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F. Skopik, D. Schall, S. Dustdar:
"Discovering and Managing Social Compositions in Collaborative Enterprise Crowdsourcing Systems";
International Journal of Cooperative Information Systems, Volume 21 (2012), No. 4; 297 - 341.

Crowdsourcing is an increasingly used model to outsource certain tasks to be carried out by external experts on the Web. Especially when lacking experience or expertise with certain task types, crowdsourcing offers a convenient way to receive instant support. In this paper, we introduce an in-house enterprise crowdsourcing model, which leverages the crowdsourcing concept and transfers it to traditional organizations. Here, a company's staff is considered a crowd that - besides its regularly assigned tasks - can also receive tasks from colleagues from other departments and across hierarchical structures. The aim is to offer instant support and utilize free capacities throughout a large organization more efficiently. In our work, we describe this concept and supporting mechanisms in context of an agile software development use case. However, in contrast to usually crowdsourced microtasks, complex software architectures usually consist of tens and hundreds of connected modules that can be potentially crowdsourced. These technical dependencies between modules require active coordination and interactions between crowd members that process the single artifacts. Hence, technical dependencies of artifacts result in social dependencies of collaborating crowd members that create them. In order to efficiently discover member compositions based on artifact dependencies, we introduce an indexing and discovery approach based on subgraph matching. Typically, assigning tasks to well-rehearsed teams results in more reliable task processing, faster results, and higher quality of work. We evaluate our approach in terms of system scalability and overall applicability by mining and analyzing the popular SourceForge community. We show that our approach of member composition discovery is feasibly in terms of scalability and quality of discovery results. Our findings deliver important input for the design and implementation of supporting information systems for future large-scale collaboration platforms.

SOA-based collaboration; interaction mining; enterprise 2.0; crowdsourcing; social networks; social composition discovery; subgraph matching.

http://dx.doi.org/10.1142/S0218843012500050

Project Head Schahram Dustdar:
COIN

Project Head Schahram Dustdar:
S-Cube