Breakthroughs in molecular profiling technologies are enabling a new data-intensive approach to biomedical research, with the potential to revolutionize how we study, manage, and treat complex diseases. The next great challenge for clinical applications of these innovations will be to create scalable computational solutions for intelligently linking complex biomedical patient data to clinically actionable knowledge. Traditional database management systems (DBMS) are not well suited to representing complex syntactic and semantic relationships in unstructured biomedical information, introducing barriers to realizing such solutions. We propose a scalable computational framework for addressing this need, which leverages a hypergraph-based data model and query language that may be better suited for representing complex multi-lateral, multi-scalar, and multi-dimensional relationships. We also discuss how this framework can be used to create rapid learning knowledge base systems to intelligently capture and relate complex patient data to biomedical knowledge in order to automate the recovery of clinically actionable information.

As the Internet'sde factointerdomain routing protocol, the Border Gateway Protocol (BGP) is the glue that holds the disparate parts of the Internet together. A major limitation of BGP is its failure to adequately address security. Recent high-profile outages and security analyses clearly indicate that the Internet routing infrastructure is highly vulnerable. Moreover, the design of BGP and the ubiquity of its deployment have frustrated past efforts at securing interdomain routing. This paper considers the current vulnerabilities of the interdomain routing system and surveys both research and standardization efforts relating to BGP security. We explore the limitations and advantages of proposed security extensions to BGP, and explain why no solution has yet struck an adequate balance between comprehensive security and deployment cost.

Abstract Network resilience measures the average two‐terminal reliability (connectedness) of a network. Multiterminal resilience extends this measure to any k vertices; it is the average k‐terminal reliability of a network. This generalizes two well‐studied network connectedness measures. Calculating multiterminal resilience on general networks encompasses all‐terminal reliability and thus is NP‐hard. Multiterminal resilience is examined on undirected series‐parallel networks, and an efficient (polynomial time) algorithm is developed for calculating the resilience for every k. Applications in mobile ad hoc and sensor networks are outlined. © 2007 Wiley Periodicals, Inc. NETWORKS, Vol. 50(2), 164–172 2007