Optimizing SPARQL Query Processing On Dynamic and Static Data Based on Query Time/Freshness Requirements Using Materialization
Predicting Multiple Metrics for Queries: Better Decision Enabled by Machine Learning
Approximate Continuous Query Answering Over Streams and Dynamic Linked Data Sets
addressing tim/quality trade-off in view maintenance
lightweight graphical models for selectivity estimation without independance assumption
Performance comparison between Linux Containers and Virtual Machines
