Engineering Crowdsourced Stream Processing Systems

A crowdsourced stream processing system (CSP) is a system that incorporates crowdsourced tasks in the processing of a data stream. This can be seen as enabling crowdsourcing work to be applied on a sample of large-scale data at high speed, or equivalently, enabling stream processing to employ human intelligence. It also leads to a substantial expansion of the capabilities of data processing systems. Engineering a CSP system requires the combination of human and machine computation elements. From a general systems theory perspective, this means taking into account inherited as well as emerging properties from both these elements. In this paper, we position CSP systems within a broader taxonomy, outline a series of design principles and evaluation metrics, present an extensible framework for their design, and describe several design patterns. We showcase the capabilities of CSP systems by performing a case study that applies our proposed framework to the design and analysis of a real system (AIDR) that classifies social media messages during time-critical crisis events. Results show that compared to a pure stream processing system, AIDR can achieve a higher data classification accuracy, while compared to a pure crowdsourcing solution, the system makes better use of human workers by requiring much less manual work effort

Cheaper and Better: Selecting Good Workers for Crowdsourcing

Crowdsourcing provides a popular paradigm for data collection at scale. We study the problem of selecting subsets of workers from a given worker pool to maximize the accuracy under a budget constraint. One natural question is whether we should hire as many workers as the budget allows, or restrict on a small number of top-quality workers. By theoretically analyzing the error rate of a typical setting in crowdsourcing, we frame the worker selection problem into a combinatorial optimization problem and propose an algorithm to solve it efficiently. Empirical results on both simulated and real-world datasets show that our algorithm is able to select a small number of high-quality workers, and performs as good as, sometimes even better than, the much larger crowds as the budget allows

An evaluation methodology for crowdsourced design

In recent years, the “power of the crowd” has been repeatedly demonstrated and various Internet platforms have been used to support applications of collaborative intelligence to tasks ranging from open innovation to image analysis. However, crowdsourcing applications in the fields of design research and creative innovation have been much slower to emerge. So, although there have been reports of systems and researchers using Internet crowdsourcing to carry out generative design, there are still many gaps in knowledge about the capability and limitations of the technology. Indeed the process models developed to support traditional commercial design (e.g. Pugh’s Total Design, Agile, Double-Diamond etc.) have yet to be established for Crowdsourced Design. As a contribution to the development of such a general model this paper proposes the cDesign framework to support the creation of Crowdsourced Design activities. Within the cDesign framework the effective evaluation of design quality is identified as a key component that not only enables the leveraging of a large, virtual workforces’ creative activities but is also fundamental to most iterative and optimisation processes. This paper reports an experimental investigation (developed using the cDesign framework) into two different Crowdsourced design evaluation approaches; free evaluation and ‘crowdsourced Design Evaluation Criteria’ (cDEC). The results are benchmarked against an evaluation carried out by a panel of experienced designers. The results suggest that the cDEC approach produces design rankings that correlate strongly with the judgements of an “expert panel”. The paper concludes that cDEC assessment methodology demonstrates how Crowdsourcing can be effectively used to evaluate, as well as generate, new design solutions