Abstract: Systems, methods, and software are disclosed herein for enhancing entity classification operations for digital maps. In an implementation, an entity classification system associates tiles in a grid overlaying a map with discrete positioning records produced by devices operating in areas represented in the map. For each tile in an area of interest in the grid, the system produces a scalar description based on a subset of the discrete positioning records associated with the tile. The system then performs a binary classification of each tile as a type of entity (e.g. a road, business, or residence) based on the scalar description of the tile and the scalar descriptions of other tiles in the area of interest.

Abstract: Systems, methods, and software for operational anomaly detection in communication systems is provided herein. An exemplary method includes obtaining a measured sequence of state information associated with the communications system during a first timeframe, processing the measured sequence of state information to determine a predicted sequence of state information for the communication system during a second timeframe, and monitoring current state information for the communication system over at least a portion of the second timeframe. The method also includes determining operational anomalies associated with the communication system based at least on a comparison between the current state information and the predicted sequence of state information.

Abstract: Systems, methods, and software are disclosed herein for enhancing entity classification operations for digital maps. In an implementation, an entity classification system associates tiles in a grid overlaying a map with discrete positioning records produced by devices operating in areas represented in the map. For each tile in an area of interest in the grid, the system produces a scalar description based on a subset of the discrete positioning records associated with the tile. The system then performs a binary classification of each tile as a type of entity (e.g. a road, business, or residence) based on the scalar description of the tile and the scalar descriptions of other tiles in the area of interest.

Abstract: Training data is collected describing multiple past messages sent over a computer-implemented communication service. For each of the past messages, the training data set comprises a record of a respective channel of the respective message, and a record of respective feature vector of the respective message, wherein the channel corresponds to a respective one or more recipients to which the respective message was sent, and wherein the feature vector comprises a respective set of values of a plurality of parameters associated with the sending of the respective message. The training data is used to train a machine learning algorithm. By applying the machine learning algorithm to the feature vector of a respective subsequent message, to be sent by a sending user over the computer-implemented communication service, a prediction is generated regarding one or more potential recipients of the subsequent message.

Abstract: Training data is collected describing multiple past communications over a computer-implemented communication service. For each of the past communications, the training data set comprises a record of a respective recipient of the respective communication, and a record of respective feature vector of the respective communication, wherein the recipient is defined in terms of an identity of in individual person, and wherein the feature vector comprises a respective set of values of a plurality of parameters associated with the respective communication. The training data is used to train a machine learning algorithm. By applying the machine learning algorithm to the feature vector of a respective subsequent message, to be sent by a sending user over the computer-implemented communication service, a prediction is generated regarding one or more potential recipients of the subsequent message.

Abstract: A geocoding architecture that generates and associates one or more tile documents with geocoded tiles. When connected entities are defined, the connected entity attributes are collected in a single tile document so that tile-document terms are attributes of all connected entities. These terms later serve as keys that enable search for tiles relevant for a given query. Entity documents are created that are an aggregation of entity attributes. Like the entity document, the tile document serves as an aggregator for all the geospatial entities within a pre-determined surface area. Search is then performed on the content of tile and entity documents.