Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining i) historical location data specifying geographic locations of the user over a period of time that is included in a user profile of the user and ii) predefined geographic location data specifying a predefined geographic location of the user that is included in the user profile of the user; identifying a pair of cross-contaminated geographic regions that both include geographic locations specified by the historical location data of the user profiles for each of a threshold number of users of the plurality of users, and in response, merging the pair of cross-contaminated geographic regions to define a merged geographic region; and merging the merged geographic region with additional geographic regions until a cross-contamination between a resulting merged geographic region created by the merging and other geographic regions is reduced to a specified level of cross-contamination.

Systems, methods, and non-transitory computer readable media are disclosed that include operations to generate a virtual representation of a physical location with spatially localized information related to elements within the location being embedded in the virtual representation. The operations includes receiving description data (e.g., a plurality of images and videos) of the location, the description data being generated via at least one of a camera, a user interface; receive metadata associated elements within the location; generating (e.g., offline or in real-time), via a machine learning model and/or a geometric model, a 3-dimensional (3D) model of the location and elements therein; and generating, based on the 3D model of the location, an information-rich virtual representation of the location by annotating the 3D model with spatially localized metadata associated with the elements within the location and semantic information of the elements.

Metric forecasting techniques and systems in a digital medium environment are described that leverage similarity of elements, one to another, in order to generate a forecast value for a metric for a particular element. In one example, training data is received that describes a time series of values of the metric for a plurality of elements. The model is trained to generate the forecast value of the metric, the training using machine learning of a neural network based on the training data. The training includes generating dimensional-transformation data configured to transform the training data into a simplified representation to determine similarity of the plurality of elements, one to another, with respect to the metric over the time series. The training also includes generating model parameters of the neural network based on the simplified representation to generate the forecast value of the metric.

A method for displaying a price chart, the price chart including a plurality of regions includes displaying, by a charting engine, a first region including one or more historical completed price-range-over-time-range-type symbols, displaying, by the charting engine, a second region adjacent to the first region, the second region including a current uncompleted price range-over-an-uncompleted-time-range-type symbol corresponding to an uncompleted time range, and in response to determining that the uncompleted time range has completed, generating and displaying, in the first region, by the charting engine, a new historical completed price-range-over-time-range-type symbol adjacent to the one or more historical completed-price-range-over-time-range-type symbols, and erasing, in the second region, the current uncompleted price-range-over-an-uncompleted-time-range-type symbol.

Systems, methods, and apparatus, including computer program products, for configuring online properties, such as content pages of a website, through an online user interface. A system generates the online user interface and receives, over a network and through the online user interface presented by a client device, a request to insert a restricted third party file into a user's online property. The online user interface is updated to inform the user that inclusion of the third party file in the user's online property is restricted and to provide the user with options for satisfying requirements for including the third party file in the user's online property. In response to detecting a user selection of at least one of the options, the restricted third party file is received from a content repository, and is inserted into the user's online property.

A device receives vehicle data associated with vehicles located at a vehicle dealership lot, and receives, from a user device, profile data identifying a user of the user device and data identifying a particular vehicle of the vehicles. The device compares the data identifying the particular vehicle and the vehicle data to determine particular vehicle data associated with the particular vehicle, and processes the particular vehicle data and the profile data of the user, with a first model, to determine purchase options for the particular vehicle and the user. The device provides, to the user device, the particular vehicle data and the purchase options for the particular vehicle to cause the user device to display the particular vehicle data and the purchase options for the particular vehicle.

A processing system may collect sensor data for a first zone via sensor devices deployed in the first zone in communication with the processing system, the sensor devices including at least one of a camera or a microphone, and where the sensor data is collected over a period of time, may identify that a first disposition is associated with the first zone based upon the sensor data, by applying at least one detection model configured to output at least one disposition based upon the sensor data as input data, where the at least one disposition comprises the first disposition, where the sensor data comprises a plurality of inputs to the at least one detection model, and where the identifying comprises aggregating a plurality of outputs of the at least one detection model from the plurality of inputs, and may report that the first disposition is associated with the first zone.

A computer implemented method of generating a custom signal from a data library containing multiple datasets of variable values correlated with time and geography includes receiving a user defined target variable, a time parameter, and a geography parameter, determining the applicable datasets from the data library overlapping the user-defined time parameter or geography parameter, testing the control variables of the applicable datasets for statistical significance to the target variable, aggregating a custom signal of at least three control variables having the greatest statistical significance to the target variable. The method includes generating a forecasting model by determining an internal feature analysis, determining an optimal external feature analysis, and selecting an optimal feature set based on a statistical strength of the internal feature analysis and the optimal external feature analysis.

Systems and methods of distributing campaign media are disclosed. In some embodiments, a campaign data structure is obtained related to campaign media. Additionally, audience data structures are obtained, wherein each of the audience data structures describes a corresponding audience of a plurality of audiences. Using a computer device, at least one target audience is selected for the campaign media from the plurality of audiences based on the campaign data structure and the audience data structures. Also using the computer device, one or more electronic transmissions of the campaign media are scheduled to at least one user device associated with the at least one target audience based on the campaign data structure and the audience data structures.

Techniques for data evaluation and optimized resource allocation are provided. Usage data is received collected from at least one sensor in a physical location, where the usage data indicates consumption of an item. The usage data is aggregated within a predefined cluster, where the predefined cluster corresponds to a geographic area. Future consumption of the item is predicted based at least in part on evaluating the usage data using one or more cognitive models. A recommended reconfiguration of one or more aspects of production for the item is generated, based on the predicted future consumption.