Table 1 An illustration of a possible application of AKAAS and Knowledge Analytics in the context of cloud customers

Capturing/Creating Knowledge

□ Search query/search logs data analysis (text mining & clustering)

□ Identification of knowledge needs and services requirements

□ What has been created? for which audience?

□ Mapping knowledge coverage: dashboarding of available knowledge (main domains & associated targets)

□ Knowledge created vs. knowledge searched and retrieved

□ Value delivered analysis (offer vs. demand bells)

Broadcasting/Sharing Knowledge

□ Measuring entry and exit points on multiple channels

□ Providing real-time knowledge delivery through relevant channels

□ Click-through rates & interactions

□ Assessing the accessibility of content and potential issues to be addressed (keywords, tagging, etc.…)

□ Curating knowledge

Reusing/

Refining Knowledge

□ Search query data (text mining analytics)

□ Discovery of consumption patterns & interaction with content

□ Interestingness/ Consumed contents trends visualization (demand focus)

□ Predicting future knowledge to be created

□ Indicators of quality of the results (ratings, numbers of sharing, recommendations, etc.…)

□ Identifying and solving potential issues with the knowledge offer (duplicates, overlaps, gaps, etc.…)

□ Enrichment or modification requests

□ Identify axis to improve authoring guidelines and review processes