Abstract: The present disclosure provides apparatus, systems, and methods for crowdsourcing domain specific intelligence. The disclosed crowdsourcing mechanism can receive domain specific intelligence as a data processing rule module. For example, a data analytics system can request a crowd of software developers to provide a data processing rule module tailored to process a particular type of information from a particular domain. When the data analytics system receives the data processing rule module from one of the software developers for the particular domain, the data analytics system can use the received data processing rule module to process information associated with the particular domain.

Abstract: The present application relates to apparatus, systems, and methods for grouping data records based on entities referenced by the data records. The disclosed grouping mechanism can include determining a pair-wise similarity between a large number of data records, and clustering a subset of the data records based on their pair-wise similarity.

Abstract: An online system matches source and target entities using multiple queues. The online system performs clustering of target entities to generate a plurality of clusters based on vector distances between pairs of target entities. Each cluster comprises target entities that are close to other target entities within the cluster compared to target entities of remaining clusters. The online system optimizes an aggregate score across the plurality of entities using a plurality of queues. Each queue is associated with a source entity and includes one or more target entities. The online system identifies a cluster of target entities based on a distance between the source entities and entities of the cluster of target entities. The online system selects a subset of target entities from the identified cluster using a machine learning based model trained to determine a score between an input source entity and target entity.

Abstract: Methods and systems are provided that may be utilized to detect occurrence of one or more specified circumstances. A determination may be made as to whether one or more specified circumstances are detected such as responsive to one or more user actions or an occurrence of an event unrelated to a user. One or more binary digital signals may be generated to store a detection of one or more specified user circumstances in a log or memory at least partially in response to detection of the one or more specified circumstances.

Abstract: The present application relates to apparatus, systems, and methods for grouping data records based on entities referenced by the data records. The disclosed grouping mechanism can include determining a pair-wise similarity between a large number of data records, and clustering a subset of the data records based on their pair-wise similarity.

Abstract: An online system matches source and target entities using multiple queues. The online system receives information identifying a plurality of source entities and a plurality of target entities. The online system maintains a plurality of queues, each queue associated with a source entity. Each queue comprises one or more target entities. A target entity is included in a queue based on matching of the target entity with a source entity. The online system may perform matching using a machine learning model. The online system selects a particular target entity for a particular source entity from the queue corresponding to the source entity. The online system schedules an interaction between the particular source entity and the particular target entity.

Abstract: An online system matches source and target entities using multiple queues. The online system performs clustering of target entities to generate a plurality of clusters based on vector distances between pairs of target entities. Each cluster comprises target entities that are close to other target entities within the cluster compared to target entities of remaining clusters. The online system optimizes an aggregate score across the plurality of entities using a plurality of queues. Each queue is associated with a source entity and includes one or more target entities. The online system identifies a cluster of target entities based on a distance between the source entities and entities of the cluster of target entities. The online system selects a subset of target entities from the identified cluster using a machine learning based model trained to determine a score between an input source entity and target entity.

Abstract: The present disclosure relates to apparatus, systems, and methods for providing a location information analytics mechanism. The location information analytics mechanism is configured to analyze location information to extract contextual information (e.g., profile) about a mobile device or a user of a mobile device, collectively referred to as a target entity. The location information analytics mechanism can include analyzing location data points associated with a target entity to determine features associated with the target entity, and using the features to predict attributes associated with the target entity. The set of predicted attributes can form a profile of the target entity.

Abstract: The present disclosure relates to apparatus, systems, and methods for providing a location information analytics mechanism. The location information analytics mechanism is configured to analyze location information to extract contextual information (e.g., profile) about a mobile device or a user of a mobile device, collectively referred to as a target entity. The location information analytics mechanism can include analyzing location data points associated with a target entity to determine features associated with the target entity, and using the features to predict attributes associated with the target entity. The set of predicted attributes can form a profile of the target entity.

Abstract: The present disclosure relates to apparatus, systems, and methods for analyzing characteristics of entities of interest. In particular, the present disclosure provides a mechanism for analyzing information about entities of interest and for rating or scoring the entities of interest based on the analyzed information. The rating or the score of an entity of interest can sometimes be referred to as a placerank value of an entity of interest.

Abstract: The present disclosure relates to apparatus, systems, and methods for analyzing characteristics of entities of interest. In particular, the present disclosure provides a mechanism for analyzing information about entities of interest and for rating or scoring the entities of interest based on the analyzed information. The rating or the score of an entity of interest can sometimes be referred to as a placerank value of an entity of interest.

Abstract: The disclosed apparatus, systems, and methods relate to a location query mechanism that can efficiently determine whether a target entity is located within a region of interest (ROI). At a high level, the location query mechanism can be configured to represent a ROI using one or more polygons. The location query mechanism can, in turn, divide (e.g., Jesse Hate) the one or more polygons into sub-polygons. Subsequently, the location query mechanism can use the sub-polygons to build an index system that can efficiently determine whether a particular location is within any of the sub-polygons. Therefore, when a computing device queries whether a particular location is within the region of interest, the location query mechanism can use the index system to determine whether the particular location is within any of the sub-polygons.

Abstract: Methods and systems are provided that may be utilized to detect occurrence of one or more specified circumstances. A determination may be made as to whether one or more specified circumstances are detected such as responsive to one or more user actions or an occurrence of an event unrelated to a user. One or more binary digital signals may be generated to store a detection of one or more specified user circumstances in a log or memory at least partially in response to detection of the one or more specified circumstances.

Abstract: A traditional data processing system is configured to process input data either in batch or in real-time. On one hand, a batch data processing system is limiting because the batch data processing often cannot take into account any data received during the batch data processing. On the other hand, a real-time data processing system is limiting because the real-time system often cannot scale. The real-time data processing system is often limited to dealing with primitive data types and/or a small amount of data. Therefore, it is desirable to address the limitations of the batch data processing system and the real-time data processing system by combining the benefits of the batch data processing system and the real-time data processing system into a single data processing system.

Abstract: The present disclosure relates to apparatus, systems, and methods for providing a location information analytics mechanism. The location information analytics mechanism is configured to analyze location information to extract contextual information (e.g., profile) about a mobile device or a user of a mobile device, collectively referred to as a target entity. The location information analytics mechanism can include analyzing location data points associated with a target entity to determine features associated with the target entity, and using the features to predict attributes associated with the target entity. The set of predicted attributes can form a profile of the target entity.

Abstract: The present application relates to apparatus, systems, and methods for grouping data records based on entities referenced by the data records. The disclosed grouping mechanism can include determining a pair-wise similarity between a large number of data records, and clustering a subset of the data records based on their pair-wise similarity.

Abstract: The present disclosure provides apparatus, systems, and methods for crowdsourcing domain specific intelligence. The disclosed crowdsourcing mechanism can receive domain specific intelligence as a data processing rule module. For example, a data analytics system can request a crowd of software developers to provide a data processing rule module tailored to process a particular type of information from a particular domain. When the data analytics system receives the data processing rule module from one of the software developers for the particular domain, the data analytics system can use the received data processing rule module to process information associated with the particular domain.

Abstract: The disclosed apparatus, systems, and methods relate to a location query mechanism that can efficiently determine whether a target entity is located within a region of interest (ROI). At a high level, the location query mechanism can be configured to represent a ROI using one or more polygons. The location query mechanism can, in turn, divide (e.g., Jesse Hate) the one or more polygons into sub-polygons. Subsequently, the location query mechanism can use the sub-polygons to build an index system that can efficiently determine whether a particular location is within any of the sub-polygons. Therefore, when a computing device queries whether a particular location is within the region of interest, the location query mechanism can use the index system to determine whether the particular location is within any of the sub-polygons.

Abstract: Methods and systems are provided that may be utilized to detect occurrence of one or more specified circumstances. A determination may be made as to whether one or more specified circumstances are detected such as responsive to one or more user actions or an occurrence of an event unrelated to a user. One or more binary digital signals may be generated to store a detection of one or more specified user circumstances in a log or memory at least partially in response to detection of the one or more specified circumstances.

Abstract: A traditional data processing system is configured to process input data either in batch or in real-time. On one hand, a batch data processing system is limiting because the batch data processing often cannot take into account any data received during the batch data processing. On the other hand, a real-time data processing system is limiting because the real-time system often cannot scale. The real-time data processing system is often limited to dealing with primitive data types and/or a small amount of data. Therefore, it is desirable to address the limitations of the batch data processing system and the real-time data processing system by combining the benefits of the batch data processing system and the real-time data processing system into a single data processing system.