Publication date: 07/08/2018

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Summary: Crowdsourcing is an approach that employs people to process input data to solve a computationally complex problem, such as generating a large dataset of annotated images, audio transcriptions or video scene descriptions. In this approach, people select tasks and produce individual results according to a list of steps that leads to an efficient solution. Then, every single result must be collected, interpreted, and integrated by a platform or system supporting the crowdsourcing process. This MSc dissertation starts with a state-of-the-art discussion, which provides an understanding of the main concepts and relationships reported in crowdsourcing projects found in the literature. By conducting a systematic review of crowdsourcing projects, we understand how these projects are designed and executed in the state-of-the-art, considering the following dimensions: Task execution, quality management, and platform usage. Our results summarized trends of the important aspects of a crowdsourcing project, such as crowd and task types, crowdsourcing platforms, and activities used to manage the quality; we also addressed functions and limitations in traditional crowdsourcing platforms, the definition of a crowdsourcing workflow, and the lack of standardization when designing a crowdsourcing project. In sequence, we developed a detailed conceptual model of crowdsourcing projects, specifying the essential entities and their relationships, based on the concepts leveraged in the accomplished systematic review. This work shows a class diagram that represents a general view of crowdsourcing projects, alongside with the concept of using an activity diagram to describe the execution of a specific project workflow, a neglected concept in previous works. To illustrate our contributions, the conceptual model is applied in some real crowdsourcing projects related to image annotation and segmentation data at scale, image QoE subjective assessments, software development, and cascading crowdsourcing to achieve complex video annotations.

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