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M. Riveni, M. Baeth, M. Aktas, S. Dustdar:
"Provenance in Social Computing: A Case Study";
Vortrag: 13th International Conference on Semantics, Knowledge and Grids, SKG 2017, Beijing, China; 14.08.2017 - 15.08.2017; in: "Proceedings of the 13th International Conference on Semantics, Knowledge and Grids, SKG 2017", H. Zhuge, X. Sun (Hrg.); IEEE, (2017), ISBN: 978-1-5386-2558-3; S. 77 - 84.

Complex systems such as Collective Adaptive Systems that include a variety of resources, are increasingly being designed to include people in task-execution, and so social computing is not a stand-alone paradigm only, but it is increasingly researched within mixed-resource systems. The Social Computing paradigm has led to significant advancements in engaging people as resources and/services in solving tasks that can not yet be solved by software. Collectives, encapsulating human resources/services, represent one type of an application of social computing, within which people with different type of skills can be engaged to solve one common problem or work on the same project. Mechanisms of managing social collectives are dependent on functional and non-functional parameters of members of social collectives. In this work, we investigate and show experimental results of how provenance data related to those parameters can help better visualize and extract interaction and performance patterns during a collective's run-time.

http://dx.doi.org/10.1109/SKG.2017.00021