Abstract
Crowdsourcing (CS) is the outsourcing of a unit of work to a crowd of people via an open call for contributions. Thanks to the availability of online CS platforms, such as Amazon Mechanical Turk or CrowdFlower, the practice has experienced a tremendous growth over the past few years and demonstrated its viability in a variety of fields, such as data collection and analysis or human computation. Yet it is also increasingly struggling with the inherent limitations of these platforms: each platform has its own logic of how to crowdsource work (e.g., marketplace or contest), there is only very little support for structured work (work that requires the coordination of multiple tasks), and it is hard to integrate crowdsourced tasks into state-of-the-art business process management (BPM) or information systems. We attack these three shortcomings by (1) developing a flexible CS platform (we call it Crowd Computer , or CC) that allows one to program custom CS logics for individual and structured tasks, (2) devising a BPMN--based modeling language that allows one to program CC intuitively, (3) equipping the language with a dedicated visual editor, and (4) implementing CC on top of standard BPM technology that can easily be integrated into existing software and processes. We demonstrate the effectiveness of the approach with a case study on the crowd-based mining of mashup model patterns.
Highlights
Crowdsourcing (CS) is a relatively new approach to execute work that requires human capabilities. Howe [2008], who coined the term, defines crowdsourcing generically as “the act of taking a job traditionally performed by a designated agent and outsourcing it to an undefined, generally large group of people in the form of an open call.” In principle, work could be outsourced in a variety of ways, such as by temporarily recruiting volunteers to help complete a given job or by distributing questionnaires that people fill out voluntarily
The rationale of this choice is that business process model and notation (BPMN) already satisfies some of the requirements outlined earlier: it supports human tasks (R4) and machine tasks (R5), and control flow (R6) and dataflow (R7) constructs, and its native extensibility allows us to implement custom task types for CS tasks (R1) and data management operations (R8), which are not supported by the language
We allow the crowdsourcer to develop his own tactics and logics, but we provide a set of predefined tactics and logics that can be reused via suitable configurations of the CS processes
Summary
Crowdsourcing (CS) is a relatively new approach to execute work that requires human capabilities. Howe [2008], who coined the term, defines crowdsourcing generically as “the act of taking a job traditionally performed by a designated agent (usually an employee) and outsourcing it to an undefined, generally large group of people in the form of an open call.” In principle, work could be outsourced in a variety of ways, such as by temporarily recruiting volunteers to help complete a given job or by distributing questionnaires that people fill out voluntarily. Processes to be deployed on top of existing crowdsourcing platforms, typically Mechnical Turk They suffer from the inherent limitations described earlier and are only hardly integrable with legacy systems (e.g., to enable the inclusion of crowdsourced tasks into common business processes or applications). These limitations put the healthy growth and further spreading of CS at risk, in that they load the crowdsourcer with unnecessary coordination overhead, prevent scalability, and lower flexibility, as well as threaten cost and time savings if more complex, structured work is to be crowdsourced.
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