Improved survivability analysis for SONET SHRs

A special type of Markov model called parametric state reward Markov model (SRMM/p) and a set of survivability metrics comprising reliability, availability, and restorability were proposed for the evaluation of self-healing SONET mesh networks. The SRMM/p accommodates multiple consecutive link failures and uses topology-free approximation in order to calculate the average performance loss due to a failure. The SRMM/p is equally applicable to the analysis of self-healing SONET rings by considering a ring as a special case of a mesh topology

PrOntoLearn: Unsupervised Lexico-Semantic Ontology Generation using Probabilistic Methods

Abstract. Formalizing an ontology for a domain manually is well-known as a tedious and cumbersome process. It is constrained by the knowledge acquisition bottleneck. Therefore, researchers developed algorithms and systems that can help to automatize the process. Among them are systems that include text corpora for the acquisition. Our idea is also based on vast amount of text corpora. Here, we provide a novel unsupervised bottom-up ontology generation method. It is based on lexico-semantic structures and Bayesian reasoning to expedite the ontology generation process. We provide a quantitative and two qualitative results illustrating our approach using a high throughput screening assay corpus and two custom text corpora. This process could also provide evidence for domain experts to build ontologies based on top-down approaches