Abstract: Technologies described herein can be used to generate and distribute neural network models and executable code using feedback data received from one or more client computing devices. A neural network model can be generated by a server computer. Executable code can also be generated by the server that, when executed by a client computing device, causes the client device to generate a prediction using the neural network model. The server can transmit the model and code to one or more client computing devices. The server can receive feedback data from the client device(s) based on predictions generated by the client device(s) using the neural network model and the executable code. The server can generate an updated version of the neural network model and/or an updated version of the executable code base on the feedback data, and can transmit the updated model and/or the updated code to the client device(s).

Abstract: A database includes a blockchain infrastructure that allows data tables comprising the database to be associated with respective blockchains. Data inserted into a data table is replicated in its associated blockchain. Certain operations on a data table are disabled when it is associated with a blockchain.

Abstract: A database includes a blockchain infrastructure that allows data tables comprising the database to be associated with respective blockchains. Data inserted into a data table is replicated in its associated blockchain. Certain operations on a data table are disabled when it is associated with a blockchain.

Abstract: Embodiments of the present disclosure pertain to a personal assistant controller. In one embodiment, the present disclosure includes a computer implemented method comprising receiving a voice audio signal in the personal assistant controller, converting the voice audio signal into a target command corresponding to one of a plurality of personal assistants, wherein different personal assistants comprise different target command protocols for executing different operations on different network enabled appliances, and sending the target command for execution by a backend system corresponding to the one of the plurality of personal assistants, and in accordance therewith, performing an operation on the backend system.

Abstract: Technologies described herein can be used to generate and distribute neural network models and executable code using feedback data received from one or more client computing devices. A neural network model can be generated by a server computer. Executable code can also be generated by the server that, when executed by a client computing device, causes the client device to generate a prediction using the neural network model. The server can transmit the model and code to one or more client computing devices. The server can receive feedback data from the client device(s) based on predictions generated by the client device(s) using the neural network model and the executable code. The server can generate an updated version of the neural network model and/or an updated version of the executable code base on the feedback data, and can transmit the updated model and/or the updated code to the client device(s).

Abstract: Some embodiments provide a non-transitory machine-readable medium that stores a program. The program receives a transaction from a transaction source. Based on a set of rules configured for processing transactions, the program further determines a blockchain from a plurality of blockchains stored in memory of the device. The program also records the transaction to the determined blockchain in the memory of the device.

Abstract: A database includes a blockchain infrastructure that allows data tables comprising the database to be associated with respective blockchains. Data inserted into a data table is replicated in its associated blockchain. Certain operations on a data table are disabled when it is associated with a blockchain.

Abstract: Embodiments of the present disclosure pertain to a personal assistant controller. In one embodiment, the present disclosure includes a computer implemented method comprising receiving a voice audio signal in the personal assistant controller, converting the voice audio signal into a target command corresponding to one of a plurality of personal assistants, wherein different personal assistants comprise different target command protocols for executing different operations on different network enabled appliances, and sending the target command for execution by a backend system corresponding to the one of the plurality of personal assistants, and in accordance therewith, performing an operation on the backend system.

Abstract: Embodiments relate to systems and methods allowing selective bypass of code flows in computer software. According to particular embodiments, a bypass attribute is introduced into code flows (e.g., present for development and/or testing purposes only) that are to be disabled prior to releasing the final operational version of the software. A value for the bypass attribute is determined at single, a central engine. Based upon that bypass attribute value, the code flow is either executed or bypassed. In certain embodiments the bypass attribute is implemented in the form of a substitute variable. In some embodiments the bypass attribute is implemented in the form of a code flow call. The bypass attribute may operate in a sequential manner, such that execution of other downstream code flows is also bypassed. One example is provided in the form of bypassing code flows of an Extract, Transform, Load (ETL) process.

Abstract: Described herein is an extensible extract, transform and load (ETL) framework. In accordance with one aspect of the framework, a file location object and a corresponding file format object are retrieved from a repository. The file location object stores file location parameters that specify a remote file system directory and a local file system directory, while the file format object stores location configuration parameters that specify a data file and the file location object. The data file may be transferred from the remote file system directory to the local file system directory according to the file location parameters and the location configuration parameters. An extract, transform and load (ETL) job may then be performed on the data file to generate an output file.

Abstract: Described herein is an extensible extract, transform and load (ETL) framework. In accordance with one aspect of the framework, a file location object and a corresponding file format object are retrieved from a repository. The file location object stores file location parameters that specify a remote file system directory and a local file system directory, while the file format object stores location configuration parameters that specify a data file and the file location object. The data file may be transferred from the remote file system directory to the local file system directory according to the file location parameters and the location configuration parameters. An extract, transform and load (ETL) job may then be performed on the data file to generate an output file.

Abstract: Embodiments encrypt Extract, Transform, Load (ETL) scripts created by a developer for an initial customer, against unauthorized access and copying. Such protection preserves the economic value of the ETL script for the developer, allowing re-use with other customers (who could otherwise simply copy the ETL script from the initial customer). A new hidden attribute is introduced to indicate ownership and protection of an object used in an ETL utility. A customer will not see this hidden attribute via the ETL utility. The hidden attribute may be assigned programmatically during a protecting process, and its value may be a current keycode (e.g., the signature of the developer's license of the ETL utility). The protected object thus has the attribute signature, and its value does not match any current keycode known to the customer. The signature for internal decryption of the script by the ETL utility, could be supplied by the developer.

Abstract: Embodiments encrypt Extract, Transform, Load (ETL) scripts created by a developer for an initial customer, against unauthorized access and copying. Such protection preserves the economic value of the ETL script for the developer, allowing re-use with other customers (who could otherwise simply copy the ETL script from the initial customer). A new hidden attribute is introduced to indicate ownership and protection of an object used in an ETL utility. A customer will not see this hidden attribute via the ETL utility. The hidden attribute may be assigned programmatically during a protecting process, and its value may be a current keycode (e.g., the signature of the developer's license of the ETL utility). The protected object thus has the attribute signature, and its value does not match any current keycode known to the customer. The signature for internal decryption of the script by the ETL utility, could be supplied by the developer.

Abstract: Embodiments relate to systems and methods allowing selective bypass of code flows in computer software. According to particular embodiments, a bypass attribute is introduced into code flows (e.g., present for development and/or testing purposes only) that are to be disabled prior to releasing the final operational version of the software. A value for the bypass attribute is determined at single, a central engine. Based upon that bypass attribute value, the code flow is either executed or bypassed. In certain embodiments the bypass attribute is implemented in the form of a substitute variable. In some embodiments the bypass attribute is implemented in the form of a code flow call. The bypass attribute may operate in a sequential manner, such that execution of other downstream code flows is also bypassed. One example is provided in the form of bypassing code flows of an Extract, Transform, Load (ETL) process.

Abstract: A system and method of querying data. The method includes transforming first data according to a unified data model. The unified data model has a hierarchical structure with tree nodes and leaf nodes. A leaf node contains a table. The method further includes executing a unified data model query on the first data (having been transformed) to result in second data. The method further includes outputting the second data.

Abstract: A computer readable medium is configured to receive a description of a COBOL copybook that can be represented in one of a plurality of disparate formats, to parse the COBOL copybook based on the description of the COBOL copybook, and to create a standardized data record schema based on the COBOL copybook. The description of the COBOL copybook includes information about the format of the COBOL copybook.

Abstract: A system and method of querying data. The method includes transforming first data according to a unified data model. The unified data model has a hierarchical structure with tree nodes and leaf nodes. A leaf node contains a table. The method further includes executing a unified data model query on the first data (having been transformed) to result in second data. The method further includes outputting the second data.

Abstract: Embodiments relate to graphical representation and/or automatic generation of an iteration rule in mapping design that is to integrate or transform one or more input data sets into another target data set. The input and output data set can be of flat or hierarchical in nature. In an embodiment, a graphical interface allows users to specify an iteration rule (e.g. JOIN operation in a relational database) in a tree-like structure (e.g. a JOIN tree). The interface allows users to visualize and implement complicated and powerful combinations of multiple data sets, including data sets exhibiting hierarchical structure. Drag-and-drop techniques may be employed to reduce the need for manual typing. Also disclosed are procedures automatically generating an iteration rule based on the data mapping information, thereby reducing a need for manual mapping.

Abstract: A data transform leverages a known hierarchy within a target data structure, in order to improve query and mapping capabilities and enhance performance. Where a target data structure is hierarchical, output data of that target data structure is often built in the document order of the nodes in the structure (from top down and from left to right). Hence, when the data for a child node in the target structure is being built, the data for the parent nodes of the child node has been built. Embodiments utilize this available portion of the target data in the form of target columns, to increase processing efficiency of the transformation process. Use of target columns according to embodiments may also allow powerful and concise expression of mapping logic in the transform, facilitating the use of functions such as selection (e.g. Where clauses), uniqueness (e.g. DISTINCT), ordering (Order By, Group By), and Aggregation.

Abstract: Disclosed is a user interface on a display for editing data transformations comprising an ETL process. A first display area presents a data representation of a data transformation. A second display area presents a view of input data, and a third display are presents a view of output data. User input to modify the data transformation is received. In response to receiving the user input, the third display area is updated with output data generated by applying the modified data transformation to the input data.