Activation based feature identification

A system for managing data, comprising:

• a network computer, comprising:
• a transceiver that communicates over the network;
• a memory that stores at least instructions;
• and
• one or more processor devices that execute instructions that perform actions, including:
• instantiating a data engine that performs actions, including:
• providing a data model that includes a plurality of concepts and a plurality of relations between the concepts, wherein each concept is a node in the data model and each relation is an edge in the data model;
• and
• associating a propagation weight with each relation based on one or more characteristics of the plurality of concepts, wherein the propagation weight is based on one or more heuristics that are determined prior to training of a machine learning model;
• and
• instantiating a feature engine that performs actions, including:
• associating an initial impetus value with a pivot concept, wherein a query is employed to select one of the plurality of concepts as the pivot concept;
• employing the pivot concept as a start point to recursively traverse the data model;
• allocating a portion of the impetus value to one or more concepts that are on a direct path of the traversal based on the propagation weight associated with each relation of the one or more concepts;
• and
• identifying one or more of the plurality concepts as a feature concept based on a value of a portion of the impetus value that exceeds a threshold, wherein one or more of the feature concepts or the data model are visually presented in a display to a user, and wherein internal processes, databases, and elements of the visual presentation are modified based on geo-location information of the user provided by a global positioning system (GPS) device, and wherein the modified elements include one or more of a time zone, language, currency, or calendar format;
• and
• instantiating a machine learning engine to employ the one or more feature concepts to train the machine learning model, wherein the use of the one or more feature concepts reduces one or more computing resources required to train the machine learning model;
• and
• a client computer, comprising:
• a client computer transceiver that communicates over the network;
• a client computer memory that stores at least instructions;
• and
• one or more processor devices that execute instructions that perform actions, including:
• displaying one or more of the data model or one or more featured concepts on the display of the client computer.

A processor readable non-transitory storage media that includes instructions for managing data, wherein execution of the instructions by one or more hardware processors performs actions, comprising:

• instantiating a data engine that performs actions, including:
• providing a data model that includes a plurality of concepts and a plurality of relations between the concepts, wherein each concept is a node in the data model and each relation is an edge in the data model;
• and
• associating a propagation weight with each relation based on one or more characteristics of the plurality of concepts, wherein the propagation weight is based on one or more heuristics that are determined prior to training of a machine learning model;
• and
• instantiating a feature engine that performs actions, including:
• associating an initial impetus value with a pivot concept, wherein a query is employed to select one of the plurality of concepts as the pivot concept;
• employing the pivot concept as a start point to recursively traverse the data model;
• allocating a portion of the impetus value to one or more concepts that are on a direct path of the traversal based on the propagation weight associated with each relation of the one or more concepts;
• and
• identifying one or more of the plurality concepts as a feature concept based on a value of a portion of the impetus value that exceeds a threshold, wherein one or more of the feature concepts or the data model are visually presented in a display to a user, and wherein internal processes, databases, and elements of the visual presentation are modified based on geo-location information of the user provided by a global positioning system (GPS) device, and wherein the modified elements include one or more of a time zone, language, currency, or calendar format;
• and
• instantiating a machine learning engine to employ the one or more feature concepts to train the machine learning model, wherein the use of the one or more feature concepts reduces one or more computing resources required to train the machine learning model.

A network computer for managing data, comprising:

• a transceiver that communicates over the network;
• a memory that stores at least instructions;
• and
• one or more processor devices that execute instructions that perform actions, including:
• instantiating a data engine that performs actions, including:
• providing a data model that includes a plurality of concepts and a plurality of relations between the concepts, wherein each concept is a node in the data model and each relation is an edge in the data model;
• and
• associating a propagation weight with each relation based on one or more characteristics of the plurality of concepts, wherein the propagation weight is based on one or more heuristics that are determined prior to training of a machine learning model;
• and
• instantiating a feature engine that performs actions, including:
• associating an initial impetus value with a pivot concept, wherein a query is employed to select one of the plurality of concepts as the pivot concept;
• employing the pivot concept as a start point to recursively traverse the data model;
• allocating a portion of the impetus value to one or more concepts that are on a direct path of the traversal based on the propagation weight associated with each relation of the one or more concepts;
• and
• identifying one or more of the plurality concepts as a feature concept based on a value of a portion of the impetus value that exceeds a threshold, wherein one or more of the feature concepts or the data model are visually presented in a display to a user, and w wherein internal processes, databases, and elements of the visual presentation are modified based on geo-location information of the user provided by a global positioning system (GPS) device, and wherein the modified elements include one or more of a time zone, language, currency, or calendar format;
• and
• instantiating a machine learning engine to employ the one or more feature concepts to train the machine learning model, wherein the use of the one or more feature concepts reduces one or more computing resources required to train the machine learning model.