Abstract: A method for transmitting ticket information to user devices based on interactions of the user devices with a ticket system, is disclosed. The method involves identifying interactions of a first user device and a second user device with the ticket system within a predefined time period. The interactions are associated with ticket requests for a resource. Interaction scores are generated and updated based on the number of interactions. Upon interaction with the first user device, a first interface containing first ticket information is transmitted within the predefined time period, determined by the first interaction score. Simultaneously, in response to the second user device interaction, a second interface with second ticket information is transmitted, determined by the second interaction score. Notably, the second ticket information includes a second quantity of available tickets for the resource, which is lesser than a first quantity in the first ticket information.

Abstract: Certain aspects and features of the present disclosure relate to systems and methods that generate machine-learning models to predict whether user devices are likely to meet defined objectives. For example, a machine-learning model can be generated to predict whether or not a user device is likely to access a resource. In some implementations, a semi-supervised model can be used to determine to what extent user devices are predicted to satisfy the defined objective(s). For example, a resource-affinity parameter can be generated as a result of inputting various data points into a semi-supervised model. The various data points can be access from a plurality of data sources, and can represent one or more activities or attributes associated with a user. The value of the resource-affinity parameter can be evaluated to determine the extent to which the user is likely to meet an objective.

Abstract: Certain aspects and features of the present disclosure relate to systems and methods that generate machine-learning models to predict whether user devices are likely to meet defined objectives. For example, a machine-learning model can be generated to predict whether or not a user device is likely to access a resource. In some implementations, a semi-supervised model can be used to determine to what extent user devices are predicted to satisfy the defined objective(s). For example, a resource-affinity parameter can be generated as a result of inputting various data points into a semi-supervised model. The various data points can be access from a plurality of data sources, and can represent one or more activities or attributes associated with a user. The value of the resource-affinity parameter can be evaluated to determine the extent to which the user is likely to meet an objective.

Abstract: A system and method for scheduling tasks associated with controlling access to databases. The system and method relate to scheduling tasks for data requesting systems that satisfy particular conditions. For example, data requesting systems that satisfy the conditions may have associated tasks stored in a queue during a first processing phase. Data requesting systems that do not satisfy the conditions may have associated tasks inhibited from being stored in the queue during the first processing phase, but these tasks may be stored in the queue during a later second processing phase. Tasks stored in the queue during the first processing phase may be processed before tasks stored in the queue during the second processing phase. For example, the tasks may correspond to accessing a database for querying data representing access rights to a resource.

Abstract: A system and method for scheduling tasks associated with changing a personality of a ticketing interface. One or more processors generate interaction scores for each of the plurality of user devices based on receiving interactions between the ticketing engine and a plurality of user devices. The system further generate interaction patterns for each of the plurality of user devices that include a relation between the interaction scores generated for each of the plurality of user devices with the interactions from the plurality of user devices. The system further classify each of the plurality of user devices based on the generated interaction patterns to identify whether a user device from the plurality of user devices is a fraudulent or a non-fraudulent user device and modify interface of the ticketing engine based on the classification of each of the plurality of user devices.

Abstract: Certain aspects and features of the present disclosure relate to systems and methods that generate machine-learning models to predict whether user devices are likely to meet defined objectives. For example, a machine-learning model can be generated to predict whether or not a user device is likely to access a resource. In some implementations, a semi-supervised model can be used to determine to what extent user devices are predicted to satisfy the defined objective(s). For example, a resource-affinity parameter can be generated as a result of inputting various data points into a semi-supervised model. The various data points can be access from a plurality of data sources, and can represent one or more activities or attributes associated with a user. The value of the resource-affinity parameter can be evaluated to determine the extent to which the user is likely to meet an objective.

Abstract: Certain aspects and features of the present disclosure relate to systems and methods that generate machine-learning models to predict whether user devices are likely to meet defined objectives. For example, a machine-learning model can be generated to predict whether or not a user device is likely to access a resource. In some implementations, a semi-supervised model can be used to determine to what extent user devices are predicted to satisfy the defined objective(s). For example, a resource-affinity parameter can be generated as a result of inputting various data points into a semi-supervised model. The various data points can be access from a plurality of data sources, and can represent one or more activities or attributes associated with a user. The value of the resource-affinity parameter can be evaluated to determine the extent to which the user is likely to meet an objective.

Abstract: Certain aspects and features of the present disclosure relate to systems and methods that generate machine-learning models to predict whether user devices are likely to meet defined objectives. For example, a machine-learning model can be generated to predict whether or not a user device is likely to access a resource. In some implementations, a semi-supervised model can be used to determine to what extent user devices are predicted to satisfy the defined objective(s). For example, a resource-affinity parameter can be generated as a result of inputting various data points into a semi-supervised model. The various data points can be access from a plurality of data sources, and can represent one or more activities or attributes associated with a user. The value of the resource-affinity parameter can be evaluated to determine the extent to which the user is likely to meet an objective.

Abstract: A system and method for scheduling tasks associated with controlling access to databases. The system and method relate to scheduling tasks for data requesting systems that satisfy particular conditions. For example, data requesting systems that satisfy the conditions may have associated tasks stored in a queue during a first processing phase. Data requesting systems that do not satisfy the conditions may have associated tasks inhibited from being stored in the queue during the first processing phase, but these tasks may be stored in the queue during a later second processing phase. Tasks stored in the queue during the first processing phase may be processed before tasks stored in the queue during the second processing phase. For example, the tasks may correspond to accessing a database for querying data representing access rights to a resource.

Abstract: A system and method for scheduling tasks associated with controlling access to databases. The system and method relate to scheduling tasks for data requesting systems that satisfy particular conditions. For example, data requesting systems that satisfy the conditions may have associated tasks stored in a queue during a first processing phase. Data requesting systems that do not satisfy the conditions may have associated tasks inhibited from being stored in the queue during the first processing phase, but these tasks may be stored in the queue during a later second processing phase. Tasks stored in the queue during the first processing phase may be processed before tasks stored in the queue during the second processing phase. For example, the tasks may correspond to accessing a database for querying data representing access rights to a resource.

Abstract: Certain aspects and features of the present disclosure relate to systems and methods that generate machine-learning models to predict whether user devices are likely to meet defined objectives. For example, a machine-learning model can be generated to predict whether or not a user device is likely to access a resource. In some implementations, a semi-supervised model can be used to determine to what extent user devices are predicted to satisfy the defined objective(s). For example, a resource-affinity parameter can be generated as a result of inputting various data points into a semi-supervised model. The various data points can be access from a plurality of data sources, and can represent one or more activities or attributes associated with a user. The value of the resource-affinity parameter can be evaluated to determine the extent to which the user is likely to meet an objective.

Abstract: A method of forwarding packets by a physical network switch is provided. The method assigns egress ports that connect the network switch to each particular next hop to a weighted-cost multipathing (WCMP) group associated with the particular next hop. The method assigns weights to each egress port in each WCMP group according to the capacity of each path that connects the egress port to the next hop associated with the WCMP group and normalizes the weights over a range of values. For each packet received at the network switch, the method identifies the WCMP group associated with a next hop destination of the packet. The method calculates a hash value of a set of fields in the packet header and uses the hash value to perform a range lookup in the identified WCMP group to select an egress port for forwarding the packet to the next hop.

Abstract: Certain aspects and features of the present disclosure relate to systems and methods that generate machine-learning models to predict whether user devices are likely to meet defined objectives. For example, a machine-learning model can be generated to predict whether or not a user device is likely to access a resource. In some implementations, a semi-supervised model can be used to determine to what extent user devices are predicted to satisfy the defined objective(s). For example, a resource-affinity parameter can be generated as a result of inputting various data points into a semi-supervised model. The various data points can be access from a plurality of data sources, and can represent one or more activities or attributes associated with a user. The value of the resource-affinity parameter can be evaluated to determine the extent to which the user is likely to meet an objective.

Abstract: Certain aspects and features of the present disclosure relate to systems and methods that generate machine-learning models to predict whether user devices are likely to meet defined objectives. For example, a machine-learning model can be generated to predict whether or not a user device is likely to access a resource. In some implementations, a semi-supervised model can be used to determine to what extent user devices are predicted to satisfy the defined objective(s). For example, a resource-affinity parameter can be generated as a result of inputting various data points into a semi-supervised model. The various data points can be access from a plurality of data sources, and can represent one or more activities or attributes associated with a user. The value of the resource-affinity parameter can be evaluated to determine the extent to which the user is likely to meet an objective.

Abstract: A method of forwarding packets by a physical network switch is provided. The method assigns egress ports that connect the network switch to each particular next hop to a weighted-cost multipathing (WCMP) group associated with the particular next hop. The method assigns weights to each egress port in each WCMP group according to the capacity of each path that connects the egress port to the next hop associated with the WCMP group and normalizes the weights over a range of values. For each packet received at the network switch, the method identifies the WCMP group associated with a next hop destination of the packet. The method calculates a hash value of a set of fields in the packet header and uses the hash value to perform a range lookup in the identified WCMP group to select an egress port for forwarding the packet to the next hop.

Abstract: Certain aspects and features of the present disclosure relate to systems and methods that generate machine-learning models to predict whether user devices are likely to meet defined objectives. For example, a machine-learning model can be generated to predict whether or not a user device is likely to access a resource. In some implementations, a semi-supervised model can be used to determine to what extent user devices are predicted to satisfy the defined objective(s). For example, a resource-affinity parameter can be generated as a result of inputting various data points into a semi-supervised model. The various data points can be access from a plurality of data sources, and can represent one or more activities or attributes associated with a user. The value of the resource-affinity parameter can be evaluated to determine the extent to which the user is likely to meet an objective.

Abstract: A system and method for scheduling tasks associated with controlling access to databases. The system and method relate to scheduling tasks for data requesting systems that satisfy particular conditions. For example, data requesting systems that satisfy the conditions may have associated tasks stored in a queue during a first processing phase. Data requesting systems that do not satisfy the conditions may have associated tasks inhibited from being stored in the queue during the first processing phase, but these tasks may be stored in the queue during a later second processing phase. Tasks stored in the queue during the first processing phase may be processed before tasks stored in the queue during the second processing phase. For example, the tasks may correspond to accessing a database for querying data representing access rights to a resource.

Abstract: Certain aspects and features of the present disclosure relate to systems and methods that generate machine-learning models to predict whether user devices are likely to meet defined objectives. For example, a machine-learning model can be generated to predict whether or not a user device is likely to access a resource. In some implementations, a semi-supervised model can be used to determine to what extent user devices are predicted to satisfy the defined objective(s). For example, a resource-affinity parameter can be generated as a result of inputting various data points into a semi-supervised model. The various data points can be access from a plurality of data sources, and can represent one or more activities or attributes associated with a user. The value of the resource-affinity parameter can be evaluated to determine the extent to which the user is likely to meet an objective.

Abstract: Certain aspects and features of the present disclosure relate to systems and methods that generate machine-learning models to predict whether user devices are likely to meet defined objectives. For example, a machine-learning model can be generated to predict whether or not a user device is likely to access a resource. In some implementations, a semi-supervised model can be used to determine to what extent user devices are predicted to satisfy the defined objective(s). For example, a resource-affinity parameter can be generated as a result of inputting various data points into a semi-supervised model. The various data points can be access from a plurality of data sources, and can represent one or more activities or attributes associated with a user. The value of the resource-affinity parameter can be evaluated to determine the extent to which the user is likely to meet an objective.

Abstract: A method of forwarding packets by a physical network switch is provided. The method assigns egress ports that connect the network switch to each particular next hop to a weighted-cost multipathing (WCMP) group associated with the particular next hop. The method assigns weights to each egress port in each WCMP group according to the capacity of each path that connects the egress port to the next hop associated with the WCMP group and normalizes the weights over a range of values. For each packet received at the network switch, the method identifies the WCMP group associated with a next hop destination of the packet. The method calculates a hash value of a set of fields in the packet header and uses the hash value to perform a range lookup in the identified WCMP group to select an egress port for forwarding the packet to the next hop.