Abstract: In one embodiment, a method includes determining a first geographic location of a client computer of a first user, receiving one or more first meet-up preferences from the first user, and identifying a second user based on one or more notification rules and the first meet-up preferences. The second user may be associated with one or more second meet-up preferences. After sending a first notification to the client computer of the first user and a second notification to the client computer of the second user based on the first meet-up preferences and the second meet-up preferences, the method further includes receiving a first opt-in response from the first user, receiving a second opt-in response from the second user, and sending a first meet-up confirmation to the first user and a second meet-up confirmation to the second user.

Abstract: In one embodiment, a method includes identifying a first node and a second node in a social graph. The historical location data is available for the first node and for the second node. The method also includes accessing one or more component metrics for computing a geo-metric. The geo-metric represents an assessment of a spatial commonality between the first node and the second node. The spatial commonalities are determined based on historical location data stored for the first node and historical location data stored for the second node. The method also includes accessing one or more coefficients each corresponding to one of the component metrics; calculating the geo-metric by applying the received coefficients to the component metrics and combining the component metrics; and providing the calculated geo-metric.

Abstract: In one embodiment, sensor signals corresponding to motions of a client device are received. Activities of a user corresponding to the client device are determined, based on a first analysis algorithm that uses the sensor signals to select activity types, each activity type corresponding to a particular time period. A reference to the selected activity types is sent to the client device. User input indicating whether the reference is correct is received from the client device. Based on the indication, which reflects the accuracy of the first analysis algorithm, a priority between the first analysis algorithm and a second analysis algorithm is determined, based on determining which analysis algorithm is more accurate.

Abstract: In an embodiment, a mobile device determines sensor signals from one or more sensors of the mobile device, the sensor signals indicating motion that is associated with activities of a user of the mobile device. The mobile device determines activity types that correspond to at least a portion of the determined sensor signals. The mobile device determines activity periods of the user, each activity period corresponding to a period of time for one of the determined activity types. The mobile device then performs a heuristics-type analysis based on the length of time for each activity period and the corresponding activity types to determine whether two or more activity periods should be combined into a single activity period. The determined activity types are displayed on the mobile device.

Abstract: In one embodiment, sensor signals corresponding to motions of a client device are received. Activities of a user corresponding to the client device are determined, based on a first analysis algorithm that uses the sensor signals to select activity types, each activity type corresponding to a particular time period. A reference to the selected activity types is sent to the client device. User input indicating whether the reference is correct is received from the client device. Based on the indication, which reflects the accuracy of the first analysis algorithm, a priority between the first analysis algorithm and a second analysis algorithm is determined, based on determining which analysis algorithm is more accurate.

Abstract: A continuous stream data (e.g., messages, tweets) is received by ingest nodes of a platform. The ingest nodes may analyze the data to create a transaction of graph updates, assign a sequence number to the transaction, and distribute the graph updates with the sequence number to graph nodes of the platform. The graph nodes may store graph updates from ingest nodes, and then the ingest nodes may report graph update progresses in a progress table. A snapshot may be taken based on the progress table, and then graph-mining computation may be implemented. Tolerate failures and decaying may be supported and incremental expansion may be allowed to cope with increasing update rates and computation needs.

Abstract: In an embodiment, a mobile device determines sensor signals from one or more sensors of the mobile device, the sensor signals indicating motion that is associated with activities of a user of the mobile device. The mobile device determines activity types that correspond to at least a portion of the determined sensor signals. The mobile device determines activity periods of the user, each activity period corresponding to a period of time for one of the determined activity types. The mobile device then performs a heuristics-type analysis based on the length of time for each activity period and the corresponding activity types to determine whether two or more activity periods should be combined into a single activity period. The determined activity types are displayed on the mobile device.

Abstract: In one embodiment, a method includes determining a first geographic location of a client computer of a first user, receiving one or more first meet-up preferences from the first user, and identifying a second user based on one or more notification rules and the first meet-up preferences. The second user may be associated with one or more second meet-up preferences. After sending a first notification to the client computer of the first user and a second notification to the client computer of the second user based on the first meet-up preferences and the second meet-up preferences, the method further includes receiving a first opt-in response from the first user, receiving a second opt-in response from the second user, and sending a first meet-up confirmation to the first user and a second meet-up confirmation to the second user.

Abstract: In one embodiment, sensor signals corresponding to motions of a computing device are determined. Activities of a user corresponding to the computing device are determined by selecting activity types which are based on the sensor signals, a set of user characteristics associated with the user, a classification of the set of user characteristics, and signal parameters. A reference to the selected activity types is displayed to the user. User input indicating whether the reference is correct is received. Based on the user input, the signal parameters may be adjusted.

Abstract: In one embodiment, a method includes identifying a first node and a second node in a social graph. The historical location data is available for the first node and for the second node. The method also includes accessing one or more component metrics for computing a geo-metric. The geo-metric represents an assessment of a spatial commonality between the first node and the second node. The spatial commonalities are determined based on historical location data stored for the first node and historical location data stored for the second node. The method also includes accessing one or more coefficients each corresponding to one of the component metrics; calculating the geo-metric by applying the received coefficients to the component metrics and combining the component metrics; and providing the calculated geo-metric.

Abstract: In an embodiment, a computing device determines sensor signals detected by one or more sensors of the computing device. The sensor signals indicate motion of the computing device. The computing device selects one of a plurality of activity categories that corresponds to a portion of the sensor signals, each of the activity categories including one or more activity types. The activity types in each activity category are characterized by a common motion corresponding to the portion of the sensor signals. One of the activity types in the selected activity category is determined by analyzing the sensor signals with respect to each of the activity types in the selected activity category, and calculating a probability of each of the activity types. The determined activity type is displayed on the computing device.

Abstract: A continuous stream data (e.g., messages, tweets) is received by ingest nodes of a platform. The ingest nodes may analyze the data to create a transaction of graph updates, assign a sequence number to the transaction, and distribute the graph updates with the sequence number to graph nodes of the platform. The graph nodes may store graph updates from ingest nodes, and then the ingest nodes may report graph update progresses in a progress table. A snapshot may be taken based on the progress table, and then graph-mining computation may be implemented. Tolerate failures and decaying may be supported and incremental expansion may be allowed to cope with increasing update rates and computation needs.

Abstract: A continuous stream data (e.g., messages, tweets) is received by ingest nodes of a platform. The ingest nodes may analyze the data to create a transaction of graph updates, assign a sequence number to the transaction, and distribute the graph updates with the sequence number to graph nodes of the platform. The graph nodes may store graph updates from ingest nodes, and then the ingest nodes may report graph update progresses in a progress table. A snapshot may be taken based on the progress table, and then graph-mining computation may be implemented. Tolerate failures and decaying may be supported and incremental expansion may be allowed to cope with increasing update rates and computation needs.

Abstract: In an embodiment, a computing device determines sensor signals detected by one or more sensors of the computing device. The sensor signals indicate motion of the computing device. The computing device selects one of a plurality of activity categories that corresponds to a portion of the sensor signals, each of the activity categories including one or more activity types. The activity types in each activity category are characterized by a common motion corresponding to the portion of the sensor signals. One of the activity types in the selected activity category is determined by analyzing the sensor signals with respect to each of the activity types in the selected activity category, and calculating a probability of each of the activity types. The determined activity type is displayed on the computing device.

Abstract: In one embodiment, sensor signals corresponding to motions of a computing device are determined. Activities of a user corresponding to the computing device are determined by selecting activity types which are based on the sensor signals, a set of user characteristics associated with the user, a classification of the set of user characteristics, and signal parameters. A reference to the selected activity types is displayed to the user. User input indicating whether the reference is correct is received. Based on the user input, the signal parameters may be adjusted.

Abstract: The present disclosure provides a system for tracking and recording movements of a mobile communication device that includes one or more movement sensors. The mobile communication device communicates sensor signals to the system, wherein the sensor signals are indicative of motion associated with activities to which the mobile communication device is exposed by its user. The sensor signals are pre-classified to generate the intermediate data, where the intermediate data is processed in one or more processors to generate indications of likely activities associated with the sensor signals, and an aggregate of the indications is computed to provide an analysis of activities associated with the sensor signals. The information indicating most likely activity types is then sent to the mobile communication device.

Abstract: In one embodiment, sensor signals corresponding to motions of a client computing device are received. The sensor signals are associated with activity periods, each activity period also being associated with a geographic location of the client computing device. For each activity period, one or more activity types are determined for the client computing device based on the sensor signals for the activity period and one or more signal parameters. A reference of the activity type and activity period is sent to the client computing device, and an indication is received regarding whether that reference is correct. The signal parameters may be adjusted based on the received indication.

Abstract: In one embodiment, a computing device receives from a mobile device sensor signals which indicate motion associated with activities of a user of the mobile device. The computing device determines a number of activity types that correspond to at least a portion of the received sensor signals. The computing device determines a number of activity periods of the user, each activity period corresponding to a likely activity type, by analyzing the sensor signals against each of the activity types, and calculating a probability of each of the activity types. The computing device determines whether two or more of the determined activity periods should be combined into a single period, based on a heuristics-type analysis based on a length of time for each of the two or more activity periods. The activity periods are sent to the mobile device.

Abstract: In one embodiment, a computing device receives from a mobile device sensor signals which indicate motion associated with activities of a user of the mobile device. The computing device determines a number of activity types that correspond to at least a portion of the received sensor signals. The computing device determines a number of activity periods of the user, each activity period corresponding to a likely activity type, by analyzing the sensor signals against each of the activity types, and calculating a probability of each of the activity types. The computing device determines whether two or more of the determined activity periods should be combined into a single period, based on a heuristics-type analysis based on a length of time for each of the two or more activity periods. The activity periods are sent to the mobile device.

Abstract: A continuous stream data (e.g., messages, tweets) is received by ingest nodes of a platform. The ingest nodes may analyze the data to create a transaction of graph updates, assign a sequence number to the transaction, and distribute the graph updates with the sequence number to graph nodes of the platform. The graph nodes may store graph updates from ingest nodes, and then the ingest nodes may report graph update progresses in a progress table. A snapshot may be taken based on the progress table, and then graph-mining computation may be implemented. Tolerate failures and decaying may be supported and incremental expansion may be allowed to cope with increasing update rates and computation needs.