Abstract: Various embodiments are directed to systems and methods for automatically distributing loads among computing devices involved in message delivery within a group-based communication platform. Embodiments utilize a status checker to monitor the relative health and/or utilization of various channel servers each servicing a group-based communication channel for communication among a particular group of client devices. Upon detecting that one or more of the channel servers exhibit failing health characteristics, the status checker may automatically reallocate the messaging load performed by the failing channel server to other servers, thereby redefining the group-based communication channel associated with a particular group to encompass the newly assigned channel server and minimizing the impact of the failed channel server on message distribution within the group-based communication channel.

Abstract: Subscriptions for object updates to be transmitted to a client device may be dynamically managed based on the occurrence of one or more trigger events at the client device, which causes the client device to transmit subscription modification requests to a computing platform configured to dynamically maintain object subscription lists for each of one or more client devices based at least in part on subscription modification requests received from each of the one or more client devices, to determine whether subscribed objects for a particular client device are current at the client device, and to transmit updates of subscribed objects in real-time upon determining that the particular client device does not comprise current object data.

Abstract: Media objects, such as images or soundtracks, may be ranked according to a new class of metrics known as “interestingness.” These rankings may be based at least in part on the quantity of user-entered metadata concerning the media object, the number of users who have assigned metadata to the media object, access patterns related to the media object, and/or a lapse of time related to the media object.

Abstract: The Messaging Search and Management Apparatuses, Methods and Systems (“MSM”) transforms message, ranking request inputs via MSM components into work graphs, ML structure input data, ML structure, ranking response outputs. A work graph generation request that includes group level access control data may be obtained. A set of metadata access control carrying messages, a set of users, a set of channels, and a set of topics with access control data corresponding to the group level access control data may be determined. A user priority score for each of the other users, a channel priority score for each of the channels, and a topic priority score for each of the topics, from the perspective of each user, may be calculated. A work graph data structure may be generated that includes, for each user, data regarding the calculated user priority scores, channel priority scores, and topic priority scores.

Abstract: The Messaging Search and Management Apparatuses, Methods and Systems (“MSM”) transforms message, ranking request inputs via MSM components into work graphs, ML structure input data, ML structure, ranking response outputs. A work graph generation request that includes group level access control data may be obtained. A set of metadata access control carrying messages, a set of users, a set of channels, and a set of topics with access control data corresponding to the group level access control data may be determined. A user priority score for each of the other users, a channel priority score for each of the channels, and a topic priority score for each of the topics, from the perspective of each user, may be calculated. A work graph data structure may be generated that includes, for each user, data regarding the calculated user priority scores, channel priority scores, and topic priority scores.

Abstract: Media objects, such as images or soundtracks, may be ranked according to a new class of metrics known as “interestingness.” These rankings may be based at least in part on the quantity of user-entered metadata concerning the media object, the number of users who have assigned metadata to the media object, access patterns related to the media object, and/or a lapse of time related to the media object.

Abstract: Metadata may be associated with media objects by providing media objects for display, and accepting input concerning the media objects, where the input may include at least two different types of metadata. For example, metadata may be in the form of tags, comments, annotations or favorites. The media objects may be searched according to metadata, and ranked in a variety of ways.

Abstract: The Messaging Search and Management Apparatuses, Methods and Systems (“MSM”) transforms message, ranking request inputs via MSM components into work graphs, ML structure input data, ML structure, ranking response outputs. A work graph generation request that includes group level access control data may be obtained. A set of metadata access control carrying messages, a set of users, a set of channels, and a set of topics with access control data corresponding to the group level access control data may be determined. A user priority score for each of the other users, a channel priority score for each of the channels, and a topic priority score for each of the topics, from the perspective of each user, may be calculated. A work graph data structure may be generated that includes, for each user, data regarding the calculated user priority scores, channel priority scores, and topic priority scores.

Abstract: Media objects, such as images or soundtracks, may be ranked according to a new class of metrics known as “interestingness.” These rankings may be based at least in part on the quantity of user-entered metadata concerning the media object, the number of users who have assigned metadata to the media object, access patterns related to the media object, and/or a lapse of time related to the media object.

Abstract: Media objects, such as images or soundtracks, may be ranked according to a new class of metrics known as “interestingness.” These rankings may be based at least in part on the quantity of user-entered metadata concerning the media object, the number of users who have assigned metadata to the media object, access patterns related to the media object, and/or a lapse of time related to the media object.

Abstract: Metadata may be associated with media objects by providing media objects for display, and accepting input concerning the media objects, where the input may include at least two different types of metadata. For example, metadata may be in the form of tags, comments, annotations or favorites. The media objects may be searched according to metadata, and ranked in a variety of ways.

Abstract: Metadata may be associated with media objects by providing media objects for display, and accepting input concerning the media objects, where the input may include at least two different types of metadata. For example, metadata may be in the form of tags, comments, annotations or favorites. The media objects may be searched according to metadata, and ranked in a variety of ways.

Abstract: Media objects, such as images or soundtracks, may be ranked according to a new class of metrics known as “interestingness.” These rankings may be based at least in part on the quantity of user-entered metadata concerning the media object, the number of users who have assigned metadata to the media object, access patterns related to the media object, and/or a lapse of time related to the media object.

Abstract: A method of executing bytecode by means of a secondary interpreter using the services of the primary virtual machine is disclosed. This facilitates the development and execution of long-lived processes that require execution context serialization.