Abstract: Aspects of the subject disclosure may include, for example, receiving input data via a transformation UI, generating transformation configuration data, causing the transformation UI to present transformation object data per the transformation configuration data, where the transformation object data identifies data objects each including an input and output field name and a data type, detecting, from the transformation UI, an instruction defining a mapping for the input data, including a modification to the output field name of a data object such that the input field name of the data object is mapped to the modified output field name, based on the detecting the instruction, modifying the first transformation configuration data per the mapping to derive second transformation configuration data, performing a transformation of the input data based on the second transformation configuration data, and causing the transformation UI to present a transformation output. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, receiving input data via a transformation UI, generating transformation configuration data, causing the transformation UI to present transformation object data per the transformation configuration data, where the transformation object data identifies data objects each including an input and output field name and a data type, detecting, from the transformation UI, an instruction defining a mapping for the input data, including a modification to the output field name of a data object such that the input field name of the data object is mapped to the modified output field name, based on the detecting the instruction, modifying the first transformation configuration data per the mapping to derive second transformation configuration data, performing a transformation of the input data based on the second transformation configuration data, and causing the transformation UI to present a transformation output. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, receiving input data via a transformation UI, generating transformation configuration data, causing the transformation UI to present transformation object data per the transformation configuration data, where the transformation object data identifies data objects each including an input and output field name and a data type, detecting, from the transformation UI, an instruction defining a mapping for the input data, including a modification to the output field name of a data object such that the input field name of the data object is mapped to the modified output field name, based on the detecting the instruction, modifying the first transformation configuration data per the mapping to derive second transformation configuration data, performing a transformation of the input data based on the second transformation configuration data, and causing the transformation UI to present a transformation output. Other embodiments are disclosed.

Abstract: In one example, a first plurality of health scores and a second plurality of health cores are calculated. The first plurality of health scores quantifies the health of a plurality of nodes in a telecommunication service provider network, where the plurality of nodes represents connectivity points in the network. The second plurality of health scores quantifies the health of a plurality of links connecting the plurality of nodes. A priority score is calculated that quantifies an importance of a traffic demand. A route over the plurality of nodes and the plurality of links is generated, based at least in part on the first plurality of health scores, the second plurality of health scores, and the priority score. The route delivers the traffic demand from a source to a destination in a manner that meets a need of the traffic demand without exceeding the need by more than the threshold.

Abstract: In one example, a first plurality of health scores and a second plurality of health cores are calculated. The first plurality of health scores quantifies the health of a plurality of nodes in a telecommunication service provider network, where the plurality of nodes represents connectivity points in the network. The second plurality of health scores quantifies the health of a plurality of links connecting the plurality of nodes. A priority score is calculated that quantifies an importance of a traffic demand. A route over the plurality of nodes and the plurality of links is generated, based at least in part on the first plurality of health scores, the second plurality of health scores, and the priority score. The route delivers the traffic demand from a source to a destination in a manner that meets a need of the traffic demand without exceeding the need by more than the threshold.

Abstract: A graphical data type inference transparently transforms a legacy text-oriented command line interface (CLI) into a graphic-oriented or graphic-aware CLI (G-CLI). A user fuses graphical references with the legacy scripting language by identifying objects using syntax familiar to the user. The objects are presented to the user via a user interface and may be queried/selected by the user. The user issues commands on the selected objects by referencing the graphical selection in a command line within a shell. The graphical selection in short hand is translated into a form operable by a server or computer. The syntax of the legacy command language is extended with operators that operate entirely within the G-CLI. Consequently, the command line used in the shell has an existing operation set, as well as new graphic-based operations.

Abstract: A graphical data type inference transparently transforms a legacy text-oriented command line interface (CLI) into a graphic-oriented or graphic-aware CLI (G-CLI). A user fuses graphical references with the legacy scripting language by identifying objects using syntax familiar to the user. The objects are presented to the user via a user interface and may be queried/selected by the user. The user issues commands on the selected objects by referencing the graphical selection in a command line within a shell. The graphical selection in short hand is translated into a form operable by a server or computer. The syntax of the legacy command language is extended with operators that operate entirely within the G-CLI. Consequently, the command line used in the shell has an existing operation set, as well as new graphic-based operations.

Abstract: A graphical data type inference transparently transforms a legacy text-oriented command line interface (CLI) into a graphic-oriented or graphic-aware CLI (G-CLI). A user fuses graphical references with the legacy scripting language by identifying objects using syntax familiar to the user. The objects are presented to the user via a user interface and may be queried/selected by the user. The user issues commands on the selected objects by referencing the graphical selection in a command line within a shell. The graphical selection in short hand is translated into a form operable by a server or computer. The syntax of the legacy command language is extended with operators that operate entirely within the G-CLI. Consequently, the command line used in the shell has an existing operation set, as well as new graphic-based operations.