We propose a recursive clustering and order restriction (R-CORE) method for learning large-scale Bayesian networks. The proposed method considers a reduced search space for directed acyclic graph (DAG) structures in scoring-based Bayesian network learning. The candidate DAG structures are restricted by clustering variables and determining the intercluster directionality. The proposed method considers cycles on only cmax(«n) variables rather than on all n variables for DAG structures. The R-CORE method could be a useful tool in very large problems where only a very small amount of training data is available.

In mobile computing environments, cache invalidation techiniques are widely used. However, theses techniques require a large-sized invalidation report and show low cache utilization under high server update rate. In this paper, we propose a new cache-level cache invalidation technique called TTCI (Timestamp Tree-based Cache Invalidation technique) to overcome the above two problems. TTCI also supports selective tuning for a cache-level cache invalidation. We show in our experiment that our technique requires much smaller size of cache invalidation report and improves cache utilization.