data lineage to ultimately show that prepared data is in compliance. Responsive to the set of transformed data adhering to the one or more governance policies, a high-quality data repository is automatically built using the transformed data.

Abstract: A mechanism is provide for preparing a high-quality data repository. Data and related metadata from a set of data sources are ingested thereby forming a set of unprepared data. The set of unprepared data is transformed based on a set of functions into a set of transformed data. A set of semantic text descriptions that detail the transformation of the set of unprepared data to the set of transformed data is generated using a first set of semantic associations, a second set of semantic associations, and a set of semantic transformation associations. The set of transformed data is tested against one or more governance policies that tracks data lineage to ultimately show that prepared data is in compliance. Responsive to the set of transformed data adhering to the one or more governance policies, a high-quality data repository is automatically built using the transformed data.

Abstract: As disclosed herein a method, executed by a computer, for conducting non-recursive cascading reduction includes receiving a collection of floating point values, using a binary representation of an index corresponding to a value being processed to determine a reduction depth for elements on a stack to be accumulated, and according to the reduction depth, iteratively conducting a reduction operation on the current value and one or more values on the stack. In addition to accumulation, the reduction operation may include transforming the value with a corresponding function. The method may also include using a SIMD processing environment to further increase the performance of the method. The method provides results with both high performance and accuracy. A computer system and computer program product corresponding to the method are also disclosed herein.

Abstract: As disclosed herein a method, executed by a computer, for conducting non-recursive cascading reduction includes receiving a collection of floating point values, using a binary representation of an index corresponding to a value being processed to determine a reduction depth for elements on a stack to be accumulated, and according to the reduction depth, iteratively conducting a reduction operation on the current value and one or more values on the stack. In addition to accumulation, the reduction operation may include transforming the value with a corresponding function. The method may also include using a SIMD processing environment to further increase the performance of the method. The method provides results with both high performance and accuracy. A computer system and computer program product corresponding to the method are also disclosed herein.