Abstract

Abstract. We introduce the problem of Model Repair for Probabilistic Systems as follows. Given a probabilistic system M and a probabilistic temporal logic formula φ such that M fails to satisfy φ, the Model Repair problem is to find an M that satisfies φ and differs from M only in the transition flows of those states in M that are deemed controllable. Moreover, the cost associated with modifying M 's transition flows to obtain M should be minimized. Using a new version of parametric probabilistic model checking, we show how the Model Repair problem can be reduced to a nonlinear optimization problem with a minimal-cost objective function, thereby yielding a solution technique. We demonstrate the practical utility of our approach by applying it to a number of significant case studies, including a DTMC reward model of the Zeroconf protocol for assigning IP addresses, and a CTMC model of the highly publicized Kaminsky DNS cache-poisoning attack.

Abstract

WC propose Concurrent Transaction Logic (C7X) as the language for specifying, analyzing, and scheduling of workflows. We show that both local and global properties of worktlows can be naturally represented as C7X formulas and reasoning can be done with the use of the proof theory and the semantics of this logic, We describe a transformation that leads to an eilicicnt algorithm for scheduling worldlows in the presencc of global temporal constraints, which leads to decision proccdurcs for dealing with several safety related properties such as whether every valid execution of the workflow satisfits a particular property or whether a worlcfiow execution is consistent with some given global constraints on the ordering of events in a workflow. We also provide tight complexity results on the running times of these algorithms.

Abstract. The focus of contemporary Web information retrieval systems has been to provide efficient support for the querying and retrieval of relevant documents. More recently, information retrieval over semantic metadata extracted from the Web has received an increasing amount of interest in both industry and academia. In particular, discovering complex and meaningful relationships among this metadata is an interesting and challenging research topic. Just as ranking of documents is a critical component of today's search engines, the ranking of complex relationships will be an important component in tomorrow's Semantic Web analytics engines. Building upon our recent work on specifying and discovering complex relationships in RDF data, called Semantic Associations, we present a flexible ranking approach which can be used to identify more interesting and relevant relationships in the Semantic Web. Additionally, we demonstrate our ranking scheme's effectiveness through an empirical evaluation over a real-world dataset.