Abstract: A computing network includes nodes of different work groups. Nodes of a work group are dedicated to transactions of the work group. If a node of a first work group is predicted to have an idleness window, a second work group may borrow the node to execute a transaction of the second work group. At least a subset of steps of the transaction may be categorized into a step group. Trees of a transaction may be categorized into one or more tree groups. A node is selected for executing a transaction, if the predicted idleness duration of the node is sufficient relative to the predicted runtime of the transaction, the step group, and/or tree group. A credit system is maintained. A first work group transfers a credit to a second work group when borrowing a node of the second work group for executing a transaction of the first work group.

Abstract: A computing network includes nodes of different work groups. Nodes of a work group are dedicated to transactions of the work group. If a node of a first work group is predicted to have an idleness window, a second work group may borrow the node to execute a transaction of the second work group. At least a subset of steps of the transaction may be categorized into a step group. Trees of a transaction may be categorized into one or more tree groups. A node is selected for executing a transaction, if the predicted idleness duration of the node is sufficient relative to the predicted runtime of the transaction, the step group, and/or tree group. A credit system is maintained. A first work group transfers a credit to a second work group when borrowing a node of the second work group for executing a transaction of the first work group.

Abstract: A computing network includes nodes of different work groups. Nodes of a work group are dedicated to transactions of the work group. If a node of a first work group is predicted to have an idleness window, a second work group may borrow the node to execute a transaction of the second work group. At least a subset of steps of the transaction may be categorized into a step group. Trees of a transaction may be categorized into one or more tree groups. A node is selected for executing a transaction, if the predicted idleness duration of the node is sufficient relative to the predicted runtime of the transaction, the step group, and/or tree group. A credit system is maintained. A first work group transfers a credit to a second work group when borrowing a node of the second work group for executing a transaction of the first work group.

Abstract: A system provides automatic, end-to-end tagging of email messages. While a message is being composed at a sending email client, the server may receive email information that is used as an input to a predictive model. The model identifies tags that are available to a specific user group or email list that apply to the email message. These predicted tags are sent back to the email client, where they may be embedded in the email message with other user-defined tags. As the message is passed through the email server, the system may use any changes made to the predicted tags to retrain the model. When the message is received at a second email client, the receiver may further edit the tags, and any changes may again be used to retrain the model.

Abstract: An artificial neural network (ANN) determines a conversation snippet sentiment score based on content of the conversation snippet and contextual attributes associated with the conversation snippet. Contextual attributes may include, for example, a role within an organizational hierarchy of a user participating in the conversation snippet. Information representing the content is input into a hidden layer sequence of the ANN; information representing the contextual attributes is input into another hidden layer sequence of the ANN. Additionally or alternatively, a weighing engine determines a topical sentiment score by aggregating weighted conversation snippet sentiment scores.

Abstract: A system provides automatic, end-to-end tagging of email messages. While a message is being composed at a sending email client, the server may receive email information that is used as an input to a predictive model. The model identifies tags that are available to a specific user group or email list that apply to the email message. These predicted tags are sent back to the email client, where they may be embedded in the email message with other user-defined tags. As the message is passed through the email server, the system may use any changes made to the predicted tags to retrain the model. When the message is received at a second email client, the receiver may further edit the tags, and any changes may again be used to retrain the model.

Abstract: A system provides automatic, end-to-end tagging of email messages. While a message is being composed at a sending email client, the server may receive email information that is used as an input to a predictive model. The model identifies tags that are available to a specific user group or email list that apply to the email message. These predicted tags are sent back to the email client, where they may be embedded in the email message with other user-defined tags. As the message is passed through the email server, the system may use any changes made to the predicted tags to retrain the model. When the message is received at a second email client, the receiver may further edit the tags, and any changes may again be used to retrain the model.

Abstract: Methodologies and systems are described herein whereby application functionality may be optimized and extended based on a contextual analysis of the features that are accessed within the application. In one or more embodiments, the contextual analysis includes (a) detecting that an end user of an application is using a particular feature of the application and (b) determining that a set of one or more third-party applications are configured for operations accessing data sets of the same type accessed by the particular feature. Responsive at least to the detecting and determining operations, the set of one or more third-party applications are presented to the user when the user is accessing the particular feature of the first application.

Abstract: An artificial neural network (ANN) determines a conversation snippet sentiment score based on content of the conversation snippet and contextual attributes associated with the conversation snippet. Contextual attributes may include, for example, a role within an organizational hierarchy of a user participating in the conversation snippet. Information representing the content is input into a hidden layer sequence of the ANN; information representing the contextual attributes is input into another hidden layer sequence of the ANN. Additionally or alternatively, a weighing engine determines a topical sentiment score by aggregating weighted conversation snippet sentiment scores.

Abstract: Methodologies and systems are described herein whereby application functionality may be optimized and extended based on a contextual analysis of the features that are accessed within the application. In one or more embodiments, the contextual analysis includes (a) detecting that an end user of an application is using a particular feature of the application and (b) determining that a set of one or more third-party applications are configured for operations accessing data sets of the same type accessed by the particular feature. Responsive at least to the detecting and determining operations, the set of one or more third-party applications are presented to the user when the user is accessing the particular feature of the first application.

Abstract: Methodologies and systems are described herein whereby application functionality may be optimized and extended based on a contextual analysis of the features that are accessed within the application. In one or more embodiments, the contextual analysis includes (a) detecting that an end user of an application is using a particular feature of the application and (b) determining that a set of one or more third-party applications are configured for operations accessing data sets of the same type accessed by the particular feature. Responsive at least to the detecting and determining operations, the set of one or more third-party applications are presented to the user when the user is accessing the particular feature of the first application.

Abstract: Systems and methods, are provided for self-healing solutions to problems that occur during a software build. In one embodiment, build log data structures of build data, generated during execution of a software build of a software application, are automatically monitored. The build data is automatically analyzed to detect patterns that can result in build violations. Detected patterns are automatically matched to remediation solutions stored in a knowledge database. The remediation solutions are automatically applied to the software application.

Abstract: Methodologies and systems are described herein whereby application functionality may be optimized and extended based on a contextual analysis of the features that are accessed within the application. In one or more embodiments, the contextual analysis includes (a) detecting that an end user of an application is using a particular feature of the application and (b) determining that a set of one or more third-party applications are configured for operations accessing data sets of the same type accessed by the particular feature. Responsive at least to the detecting and determining operations, the set of one or more third-party applications are presented to the user when the user is accessing the particular feature of the first application.

Abstract: Systems, methods, and other embodiments are disclosed that provide self-healing solutions to problems that occur during a software build. In one embodiment, build log data structures of build data, generated during execution of a software build of a software application, are automatically monitored. The build data is automatically analyzed to detect patterns that can result in build violations. Detected patterns are automatically matched to remediation solutions stored in a knowledge database. The remediation solutions are automatically applied to the software application.