Abstract: An exemplary computer implemented method for processing data in a database management system implemented on a network of two or more nodes is disclosed. The method includes receiving a query referencing first and second user-defined table functions and a table distributed across the two or more nodes. The first user-defined table function is executed on each of the two or more nodes by processing records of the table residing on the respective nodes. A data sample is generated per node as a result of executing the first user-defined table function on each of the two or more nodes. The generated data samples are stored in a selected set of nodes. The second user-defined table function is executed on each of the selected set of nodes by processing records of the data samples residing on the respective nodes of the selected set of nodes to compute a user-defined aggregate.

Abstract: Embodiments relate to distributed processing of data on a distributed database computer system. An aspect includes distributing rows of an arbitrary matrix to all of a plurality of processing units, wherein a number of the rows is equal to a number of the processing units, wherein one row of the arbitrary matrix is stored in each storage memory. Another aspect includes executing a first user defined function (UDF) on each processing unit, wherein a Cartesian product of each processing parameter matrix and the row of the arbitrary matrix is calculated on each processing unit and the matrix set is stored in the processor memory of each processing unit; and executing a second UDF on each processing unit having at least one data record after the executing of the first UDF, wherein all data records stored in the storage memory of the each processing unit are processed one by one.

Abstract: An exemplary computer implemented method for processing data in a database management system implemented on a network of two or more nodes is disclosed. The method includes receiving a query referencing first and second user-defined table functions and a table distributed across the two or more nodes. The first user-defined table function is executed on each of the two or more nodes by processing records of the table residing on the respective nodes. A data sample is generated per node as a result of executing the first user-defined table function on each of the two or more nodes. The generated data samples are stored in a selected set of nodes. The second user-defined table function is executed on each of the selected set of nodes by processing records of the data samples residing on the respective nodes of the selected set of nodes to compute a user-defined aggregate.

Abstract: An exemplary computer implemented method for processing data in a database management system implemented on a network of two or more nodes is disclosed. The method includes receiving a query referencing first and second user-defined table functions and a table distributed across the two or more nodes. The first user-defined table function is executed on each of the two or more nodes by processing records of the table residing on the respective nodes. A data sample is generated per node as a result of executing the first user-defined table function on each of the two or more nodes. The generated data samples are stored in a selected set of nodes. The second user-defined table function is executed on each of the selected set of nodes by processing records of the data samples residing on the respective nodes of the selected set of nodes to compute a user-defined aggregate.

Abstract: Embodiments relate to performing an ensemble model based prediction for a set of input data records. An aspect includes creating an assignment table assigning all data records in the input data set to a root node of a tree. Another aspect includes splitting the assignment tables into disjoint sub-tables. Another aspect includes storing each of the sub-tables to a respective data slice. Another aspect includes designating root nodes of each of the plurality of trees as a set of current nodes. Another aspect includes evaluating split conditions of each identified current node based on data records that are assigned to the current node; re-assigning said input data records to one or more child-nodes; using the child nodes of all current nodes as the set of current nodes; and calculating a prediction result from the assignment of data records to the leaf nodes of each of a plurality of trees.

Abstract: Embodiments relate to growing a plurality of trees in parallel. An aspect includes creating, for each of a plurality of trees, a data bag based on a training data set comprising a plurality of data records. Another aspect includes splitting the training data set into disjoint data sub-sets; and storing each of the sub-sets in a respective data slice. Another aspect includes performing a single pass through the data records stored in a data slice, thereby identifying one or more of the current nodes that are assigned data records; calculating an intermediate result for each identified current node based on all data records of said data slice; and merging intermediate results into a combined intermediate result. Another aspect includes, for each of the current nodes: calculating a split criterion from the combined intermediate result; and creating two or more child nodes of the current node based on the split criterion.

Abstract: Embodiments relate to distributed processing of data on a distributed database computer system. An aspect includes distributing rows of an arbitrary matrix to all of a plurality of processing units, wherein a number of the rows is equal to a number of the processing units, wherein one row of the arbitrary matrix is stored in each storage memory. Another aspect includes executing a first user defined function (UDF) on each processing unit, wherein a Cartesian product of each processing parameter matrix and the row of the arbitrary matrix is calculated on each processing unit and the matrix set is stored in the processor memory of each processing unit; and executing a second UDF on each processing unit having at least one data record after the executing of the first UDF, wherein all data records stored in the storage memory of the each processing unit are processed one by one.

Abstract: Embodiments relate to growing a plurality of trees in parallel. An aspect includes creating, for each of a plurality of trees, a data bag based on a training data set comprising a plurality of data records. Another aspect includes splitting the training data set into disjoint data sub-sets; and storing each of the sub-sets in a respective data slice. Another aspect includes performing a single pass through the data records stored in a data slice, thereby identifying one or more of the current nodes that are assigned data records; calculating an intermediate result for each identified current node based on all data records of said data slice; and merging intermediate results into a combined intermediate result. Another aspect includes, for each of the current nodes: calculating a split criterion from the combined intermediate result; and creating two or more child nodes of the current node based on the split criterion.

Abstract: An exemplary computer implemented method for processing data in a database management system implemented on a network of two or more nodes is disclosed. The method includes receiving a query referencing first and second user-defined table functions and a table distributed across the two or more nodes. The first user-defined table function is executed on each of the two or more nodes by processing records of the table residing on the respective nodes. A data sample is generated per node as a result of executing the first user-defined table function on each of the two or more nodes. The generated data samples are stored in a selected set of nodes. The second user-defined table function is executed on each of the selected set of nodes by processing records of the data samples residing on the respective nodes of the selected set of nodes to compute a user-defined aggregate.

Abstract: Embodiments relate to performing an ensemble model based prediction for a set of input data records. An aspect includes creating an assignment table assigning all data records in the input data set to a root node of a tree. Another aspect includes splitting the assignment tables into disjoint sub-tables. Another aspect includes storing each of the sub-tables to a respective data slice. Another aspect includes designating root nodes of each of the plurality of trees as a set of current nodes. Another aspect includes evaluating split conditions of each identified current node based on data records that are assigned to the current node; re-assigning said input data records to one or more child-nodes; using the child nodes of all current nodes as the set of current nodes; and calculating a prediction result from the assignment of data records to the leaf nodes of each of a plurality of trees.