Abstract: A technique selects and serves local information items (e.g., news articles) to users. In a first stage, the technique uses a machine-trained localness-determining system to determine whether a candidate information item contains the kind of information that qualifies as locally-themed. In a second stage, a scope-determining system determines a particular geographic region associated with the information item. The technique then selectively serves the information item to a particular consumer upon determining that the particular consumer is located in the particular geographic region associated with the item. In some implementations, the scope-determining system describes the particular geographic region of the information item using a set of geohashes, and describes the location of the consumer using at least one geohash. The technique uses an ensemble approach to identify the particular geographic region of the item, and to generate training examples for use in training the localness-determining system.

Abstract: A technique selects and serves local information items (e.g., news articles) to users. In a first stage, the technique uses a machine-trained localness-determining system to determine whether a candidate information item contains the kind of information that qualifies as locally-themed. In a second stage, a scope-determining system determines a particular geographic region associated with the information item. The technique then selectively serves the information item to a particular consumer upon determining that the particular consumer is located in the particular geographic region associated with the item. In some implementations, the scope-determining system describes the particular geographic region of the information item using a set of geohashes, and describes the location of the consumer using at least one geohash. The technique uses an ensemble approach to identify the particular geographic region of the item, and to generate training examples for use in training the localness-determining system.

Abstract: A device may receive, from Internet of Things (IOT) devices, telemetry data in a variety of formats and may convert the telemetry data to converted telemetry data in a first unified format. The device may receive, from one or more of the IoT devices, offline IoT data in a variety of formats and may convert the offline IoT data to converted offline IoT data in a second unified format. The device may receive events from one or more of the IoT devices and may identify anomalies based on the telemetry data and in near-real time relative to receiving the telemetry data. The device may store the converted telemetry data, the converted offline IoT data, the events, and the anomalies, as stored data, in a data structure, and may perform actions based on the stored data.