Abstract: Systems and techniques for aggregated physical and logical network mesh view are described herein. A selection may be obtained of a portion of a graph network data structure. Nodes may be identified in the portion of the graph network data structure. Geolocation data may be determined for each node of the nodes. Geographical boundaries may be calculated for the portion of the graph network data structure. A geographical map and a network map may be generated. A physical network view may be generated for display in the user interface as an aggregated view of the geographical map and the network map. The geographical map may represent a physical layer and the network map may represent a logical layer. The aggregated view may be output for display with a set of controls that correspond to the physical layer and a set of controls that correspond to the logical layer.

Abstract: Systems and techniques for hierarchical tournament-based machine learning predictions are described herein. A machine learning selection model may be trained with training data. A configuration may be received that includes the metric and a target prediction. A set of evaluation component combinations may be selected using the machine learning selection model. Each evaluation component combination of the set of evaluation component combinations may include an algorithm, a hierarchical learning model corresponding to a level of a hierarchy, and a prediction model for the target prediction. The set of evaluation component combinations may be transmitted to a cluster of computing nodes. Output results may be received for the set of evaluation component combinations. The output results may be evaluated using the metric to determine a winning evaluation component combination. The winning evaluation component combination may be stored in storage for use in calculating future predictions for the target prediction.

Abstract: A plan model system provides interactive graphical user interfaces allowing users to view and navigate to multiple alternative views of measures modeled in one or more planning models. A modeled event is provided by a user relating to a measure of a particular plan model modeling outcomes of a particular business domain of a business organization. An effect of the event on values of one or more measures of the plan model is determined. A graphical representation is presented in the graphical user interface illustrating the effect.

Abstract: Systems and techniques for aggregated physical and logical network mesh view are described herein. A selection may be obtained of a portion of a graph network data structure. Nodes may be identified in the portion of the graph network data structure. Geolocation data may be determined for each node of the nodes. Geographical boundaries may be calculated for the portion of the graph network data structure. A geographical map and a network map may be generated. A physical network view may be generated for display in the user interface as an aggregated view of the geographical map and the network map. The geographical map may represent a physical layer and the network map may represent a logical layer. The aggregated view may be output for display with a set of controls that correspond to the physical layer and a set of controls that correspond to the logical layer.

Abstract: Systems and techniques for read-write network visualization are described herein. A selected sub-region of a visual representation of a graph network may be displayed based on inputs received from a user. Boundaries for the selected sub-region of the visual representation of the graph network may be calculated. State data may be captured for a portion of the graph network associated with the selected sub-region using the calculated boundaries for the selected sub-region of the visual representation upon activation of an edit mode user interface control. A change to an element of the graph network may be identified. The graph network may be modified based on the change. A formula may be identified that is associated with the element of the graph network. The formula may be automatically recompiled and values for the element of the graph network, and nodes and edges that share a path with the element, may be automatically recalculated using the recompiled formula.

Abstract: Systems and techniques for hierarchical tournament-based machine learning predictions are described herein. A machine learning selection model may be trained with training data. A configuration may be received that includes the metric and a target prediction. A set of evaluation component combinations may be selected using the machine learning selection model. Each evaluation component combination of the set of evaluation component combinations may include an algorithm, a hierarchical learning model corresponding to a level of a hierarchy, and a prediction model for the target prediction. The set of evaluation component combinations may be transmitted to a cluster of computing nodes. Output results may be received for the set of evaluation component combinations. The output results may be evaluated using the metric to determine a winning evaluation component combination. The winning evaluation component combination may be stored in storage for use in calculating future predictions for the target prediction.

Abstract: An interactive user interface is generated to illustrate a graphical view of a network of plan models. The network of plan models includes a plurality of plan models, each plan model including one or more measures and one or more dimensions. At least one user-entered text character is entered in a query field and the plan models are searched for names of measures and/or dimensions in the plan models that correspond to the user entry. The names are presented as search term suggestions in the search query field and at least one suggestion is selected and adopted as at least a portion of a search query. The network of plan models is searched to generate search results corresponding to the search query. The graphical view of the network of plan models can be revised based on the search results.

Abstract: Systems and techniques for aggregated physical and logical network mesh view are described herein. A selection may be obtained of a portion of a graph network data structure. Nodes may be identified in the portion of the graph network data structure. Geolocation data may be determined for each node of the nodes. Geographical boundaries may be calculated for the portion of the graph network data structure. A geographical map and a network map may be generated. A physical network view may be generated for display in the user interface as an aggregated view of the geographical map and the network map. The geographical map may represent a physical layer and the network map may represent a logical layer. The aggregated view may be output for display with a set of controls that correspond to the physical layer and a set of controls that correspond to the logical layer.

Abstract: Systems and techniques for hierarchical tournament-based machine learning predictions are described herein. A machine learning selection model may be trained with training data. A configuration may be received that includes the metric and a target prediction. A set of evaluation component combinations may be selected using the machine learning selection model. Each evaluation component combination of the set of evaluation component combinations may include an algorithm, a hierarchical learning model corresponding to a level of a hierarchy, and a prediction model for the target prediction. The set of evaluation component combinations may be transmitted to a cluster of computing nodes. Output results may be received for the set of evaluation component combinations. The output results may be evaluated using the metric to determine a winning evaluation component combination. The winning evaluation component combination may be stored in storage for use in calculating future predictions for the target prediction.

Abstract: An unstructured data input is accessed that includes an electronic communication. Content of the unstructured data is parsed to determine one or more terms in the unstructured data input. It is determined that one or more particular elements defined in a structured business data model correspond to the terms. Tags are assigned to the unstructured data based on the terms corresponding to the one or more particular elements, where the tags define an association between the unstructured data and the structured data model.

Abstract: Systems and techniques for alert-based learning for multi-dimensional data processing are described herein. Alert data may be received from a big data platform. An alert definition may be generated that includes a set of key performance indicators (KPIs) for an alert based on a determination that an alert definition does not exist for the alert. A calculation configuration may be created for the alert. Historical data from the big data platform may be evaluated with the calculation configuration to calculate the KPIs. An alert condition may be established for the alert based on the KPIs. The alert condition may be stored in the big data platform.

Abstract: A communication by a particular user within a digital collaboration is determined as related to a particular business entity. A relationship between one or more additional business entities and the particular business entity is identified from one or more business models. The communication is associated with the additional business entities based on the relationship.

Abstract: An unstructured data input is accessed that includes an electronic communication. Content of the unstructured data is parsed to determine one or more terms in the unstructured data input. It is determined that one or more particular elements defined in a structured business data model correspond to the terms. Tags are assigned to the unstructured data based on the terms corresponding to the one or more particular elements, where the tags define an association between the unstructured data and the structured data model.

Abstract: An interactive user interface is generated to illustrate a graphical view of a network of plan models. The network of plan models includes a plurality of plan models, each plan model including one or more measures and one or more dimensions. At least one user-entered text character is entered in a query field and the plan models are searched for names of measures and/or dimensions in the plan models that correspond to the user entry. The names are presented as search term suggestions in the search query field and at least one suggestion is selected and adopted as at least a portion of a search query. The network of plan models is searched to generate search results corresponding to the search query. The graphical view of the network of plan models can be revised based on the search results.

Abstract: A plan model system provides interactive graphical user interfaces allowing users to view and navigate to multiple alternative views of measures modeled in one or more planning models. A modeled event is provided by a user relating to a measure of a particular plan model modeling outcomes of a particular business domain of a business organization. An effect of the event on values of one or more measures of the plan model is determined. A graphical representation is presented in the graphical user interface illustrating the effect.

Abstract: A plan model system provides interactive graphical user interfaces allowing users to view and navigate to multiple alternative views of measures modeled in one or more planning models. A modeled event is provided by a user relating to a measure of a particular plan model modeling outcomes of a particular business domain of a business organization. An effect of the event on values of one or more measures of the plan model is determined. A graphical representation is presented in the graphical user interface illustrating the effect.

Abstract: An interactive user interface is generated to illustrate a graphical view of a network of plan models. The network of plan models includes a plurality of plan models, each plan model including one or more measures and one or more dimensions. At least one user-entered text character is entered in a query field and the plan models are searched for names of measures and/or dimensions in the plan models that correspond to the user entry. The names are presented as search term suggestions in the search query field and at least one suggestion is selected and adopted as at least a portion of a search query. The network of plan models is searched to generate search results corresponding to the search query. The graphical view of the network of plan models can be revised based on the search results.

Abstract: A communication by a particular user within a digital collaboration is determined as related to a particular business entity. A relationship between one or more additional business entities and the particular business entity is identified from one or more business models. The communication is associated with the additional business entities based on the relationship.

Abstract: Systems and techniques for constrained concurrent resource allocator are described herein. A first demand request may be received including a first demand value for a first resource in a supply chain node hierarchy from a first demand node of the supply chain node hierarchy. The supply chain node hierarchy may be traversed to identify a capacity node of the supply chain node hierarchy between the first demand node and a supply node of the supply chain node hierarchy. A first resource temporary capacity reservation may be created at the capacity node. An available supply of the first resource may be identified in the supply chain node hierarchy. The first resource temporary capacity reservation may be replaced with a first resource supply capacity reservation equal to a supply of the first resource processed by the capacity node using the available supply.

Abstract: A communication by a particular user within a digital collaboration is determined as related to a particular business entity. A relationship between one or more additional business entities and the particular business entity is identified from one or more business models. The communication is associated with the additional business entities based on the relationship.