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

It's getting crowded! : improving the effectiveness of microtask crowdsourcing

[no abstract

Assessing the Impacts of Crowdsourcing in Logistics and Supply Chain Operations

Crowdsourcing models, whereby firms start to delegate supply chain operations activities to a mass of actors in the marketplace, have grown drastically in recent years. 85% of the top global brands have reported to use crowdsourcing in the last ten year with top names such as Procter & Gamble, Unilever, and Nestle. These emergent business models, however, have remained unexplored in extant SCM literature. Drawing on various theoretical underpinnings, this dissertation aims to investigate and develop a holistic understanding of the importance and impacts of crowdsourcing in SCM from multiple perspectives. Three individual studies implementing a range of methodological approaches (archival data, netnography, and field and scenario-based experiments) are conducted to examine potential impacts of crowdsourcing in different supply chain processes from the customer’s, the crowdsourcing firm’s, and the supply chain partner’s perspectives. Essay 1 employs a mixed method approach to investigate “how, when, and why” crowdsourced delivery may affect customer satisfaction and behavioral intention in online retailing. Essay 2 uses a field experiment to address how the framing of motivation messages could enhance crowdsourced agents’ participation and performance level in crowdsourced inventory audit tasks. Lastly, Essay 3 explores the impact of crowdsourcing activities by the manufacturers on the relationship dynamics within the manufacturer-consumers-retailer triads