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

From: Actionable Knowledge As A Service (AKAAS): Leveraging big data analytics in cloud computing environments

Knowledge Processes K-Analytics Benefits
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
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