We present a conceptual framework for socio-technical affordances for stigmergic coordination, that is, coordination supported by a shared work product. Based on research on free/libre open source software development, we theorize that stigmergic coordination depends on three sets of socio-technical affordances: the visibility and combinability of the work, along with defined genres of work contributions. As a demonstration of the utility of the developed framework, we use it as the basis for the design and implementation of a system, MIDST, that supports these affordances and that we thus expect to support stigmergic coordination. We describe an initial assessment of the impact of the tool on the work of project teams of three to six data-science students that suggests that the tool was useful but also in need of further development. We conclude with plans for future research and an assessment of theory-driven system design.
In this study, we were interested in studying which characteristics of virtual teams are good predictors for the quality of their production. The experiment involved obtaining the Spanish Wikipedia database dump and applying different data mining techniques suitable for large data sets to label the whole set of articles according to their quality (comparing them with the Featured/Good Articles, or FA/GA). Then we created the attributes that describe the characteristics of the team who produced the articles and using decision tree methods, we obtained the most relevant characteristics of the teams that produced FA/GA. The team's maximum efficiency and the total length of contribution are the most important predictors. This article contributes to the literature on virtual team organization.
Detecting overlapping groups is an important challenge in clustering offering relevant solutions for many applications domains. Recently, Parametrized R-OKM method was defined as an extension of OKM to control overlapping boundaries between clusters. However, the performance of both, OKM and Parametrized R-OKM is considerably reduced when data contain outliers. The presence of outliers affects the resulting clusters and yields to clusters which do not fit the true structure of data. In order to improve the existing methods, we propose a robust method able to detect relevant overlapping clusters with outliers identification. Experiments performed on artificial and real multi-labeled data sets showed the effectiveness of the proposed method to produce relevant non disjoint groups.
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