ABSTRACT „In times of perpetual change and tight competition, scientific communities and researchers have an increased need to continually assess, reflect, forecast, and roadmap their actions and positions within the R&D landscape. In this talk I will present recent work and successes in building and deploying big data analytics infrastructures for hosting and integrating flexible toolkits for visual analytics, roadmapping, and large-scale sharing for communities in technology enhanced learning to address those needs.“
The Advanced Community Information Systems (ACIS) group supports professional communities with heterogeneous needs by open and responsive community environments. Results of our research are advancing the emerging Web Science discipline by new analysis and engineering methodologies for large-scale and heterogeneous information systems.
Analytics to tackle information overflow Pluggable, packable for scaling, privacy, etc. Openness to support sust, business model, long tail issues Scaling designs and technology RTC for modern collaboration
“Analytics as a Service combines the on-demand aspects of cloud computing with the democratization of information enabled by big data analytics.”
1 programme: 80% 2 programmes: 13% 3 programmes: 4% 4 programmes: 2% All programmes: Ellinogermaniki Agogi, UNED, U Duisburg-Essen, U Wien
FP7: ICT-2007.4.1 and .3 ICT-2009.4.2 ICT-2011.8.1 ICT-2013.8.2 FP6: IST-2002-184.108.40.206 IST-2004-2.4.10 and .13 eTEN: All calls in theme “eLearning” (aka “Learning and Culture) eContentplus Projects in “Educational Content” from Calls 2005-2008
select programme, count(*) from projectspace_projects group by programme
select o.name, count(distinct(p.programme)) from projectspace_projects p, projectspace_project_participants po, projectspace_participants o where po.participant_id = o.id and p.is_tel=1 and p.id = po.project_id group by o.name order by 2 desc, 1 asc
# orgs: select count(distinct(po.participant_id)) from projectspace_projects p, projectspace_project_participants po where p.is_tel=1 and p.id = po.project_id
The intuitions behind latent Dirichlet allocation. We assume that some number of “topics,” which are distributions over words, exist for the whole collection (far left). Each document is assumed to be generated as follows. First choose a distribution over the topics (the histogram at right); then, for each word, choose a topic assignment (the colored coins) and choose the word from the corresponding topic. The topics and topic assignments in this figure are illustrative—they are not fit from real data. See Figure 2 for topics fit from data.
Ensure that peer production is unlocked: Barriers to participation need to be lowered, massive reuse of existing materials has to be realized, and experiences people make in physical contexts needs to be included. The trade-off between collaboration, openness and sharing on the one hand and competition and securing for competitive advantage, on the other, needs to be addressed. Ensure individuals receive scaffolds to deal with the growing abundance: We need to research concepts of networked scaffolding and research the effectiveness of scaffolds across different contexts. Ensure shared meaning of work practices at individual, organisational and interorganisational levels emerges from these interactions: We need to lower barriers for participation, allow emergence as a social negotiation process and knowledge maturing across institutional boundaries, and research the role of physical artefacts and context in this process.