Abstract: Aspects of the present disclosure provide an apparatus for capturing an object. The apparatus includes a gas pressure source operable to provide a gas pressure at a gas pressure port, and a capture body having a conduit chamber in communication with the gas pressure port and a conduit opening in communication with the conduit chamber such that the capture body conveys, through the conduit chamber, to the conduit opening, at least some of the gas pressure provided by the gas pressure source at the gas pressure port. The capture body is operable to capture the object using at least some of the gas pressure conveyed through the conduit chamber to the conduit opening. The apparatus also includes a pressure sensor between the gas pressure port and the conduit opening, which is operable to measure gas pressure in the conduit chamber between the gas pressure port and the conduit opening.

Abstract: Described herein is a system and method for performing data management in a multi-cloud environment. The system includes a computing unit communicably connected to a central controller, comprising an input component adapted to present an input query, and an output component adapted to elicit a response from the central controller. The central controller includes a central repository adapted to receive multi-cloud environment resource datasets. The central controller includes processing modules, each comprising programming instructions and configured to perform a predetermined activity. Each processing module includes a data ingesting module, an app controller module and a security controller module. A user presents an input query on an interface configured onto a computing unit.

Abstract: The present invention relates generally to messaging platforms, and relates more particularly to data storage such that random sampling can be accomplished in real-time in messaging platforms. Aspects of the present invention include storing a bipartite graph with associations of two node types. The graph can be stored as a power law graph. The graph can be used to provide real-time content recommendations in a messaging platform. The content recommendations can be provided using random sampling of the node types stored in the graph.

Abstract: A method for distributed processing involves receiving a graph (G) of targets and of influencers, with each influencer related to at least one target, receiving an action graph of actions performed by one or more of the influencers, and key partitioning G across shards. The method further involves transposing the first graph (G) to obtain a first transposed graph (GT), value partitioning GT across the shards, storing the action graph on multiple shards, issuing, to a shard, a request specifying an influencer, to perform an intersection, receiving a response to the request of a set of influencers each of which is related to a target, and determining whether to send a recommendation to the target based on the response.

Abstract: A method for determining a circle of trust (CoT) includes receiving a request for the CoT, generating the CoT for the context account by: identifying a primary graph with nodes based on at least one action within a social network for the context account. The method further includes performing random walks through the nodes of the primary graph, each of the random walks including two steps, ranking each of the nodes based on an amount of the random walks that end on each of the nodes, with the CoT including a number of the highest ranking plurality of nodes, filtering content items using the CoT to identify a subset of relevant items, and providing the subset for display on a client device.

Abstract: A method for distributed processing involves receiving a graph (G) of targets and of influencers, with each influencer related to at least one target, receiving an action graph of actions performed by one or more of the influencers, and key partitioning G across shards. The method further involves transposing the first graph (G) to obtain a first transposed graph (GT), value partitioning GT across the shards, storing the action graph on multiple shards, issuing, to a shard, a request specifying an influencer, to perform an intersection, receiving a response to the request of a set of influencers each of which is related to a target, and determining whether to send a recommendation to the target based on the response.

Abstract: A method for determining a circle of trust (CoT) includes receiving a request for the CoT, generating the CoT for the context account by: identifying a primary graph with nodes based on at least one action within a social network for the context account. The method further includes performing random walks through the nodes of the primary graph, each of the random walks including two steps, ranking each of the nodes based on an amount of the random walks that end on each of the nodes, with the CoT including a number of the highest ranking plurality of nodes, filtering content items using the CoT to identify a subset of relevant items, and providing the subset for display on a client device.

Abstract: The present invention relates generally to messaging platforms, and relates more particularly to data storage such that random sampling can be accomplished in real-time in messaging platforms. Aspects of the present invention include storing a bipartite graph with associations of two node types. The graph can be stored as a power law graph. The graph can be used to provide real-time content recommendations in a messaging platform. The content recommendations can be provided using random sampling of the node types stored in the graph.

Abstract: A system and method for providing real-time search assistance is provided. Incoming queries are analyzed and an in core set of query statistics is maintained to enable a current suggestion list to be generated. By analyzing each query as it occurs, the suggestion list and associated data stores may be updated in substantially real-time to enable suggestions to be available at the same time as new messages are occurring relating to the subject of the query.

Abstract: A method for distributed processing involves receiving a graph (G) of targets and of influencers, with each influencer related to at least one target, receiving an action graph of actions performed by one or more of the influencers, and key partitioning G across shards. The method further involves transposing the first graph (G) to obtain a first transposed graph (GT), valuing partitioning GT across the shards, storing the action graph on multiple shards, issuing, to a shard, a request specifying an influencer, to perform an intersection, receiving a response to the request of a set of influencers each of which is related to a target, and determining whether to send a recommendation to the target based on the response.

Abstract: A method for distributed processing involves receiving a graph (G) of targets and of influencers, with each influencer related to at least one target, receiving an action graph of actions performed by one or more of the influencers, and key partitioning G across shards. The method further involves transposing the first graph (G) to obtain a first transposed graph (GT), valuing partitioning GT across the shards, storing the action graph on multiple shards, issuing, to a shard, a request specifying an influencer, to perform an intersection, receiving a response to the request of a set of influencers each of which is related to a target, and determining whether to send a recommendation to the target based on the response.

Abstract: A method for distributed processing involves receiving a graph (G) of targets and of influencers, with each influencer related to at least one target, receiving an action graph of actions performed by one or more of the influencers, and key partitioning G across shards. The method further involves transposing the first graph (G) to obtain a first transposed graph (GT), valuing partitioning GT across the shards, storing the action graph on multiple shards, issuing, to a shard, a request specifying an influencer, to perform an intersection, receiving a response to the request of a set of influencers each of which is related to a target, and determining whether to send a recommendation to the target based on the response.

Abstract: A method for distributed processing involves receiving a graph (G) of targets and of influencers, with each influencer related to at least one target, receiving an action graph of actions performed by one or more of the influencers, and key partitioning G across shards. The method further involves transposing the first graph (G) to obtain a first transposed graph (GT), valuing partitioning GT across the shards, storing the action graph on multiple shards, issuing, to a shard, a request specifying an influencer, to perform an intersection, receiving a response to the request of a set of influencers each of which is related to a target, and determining whether to send a recommendation to the target based on the response.

Abstract: A method for distributed processing involves receiving a graph (G) of targets and of influencers, with each influencer related to at least one target, receiving an action graph of actions performed by one or more of the influencers, and key partitioning G across shards. The method further involves transposing the first graph (G) to obtain a first transposed graph (GT), valuing partitioning GT across the shards, storing the action graph on multiple shards, issuing, to a shard, a request specifying an influencer, to perform an intersection, receiving a response to the request of a set of influencers each of which is related to a target, and determining whether to send a recommendation to the target based on the response.