Abstract: Techniques are provided for determining the semantic-type of a target column based on “fingerprints” that are created based on the values in the target column. The fingerprint set for the target column is only generated once, not once per semantic-type. Thus, the target column only needs to be scanned once, and resource usage is minimized. Once generated, the fingerprint set of the column is compared against fingerprint set that corresponds to each semantic-type to generate a “similarity measure”. The semantic-type whose fingerprint set produces the highest similarity measure relative to the target column's fingerprint set is determined to be the semantic-type of the target column.

Abstract: Techniques are provided for determining the semantic-type of a target column based on “fingerprints” that are created based on the values in the target column. The fingerprint set for the target column is only generated once, not once per semantic-type. Thus, the target column only needs to be scanned once, and resource usage is minimized. Once generated, the fingerprint set of the column is compared against fingerprint set that corresponds to each semantic-type to generate a “similarity measure”. The semantic-type whose fingerprint set produces the highest similarity measure relative to the target column's fingerprint set is determined to be the semantic-type of the target column.

Abstract: A method and mechanism for performing an operation based on multidimensional data in a relational database. A first query is submitted to a relational database engine of the relational database management system, thereby causing a relational cube to be generated. The first query includes instructions to select a portion of multidimensional data for inclusion in the relational cube. The first query includes instructions to group the selected portion of multidimensional data using at least two or more levels of granularity of at least one dimension. The first query also includes instructions to generate a materialized view. Based on a received request for an operation, a second query is submitted to the relational database engine for execution against the relational cube, thereby causing the operation to be performed.

Abstract: A coffee bean roasting apparatus for commercial application, which provides uniform roasting of coffee beans under conditions of accurate control of product properties and without risk of damaging of the beans. This is achieved by roasting the beans in a fluidized bed of hot air directed to the beans contained in a cylindrical roasting chamber through a plurality of nozzles located in the roast chamber plenum and oriented in a tangential direction to imaginary concentric circles inside the contours of the tapered distribution plate that separates the roast chamber from the roast chamber plenum and supports the aforementioned nozzles. A predetermined pressure of hot-air blower and tangential direction of the nozzles provide movement of the entire mass of fluidized coffee beans during roasting in a circular direction as a unity substantially without relative movement of the beans with respect to each other and with excellent and uniform heat-transfer conditions between the beans.

Abstract: Performing operations on multidimensional data managed by a relational database system is facilitated by registration of solved cubes with a RDBMS. A solved cube is an abstract construct likened to a view or a table on relational data and contains not only base data, but also aggregated measure data that is base data that has been processed across multiple levels of a hierarchical dimension. In an embodiment, a solved cube may provide all the pre-computed aggregated data for a particular context that the solved cube represents. In another embodiment, a solved cube may not provide any pre-computed data, but may provide several procedures, operations and/or instructions that can be executed to compute aggregated data for a particular context that the solved cube represents. A solved cube is registered with a RDBMS by providing metadata that defines a given solved cube. Therefore, a database server is able interpret the metadata, and to perform operations on the given solved cube based on the metadata.

Abstract: A method and mechanism for performing an operation based on multidimensional data in a relational database. A query is generated that includes a first set of instructions to select a portion of multidimensional data for inclusion in a relational cube and a second set of instructions to group the portion of multidimensional data in the relational cube using at least two or more levels of granularity of at least one dimension, thereby causing a relational cube to be generated. Based on a received request for an operation to be performed, the query is modified to include a third set of instructions that represent the requested operation; and the query is submitted to the relational database engine of the relational database management system. Thereafter, the operation is performed against the relational cube.

Abstract: A system and method for analyzing data is described, in which an application programming interface (API) is provided to permit an online analytical processing (OLAP) application to manipulate data and queries in a model close to the business model the OLAP application was designed to support. A data server is provided to translate between the object-oriented representation and the native database query format. In one embodiment, a multidimensional virtual cursor is implemented to further simplify the logic of the OLAP application.

Abstract: A system and method for analyzing data is described, in which an application programming interface (API) is provided to permit an online analytical processing (OLAP) application to manipulate data and queries in model close to the business model the OLAP application was designed to support. A data server is provided to translated between the object-oriented representation and the native database query format. In one embodiment, a multidimensional virtual cursor is implementation to further simplify the logic of the OLAP application.

Abstract: A method and mechanism for performing an operation based on multidimensional data in a relational database. A first query is submitted to a relational database engine of the relational database management system, thereby causing a relational cube to be generated. The first query includes instructions to select a portion of multidimensional data for inclusion in the relational cube. The first query includes instructions to group the selected portion of multidimensional data using at least two or more levels of granularity of at least one dimension. The first query also includes instructions to generate a materialized view. Based on a received request for an operation, a second query is submitted to the relational database engine for execution against the relational cube, thereby causing the operation to be performed.

Abstract: A system and method for analyzing data is described, in which an application programming interface (API) is provided to permit an online analytical processing (OLAP) application to manipulate data and queries in model close to the business model the OLAP application was designed to support. A data server is provided to translated between the object-oriented representation and the native database query format. In one embodiment, a multidimensional virtual cursor is implementation to further simplify the logic of the OLAP application.

Abstract: A method and mechanism for performing an operation based on multidimensional data in a relational database. A query is generated that includes a first set of instructions to select a portion of multidimensional data for inclusion in a relational cube and a second set of instructions to group the portion of multidimensional data in the relational cube using at least two or more levels of granularity of at least one dimension, thereby causing a relational cube to be generated. Based on a received request for an operation to be performed, the query is modified to include a third set of instructions that represent the requested operation; and the query is submitted to the relational database engine of the relational database management system. Thereafter, the operation is performed against the relational cube.

Abstract: Performing operations on multidimensional data managed by a relational database system is facilitated by registration of solved cubes with a RDBMS. A solved cube is an abstract construct likened to a view or a table on relational data and contains not only base data, but also aggregated measure data that is base data that has been processed across multiple levels of a hierarchical dimension. In an embodiment, a solved cube may provide all the pre-computed aggregated data for a particular context that the solved cube represents. In another embodiment, a solved cube may not provide any pre-computed data, but may provide several procedures, operations and/or instructions that can be executed to compute aggregated data for a particular context that the solved cube represents. A solved cube is registered with a RDBMS by providing metadata that defines a given solved cube. Therefore, a database server is able interpret the metadata, and to perform operations on the given solved cube based on the metadata.