This paper is an extended version of a paper that appeared in the proceedings of the IEEE Real-Time Systems Symposium 2009. This paper has been updated with respect to advances ma...
In this paper we present the Dynamic Grow-Shrink Inference-based Markov network learning algorithm (abbreviated DGSIMN), which improves on GSIMN, the state-ofthe-art algorithm for...
Despite serious research efforts, automatic ontology matching still suffers from severe problems with respect to the quality of matching results. Existing matching systems trade-of...
Kai Eckert, Christian Meilicke, Heiner Stuckenschm...
Multiple applications that execute concurrently on heterogeneous platforms compete for CPU and network resources. In this paper, we consider the problem of scheduling applications ...
Olivier Beaumont, Larry Carter, Jeanne Ferrante, A...
Abstract. Automata-based decision procedures commonly achieve optimal complexity bounds. However, in practice, they are often outperformed by sub-optimal (but more local-search bas...