Resolution-Aware query answering for business intelligence
Yannis Sismanis, Ling Wang, et al.
ICDE 2009
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.
Yannis Sismanis, Ling Wang, et al.
ICDE 2009
Peter J. Haas
Communications in Statistics. Part C: Stochastic Models
Ihab Ilyas, Volker Markl, et al.
ICAC 2004
Peter J. Haas, Gerald S. Shedler
Communications in Statistics. Stochastic Models