Abstract. Crowdsourcing has emerged as an important paradigm in human-problem solving techniques on the Web. One application of crowdsourcing is to outsource certain tasks to the crowd that are difficult to implement as solutions based on software services only. Another benefit of crowdsourcing is the on-demand allocation of a flexible workforce. Businesses may outsource certain tasks to the crowd based on workload variations. The paper addresses the monitoring of crowd members' characteristics and the effective use of monitored data to improve the quality of work. Here we propose the extensions of standards such as Web Service Level Agreement (WSLA) to settle quality guarantees between crowd consumers and the crowdsourcing platform. Based on negotiated agreements, we provide a skill-based crowd scheduling algorithm. We evaluate our approach through simulations and show that our approach clearly outperforms a skill-agnostic scheduling approach.

Abstract-Crowdsourcing in enterprises is a promising approach for organizing a flexible workforce. Recent developments show that the idea gains additional momentum. However, an obstacle for widespread adoption is the lack of an integrated way to execute business processes based on a crowdsourcing platform. The main difference compared to traditional approaches in business process execution is that tasks or activities cannot be directly assigned but are posted to the crowdsourcing platform, while people can choose deliberately which tasks to book and work on. In this paper we propose a framework for adaptive execution of business processes on top of a crowdsourcing platform. Based on historical data gathered by the platform we mine the booking behavior of people based on the nature and incentive of the crowdsourced tasks. Using the learned behavior model we derive an incentive management approach based on mathematical optimization that executes business processes in a cost-optimal way considering their deadlines. We evaluate our approach through simulations to prove the feasibility and effectiveness. The experiments verify our assumptions regarding the necessary ingredients of the approach and show the advantage of taking the booking behavior into account compared to the case when it is partially of fully neglected.

Abstract. Crowdsourcing has emerged as a new paradigm for outsourcing simple for humans yet hard to automate tasks to an undefined network of people. Crowdsourcing platforms like Amazon Mechanical Turk provide scalability and flexibility for customers that need to get manifold similar independent jobs done. However, such platforms do not provide certain guarantees for their services regarding the expected job quality and the time of processing, although such guarantees are advantageous from the perspective of Business Process Management. In this paper, we consider an alternative architecture of a crowdsourcing platform, where the workers are assigned to tasks by the platform according to their availability and skills. We propose the technique for estimating accomplishable guarantees and negotiating Service Level Agreements in such an environment.

Abstract. Companies strive to retain the knowledge about their business processes by modelling them. However, non-routine people-intensive processes, such as distributed collaboration, are hard to model due to their unpredictable nature. Often such processes involve advanced activities, such as discovery of socially coherent teams or unbiased experts, or complex coordination towards reaching a consensus. Modeling such activities requires an expressive formal representation of process context, i.e. related actors and artifacts. Existing modeling approaches do not provide the necessary level of expressiveness to capture it. We therefore propose a novel modeling approach and a graphical notation, demonstrate their applicability and expressivity via several use cases, and discuss their strengths and weaknesses.

Modeling collaboration processes is a challenging task. Existing modeling approaches are not capable of expressing the unpredictable, non-routine nature of human collaboration, which is influenced by the social context of involved collaborators. We propose a modeling approach which considers collaboration processes as the evolution of a network of collaborative documents along with a social network of collaborators. Our modeling approach, accompanied by a graphical notation and formalization, allows to capture the influence of complex social structures formed by collaborators, and therefore facilitates such activities as the discovery of socially coherent teams, social hubs, or unbiased experts. We demonstrate the applicability and expressiveness of our approach and notation, and discuss their strengths and weaknesses.