Temporally-biased sampling for online model management
Brian Hentschel, Peter J. Haas, et al.
EDBT 2018
Generalized semi-Markov processes and stochastic Petri nets provide building blocks for specification of discrete event system simulations on a finite or countable state space. The two formal systems differ, however, in the event scheduling (clock-setting) mechanism, the state transition mechanism, and the form of the state space. We have shown previously that stochastic Petri nets have at least the modeling power of generalized semi-Markov processes. In this paper we show that stochastic Petri nets and generalized semi-Markov processes, in fact, have the same modeling power. Combining this result with known results for generalized semi-Markov processes, we also obtain conditions for time-average convergence and convergence in distribution along with a central limit theorem for the marking process of a stochastic Petri net. © 1991, Cambridge University Press. All rights reserved.
Brian Hentschel, Peter J. Haas, et al.
EDBT 2018
Peter J. Haas
WSC 2004
Wenlei Xie, Yuanyuan Tian, et al.
ICDE 2015
Ihab Ilyas, Volker Markl, et al.
ICAC 2004