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 creating a simulator for a crowdsourcing platform. The method includes generating a plurality of rules indicative of at least one of a behavior or an interaction, of one or more entities associated with the crowdsourcing platform, based on one or more parameters associated with each of the one or more entities. Thereafter, a first level of service of the crowdsourcing platform is estimated based on the generated plurality of rules. Further, the plurality of rules are modified based on the first level of service and an observed level of service of the crowdsourcing platform. The plurality of rules are modified such that a second level of service of the crowdsourcing platform, estimated based on the modified plurality of rules, approaches the observed level of service of the crowdsourcing platform. The modified plurality of rules corresponds to the simulator for the crowdsourcing platform.
Abstract:
A method of administering mobile crowdsourcing includes: receiving a plurality of requests from requestors to fulfill tasks at specified task locations; receiving an itinerary from a worker, the itinerary defining a journey the worker plans to take and including at least a starting point of the journey and an end point of the journey; automatically generating, based on the received itinerary, at least one parameterized, personlized action plan, the plan identifying a subset of the tasks for which requests had been received; and providing the plan to the worker.
Abstract:
According to embodiments illustrated herein there is provided a method for assigning one or more tasks to one or more workers. The method includes creating a graph representative of an acquaintance between the one or more workers based on at least a previous collaboration among said one or more workers in attempting at least previous tasks. Further, at least one set of workers are identified from the one or more workers based on the graph. The workers in said at least one set of workers are unacquainted with each other. Thereafter, one or more sub-tasks in a task are assigned to the workers in at least one set of workers such that the task assigned to the at least one set of workers is different from tasks assigned to other sets of workers.
Abstract:
The disclosed embodiments illustrate methods and systems for crowdsourcing a task. The method includes presenting one or more advertisements to a worker along with said task. The one or more advertisements are determined based on at least a type of said task being attempted by said worker, one or more tasks previously attempted by said worker, and a profile of said worker. Thereafter, an incentive to said worker is provided based on said presentation of said one or more advertisements, in addition to providing a remuneration for attempting said task.
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.