Abstract: Techniques are presented for applying fine-grained client-specific rules to divide (e.g., chunk) data statements to achieve cost reduction and/or failure rate reduction associated with executing the data statements over a subject dataset. Data statements for the subject dataset are received from a client. Statement attributes derived from the data statements are processed with respect to fine-grained rules and/or other client-specific data to determine whether a data statement chunking scheme is to be applied to the data statements. If a data statement chunking scheme is to be applied, further analysis is performed to select a data statement chunking scheme. A set of data operations are generated based at least in part on the selected data statement chunking scheme. The data operations are issued for execution over the subject dataset. The results from the data operations are consolidated in accordance with the selected data statement chunking scheme and returned to the client.

Abstract: Techniques are presented for applying fine-grained client-specific rules to divide (e.g., chunk) data statements to achieve cost reduction and/or failure rate reduction associated with executing the data statements over a subject dataset. Data statements for the subject dataset are received from a client. Statement attributes derived from the data statements are processed with respect to fine-grained rules and/or other client-specific data to determine whether a data statement chunking scheme is to be applied to the data statements. If a data statement chunking scheme is to be applied, further analysis is performed to select a data statement chunking scheme. A set of data operations are generated based at least in part on the selected data statement chunking scheme. The data operations are issued for execution over the subject dataset. The results from the data operations are consolidated in accordance with the selected data statement chunking scheme and returned to the client.

Abstract: Techniques are presented for rule-based selection of alternate data sources for multidimensional data statements. A virtual multidimensional data model is implemented to represent datasets that are accessed at various data sources. Derivative cubes generated from the virtual multidimensional data model are structured to have a respective data source metadata layer that is populated at data statement execution time to identify a target data source. Data source selection rules are established to map the attributes of data statements to target data sources. The data source selection rules are evaluated subject to data statement attributes derived from detected data statements to dynamically select target data sources for the data statements. The derivative cubes codify, in their respective data source metadata layers, a unique set of data source attributes identifying the target data sources. The derivative cubes are accessed to facilitate execution of the data statements on datasets at the target data sources.

Abstract: Techniques are presented for applying fine-grained client-specific rules to divide (e.g., chunk) data statements to achieve cost reduction and/or failure rate reduction associated with executing the data statements over a subject dataset. Data statements for the subject dataset are received from a client. Statement attributes derived from the data statements are processed with respect to fine-grained rules and/or other client-specific data to determine whether a data statement chunking scheme is to be applied to the data statements. If a data statement chunking scheme is to be applied, further analysis is performed to select a data statement chunking scheme. A set of data operations are generated based at least in part on the selected data statement chunking scheme. The data operations are issued for execution over the subject dataset. The results from the data operations are consolidated in accordance with the selected data statement chunking scheme and returned to the client.

Abstract: Techniques are presented for rule-based selection of alternate data sources for multidimensional data statements. A virtual multidimensional data model is implemented to represent datasets that are accessed at various data sources. Derivative cubes generated from the virtual multidimensional data model are structured to have a respective data source metadata layer that is populated at data statement execution time to identify a target data source. Data source selection rules are established to map the attributes of data statements to target data sources. The data source selection rules are evaluated subject to data statement attributes derived from detected data statements to dynamically select target data sources for the data statements. The derivative cubes codify, in their respective data source metadata layers, a unique set of data source attributes identifying the target data sources. The derivative cubes are accessed to facilitate execution of the data statements on datasets at the target data sources.