4. User Centric Activity Data Activity analysis and interaction patterns for individual users Consolidation Integration Interpretation Logs 2 Logs 4 Logs 1 Logs 3 Website 2 Website 4 Website 1 Website 3 Organisation Users
5. What is needed Challenges: Integration of activity data from various logs Consolidation beyond “number of visits” Interpretation of activity data in a user centric, activity oriented Ontologies: Formal, machine processable conceptual models of a domain Allow for integration of heterogeneous data As semantic models of the data, can be used to reason upon the data
6. Hypothesis Taking a user centric point of view can allow different types of analysis of logs/activity data, which are valuable to the organisation and the user Ontologies and Ontology-based reasoning can support the integration, consolidation and interpretation of activity data from multiple sources
7. Overview Website Activity See next slide. Project to include tools on editing ontologies for AD Trace Actor Inference Semantic Store/Engine Based on Using OWLIM RDF Representations of traces Extracting log formats (apache, piwik, etc.) into RDF Plugin 2 Plugin 4 Plugin 1 Plugin 3 Logs 2 Logs 4 Logs 1 Use case: Open University websites and research project websites Logs 3 Website 2 Website 4 Website 1 Website 3