In some embodiments, apparatuses and methods are provided herein useful for predicting the time and geographic area of one or more weather events. More specifically, systems, apparatuses and methods are provided herein useful to providing accurate temporal prediction of the volume and location of expected insurance claims resulting from one or more weather events. In some embodiments, these teachings may be employed to predict outcomes such as a number of insurance claims or a monetary value of claim losses associated with a weather event. By one approach, in response to such prediction, these teachings may assist with the generation of responses in preparation for or in response to predicted weather events and insurance claims. For example, in some embodiments, the systems, apparatuses, and methods provided herein may be employed to mobilize resources, send notifications, and generate responses in preparation for or in response to one or more predicted insurance claims.

Embodiments are disclosed for a method that may include accessing events in a field-searchable data store. The events may include raw machine data associated with a timestamp. The raw machine data may represent interactions between a mobile device and one or more network devices at a locale. The method may further include determining, based on the interactions, one or more geographic positions of the mobile device, and calculating a metric for the locale using the geographic positions.

Provided is a process that determines a device-dwell graph based on noisy reported geolocations for mobile computing devices.

Techniques are described for determining locations of interest based on user visits. In some situations, the techniques include obtaining information about actual locations of users at various times, and automatically analyzing the information to determine particular locations in a geographic area that are of interest, such as for frequent destinations visited by users. After determining a particular location of interest, it may be represented by generating a corresponding location model to describe the geographic subarea or other location point(s) covered by the determined location of interest, and one or more points of interest (e.g., businesses, parks, schools, landmarks, etc.) may be identified that are located at or otherwise correspond to the determined location of interest. In addition, a determined location of interest may be further used in various ways, including to identify later user visits to that location (e.g., to a point of interest identified for the location).

Intermodal vehicles may be loaded with items and an aerial vehicle, and directed to travel to areas where demand for the items is known or anticipated. The intermodal vehicles may be coupled to locomotives, container ships, road tractors or other vehicles, and equipped with systems for loading one or more items onto the aerial vehicle, and for launching or retrieving the aerial vehicle while the intermodal vehicles are in motion. The areas where the demand is known or anticipated may be identified on any basis, including but not limited to past histories of purchases or deliveries to such areas, or events that are scheduled to occur in such areas. Additionally, intermodal vehicles may be loaded with replacement parts and/or inspection equipment, and configured to conduct repairs, servicing operations or inspections on aerial vehicles within the intermodal vehicles, while the intermodal vehicles are in motion.

A System and method for the application of in-memory analysis of network data applied to site selection with current market information, demand estimates, and competitor information comprising multiple data extractors, a descriptive module, a predictive module, a learning module, at least one application programming interface, and a visualization tool are disclosed. An example of network data is machine readable data that is acquired through an application programming interface. An example of in-memory analysis is the use of in-memory processing and storage objects. A descriptive module is configured to produce market features. An unsupervised learning module is configured to produce site selection market segments and a visualization tool is configured to evaluate one or more market scenarios and to display market features with maps and charts.

In one embodiment, a computer-implemented method includes acquiring sequential user behavior data including one-dimensional data. The user behavior data is associated with a user. The method includes abstracting features from the sequential user behavior data to cover short-term and long-term timeframes. The method includes determining one or more properties of the user based on the features.

In one embodiment, a method includes receiving user behavior data and contextual information associated with the user behavior data, the contextual information including a first data portion associated with a first context type. The method includes generating, from the user behavior data and the contextual information using a hashing algorithm, a first heterogeneous hash code including a first portion representing the user behavior data and a second hash code portion representing the first data portion associated with the first context type. The method includes accessing a second heterogeneous hash code including a third hash code portion representing a second data portion associated with the first context type. The method includes comparing the first heterogeneous hash code with the second heterogeneous hash code including determining similarity between the second hash code portion of the first heterogeneous hash code and the third hash code portion of the second heterogenous hash code.

The disclosure relates generally to estimating lifetime values, and more particularly to estimating Prospect Lifetime Values (PLTVs) for prospective customers for an organization. In one example, for estimating PLTV for a prospective customer, a distance of the prospective customer from each of existing customers of the organization is computed using existing customer data and prospective customer data. The existing customer data includes at least one of existing customer demographic variables and existing customer profile variables. Further, the prospective customer data includes at least one of prospective customer demographic variables and prospective customer profile variables. Subsequently, for the prospective customer, a pre-defined number of existing customers are determined from amongst the plurality of existing customers based on the distance. Thereafter, PLTV for the prospective customer is estimated using an average of Customer Lifetime Values (CLTVs) for the determined existing customers.

The disclosed embodiments provide systems, methods, and techniques for managing merchandising cards. A merchandising card may be, for example, a gift card, loyalty card, or the like. Consistent disclosed embodiments, a system for managing merchandising cards may include one or more memory devices storing instructions and one or more processors configured to acquire, from a device over a network, a plurality of locations associated with the device, the device locations being acquired at different instances in time within a predetermined period of time. Additionally, the one or more processors may be configured to calculate an overall merchant confidence rating for a merchant using the device locations. Further, the one or more processors may be configured to, based on the overall merchant confidence rating, determine that the merchant matches a merchant that is associated with merchandising card, and send a reminder a user of the device.