Abstract: Herein is acceleration for JavaScript object notation (JSON) documents in a relational database based on multilevel, multifield, multivalued indexing and querying. In an embodiment, into a table in a relational database, a computer stores many hierarchical data objects that respectively contain multiple levels that respectively contain one or more fields. An index is generated for indexed values in fields in at least two of the multiple levels. Based on the index, a database statement that references the table in the relational database and at least one of the indexed fields is executed. Indices herein provide matching to more fields in more levels of hierarchical data objects thereby decreasing or eliminating further filtering by brute force after index access. Matching ordinal positions within an array field needs no further filtration after index access. A single index provides increased spatial locality of index entries for acceleration. Index maintenance is minimized for acceleration.

Abstract: In an artificial neural network, integrality refers to the degree to which a neuron generates, for a given set of inputs, outputs that are near the border of the output range of a neuron. From each neural network of a pool of trained neural networks, a group of neurons with a higher integrality is selected to form a neural network tunnel (“tunnel”). The tunnel must include all input neurons and output neurons from the neural network, and some of the hidden neurons. Tunnels generated from each neural network in a pool are merged to form another neural network. The new network may then be trained.

Abstract: Herein is acceleration for JavaScript object notation (JSON) documents in a relational database based on multilevel, multifield, multivalued indexing and querying. In an embodiment, into a table in a relational database, a computer stores many hierarchical data objects that respectively contain multiple levels that respectively contain one or more fields. An index is generated for indexed values in fields in at least two of the multiple levels. Based on the index, a database statement that references the table in the relational database and at least one of the indexed fields is executed. Indices herein provide matching to more fields in more levels of hierarchical data objects thereby decreasing or eliminating further filtering by brute force after index access. Matching ordinal positions within an array field needs no further filtration after index access. A single index provides increased spatial locality of index entries for acceleration. Index maintenance is minimized for acceleration.

Abstract: In an artificial neural network, integrality refers to the degree to which a neuron generates, for a given set of inputs, outputs that are near the border of the output range of a neuron. From each neural network of a pool of trained neural networks, a group of neurons with a higher integrality is selected to form a neural network tunnel (“tunnel”). The tunnel must include all input neurons and output neurons from the neural network, and some of the hidden neurons. Tunnels generated from each neural network in a pool are merged to form another neural network. The new network may then be trained.

Abstract: A system, and computer program product for large-scale data transformations. Embodiments include a smoothing engine within an R environment to configure at least one master task and at least two worker tasks. A chunk calculator receives a series of data values and divides the series of data values into portions of data values which are in turn assigned as workloads to at least two worker tasks. The worker tasks serve to calculate a first state value of a first one of the portions of data values, and calculate a second state value of a second one of the portions of data values. The workloads are selected such that calculating a second state value does not depend on the first state value. The results of the workload calculations are used to calculate a smoothing factor used to predict a trend.

Abstract: A method, system, and computer program product for interfacing an R language client with a separate database engine environment. The method commences by interpreting an R language code fragment to identify and select R language constructs and transforming the R language constructs into queries or other database language constructs to execute within the database engine environment. The method further implements techniques for transmitting marshalled results (resulting from the execution of the database language constructs) back to the R client environment. In some situations, the marshalled results include an XML schema or DTD or another metadata description of the structure of the results.

Abstract: A system, and computer program product for large-scale data transformations. Embodiments include a smoothing engine within an R environment to configure at least one master task and at least two worker tasks. A chunk calculator receives a series of data values and divides the series of data values into portions of data values which are in turn assigned as workloads to at least two worker tasks. The worker tasks serve to calculate a first state value of a first one of the portions of data values, and calculate a second state value of a second one of the portions of data values. The workloads are selected such that calculating a second state value does not depend on the first state value. The results of the workload calculations are used to calculate a smoothing factor used to predict a trend.

Abstract: A method, system, and computer program product for interfacing an R language client with a separate database engine environment. The method commences by interpreting an R language code fragment to identify and select R language constructs and transforming the R language constructs into queries or other database language constructs to execute within the database engine environment. The method further implements techniques for transmitting marshalled results (resulting from the execution of the database language constructs) back to the R client environment. In some situations, the marshalled results include an XML schema or DTD or another metadata description of the structure of the results.

Abstract: A method, system, and computer program product for interfacing an R language client with a separate database engine environment. The method commences by interpreting an R language code fragment to identify and select R language constructs and transforming the R language constructs into queries or other database language constructs to execute within the database engine environment. The method further implements techniques for transmitting marshalled results (resulting from the execution of the database language constructs) back to the R client environment. In some situations, the marshalled results include an XML schema or DTD or another metadata description of the structure of the results.

Abstract: A method, system, and computer program product for interfacing an R language client with a separate database engine environment. The method commences by interpreting an R language code fragment to identify and select R language constructs and transforming the R language constructs into queries or other database language constructs to execute within the database engine environment. The method further implements techniques for transmitting marshaled results (resulting from the execution of the database language constructs) back to the R client environment. In some situations, the marshaled results include an XML schema or DTD or another metadata description of the structure of the results.