Abstract: The disclosed embodiments illustrate methods and systems for crowdsourcing a task. The method includes extracting one or more components from at least one task based on a task category associated with the at least one task. Each component corresponds to a part of the at least one task, that requires an input from a crowdworker. Further, the one or more components are categorized in a set of first categories, each of which includes similar components, and a second category that includes dissimilar components. The categorization is based on a comparison between content of each of component, and the task category. Thereafter, a representative component from at least one category from the set of first categories, and at least one component from the second category are combined to create the task. The task, so created, is crowdsourced to one or more crowdworkers.

Abstract: According to embodiments illustrated herein, there is provided a method for processing one or more tasks. The method includes monitoring a worker-computing device for at least security threats. The method further includes transmitting selected one or more tasks to the worker-computing device. Further a worker associated with the worker-computing device processes the one or more tasks, while the worker computing device is disconnected from the crowdsourcing platform. The method further includes monitoring the processing of the one or more tasks by the worker, while the worker computing device is disconnected from the crowdsourcing platform. The method further includes receiving responses for the one or more tasks from the worker computing device, while the worker-computing device is connected to the crowdsourcing platform.

Abstract: The disclosed embodiments illustrate methods and systems for assigning one or more tasks to a labor channel from one or more labor channels. The method includes selecting a first labor channel from said one or more labor channels based at least on a distribution of a performance metric associated with each of said one or more labor channels. The first labor channel is selected for execution of said one or more tasks. The method includes updating said distribution of said performance metric associated with each of said one or more labor channels. The method further includes selecting a second labor channel from said one or more labor channels based at least on a comparison between said updated distribution of said performance metric associated with said first labor channel, and said updated distribution of said performance metric associated with remaining one or more labor channels.

Abstract: The disclosed embodiments illustrate methods and systems for processing crowd-sensed data. The method includes receiving the crowd-sensed data from a mobile device associated with a user. The crowd-sensed data corresponds to metadata of an event pertaining to an aberration in at least one of a public service, a public infrastructure, a private service, or a private infrastructure. Thereafter, the event may be prioritized based at least on a type of the event, a measure of impact of the event, or a measure of urgency to resolve the event. Further, a notification of the event may be transmitted to an organization responsible to at least resolve the event, based on the prioritizing, wherein the notification comprises at least the metadata.

Abstract: The disclosed embodiments illustrate methods and systems for processing crowd-sensed data. The method includes receiving the crowd-sensed data from a mobile device associated with a user. The crowd-sensed data corresponds to metadata of an event pertaining to an aberration in at least one of a public service, a public infrastructure, a private service, or a private infrastructure. Thereafter, the event may be prioritized based at least on a type of the event, a measure of impact of the event, or a measure of urgency to resolve the event. Further, a notification of the event may be transmitted to an organization responsible to at least resolve the event, based on the prioritizing, wherein the notification comprises at least the metadata.

Abstract: The disclosed embodiments illustrate methods and systems for crowdsourcing a task. The method includes extracting one or more components from at least one task based on a task category associated with the at least one task. Each component corresponds to a part of the at least one task, that requires an input from a crowdworker. Further, the one or more components are categorized in a set of first categories, each of which includes similar components, and a second category that includes dissimilar components. The categorization is based on a comparison between content of each of component, and the task category. Thereafter, a representative component from at least one category from the set of first categories, and at least one component from the second category are combined to create the task. The task, so created, is crowdsourced to one or more crowdworkers.

Abstract: The disclosed embodiments illustrate methods and systems for scheduling a batch of tasks on one or more crowdsourcing platforms. The method includes generating one or more forecast models for each of the one or more crowdsourcing platforms based on historical data associated with each of the one or more crowdsourcing platforms and a robustness parameter. Thereafter, for a forecast model, from the one or more forecast models, associated with each of the one or more crowdsourcing platforms, a schedule is generated based on the forecast model and one or more parameters associated with the batch of tasks. Further, the schedule is executed on each of the one or more forecasts models associated with the one or more crowdsourcing platforms to determine a performance score of the schedule on each of the one or more forecast models. Finally, the schedule is recommended to a requestor based on the performance score.