Many applications today need to manage large data sets with uncertainties. In this paper we describe the foundations of managing data where the uncertainties are quantified as pro...
Efficient learnability using the state merging algorithm is known for a subclass of probabilistic automata termed µ-distinguishable. In this paper, we prove that state merging alg...
Omri Guttman, S. V. N. Vishwanathan, Robert C. Wil...
Probabilistic Choice Operators (PCOs) are convenient tools to model uncertainty in CP. They are useful to implement randomized algorithms and stochastic processes in the concurrent...
— The sensing capabilities of networked sensors are affected by environmental factors in real deployment and it is imperative to have practical considerations at the design stage...
This paper presents a scalable leader election protocol for large process groups with a weak membership requirement. The underlying network is assumed to be unreliable but characte...
Indranil Gupta, Robbert van Renesse, Kenneth P. Bi...