Probabilistic Process Model Research Articles

Model independent approach to probabilistic models

There is a lot of research on probabilistic transition systems. There are not many studies in probabilistic process models. The lack of investigation into the interactive aspect of probabilistic processes is mainly due to the difficulty caused by the discrepancy between probabilistic choices and nondeterministic actions. The paper proposes a uniform approach to probabilistic process models and a bisimulation theory for probabilistic concurrency.

ДІАГНОСТУВАННЯ СУДНОВОГО УСТАТКУВАННЯ ПРИ ВИКОРИСТАННІ РІЗНИХ МОДЕЛЕЙ ВІБРАЦІЇ

Розглянуті моделі діагностики суднового устаткування та доцільність їх використання. Найбільш ефективними методами діагностування суднових машин та механізмів є віброакустичні методи, які використовують ту або іншу модель сигналу вібрації. Проаналізовано конкретні моделі вібрації в якості вібродіагностичних моделей. Проведено аналіз властивостей розглянутих моделей випадкових процесів, що характеризують різні процеси в теорії і при проведенні експериментальних досліджень. На підставі експериментальних досліджень знайшли підтвердження різні моделі, які дозволяють розробити і отримати практичні методи діагностування.

Optimizing the configuration for the supply network of industrial enterprises based on the probabilistic process model

Optimizing the configuration for the supply network of industrial enterprises based on the probabilistic process model

A markov prediction model for data-driven semi-structured business processes

In semi-structured case-oriented business processes, the sequence of process steps is determined by case workers based on available document content associated with a case. Transitions between process execution steps are therefore case specific and depend on independent judgment of case workers. In this paper, we propose an instance-specific probabilistic process model (PPM) whose transition probabilities are customized to the semi-structured business process instance it represents. An instance-specific PPM serves as a powerful representation to predict the likelihood of different outcomes. We also show that certain instance-specific PPMs can be transformed into a Markov chain under some non-restrictive assumptions. For instance-specific PPMs that contain parallel execution of tasks, we provide an algorithm to map them to an extended space Markov chain. This way existing Markov techniques can be leveraged to make predictions about the likelihood of executing future tasks. Predictions provided by our technique could generate early alerts for case workers about the likelihood of important or undesired outcomes in an executing case instance. We have implemented and validated our approach on a simulated automobile insurance claims handling semi-structured business process. Results indicate that an instance-specific PPM provides more accurate predictions than other methods such as conditional probability. We also show that as more document data become available, the prediction accuracy of an instance-specific PPM increases.

Non-expansive [formula omitted]-bisimulations for probabilistic processes

ϵ - bisimulation equivalence has been proposed in the literature as a technique to study the concept of behavioral distance between probabilistic processes. In this paper we first consider the generative model of probabilistic processes and introduce two stronger equivalence notions: action ϵ -bisimulation and global ϵ -bisimulation. For each of these three equivalence notions we propose an SOS transition rule format ensuring the property of non-expansiveness. Non-expansiveness means that if the behavioral distance between s i and t i is ϵ i , then the behavioral distance between f ( s 1 , … , s n ) and f ( t 1 , … , t n ) is no more that ϵ 1 + ⋯ + ϵ n . As expected, the stronger the ϵ -bisimulation considered, the weaker the constraints of the transition rule format. Then, we switch to the reactive model of probabilistic processes and we propose a rule format for ϵ -bisimulation and action ϵ -bisimulation, arguing that global ϵ -bisimulation is not needed in such a context.

Probabilistic bisimulation as a congruence

We propose both an SOS transition rule format for the generative model of probabilistic processes, and an SOS transition rule format for the reactive model of the probabilistic processes. Our rule formats guarantee that probabilistic bisimulation is a congruence with respect to process algebra operations. Moreover, our rule format for generative process algebras guarantees that the probability of the moves of a given process, if there are any, sum up to 1, and the rule format for reactive process algebras guarantees that the probability of the moves of a given process labeled with the same action, if there are any, sum up to 1. We show that most operations of the probabilistic process algebras studied in the literature are captured by our formats, which, therefore, have practical applications.

Dynamic Task-Level Voltage Scheduling Optimizations

Energy versus delay trade-offs are explored for systems that must manage energy expenditure as well as computation deadlines. The focus is execution of a single process on a single processor. Two probabilistic process models are considered, along with a family of power dissipation models. The first process model assumes that process complexity is exactly c cycles with probability p(c). The second model considers the detailed branching and loop structure of the code. Probabilities are attached at branch points. The power models assume that energy dissipation per cycle is proportional to v/sup m/ and that execution time for a cycle is proportional to v/sup -n/, where v is supply voltage. The energy versus delay trade-off is implemented using dynamic voltage and clock adjustments. The problems solved include 1) minimize expected execution time given a hard energy budget and 2) minimize expected energy expenditure given a hard deadline. The problem of minimizing the expected value of Q(E,T), where Q is a penalty function and E and T are, respectively, total energy and total time, is also solved using the first process model. Analysis determines theoretical conditions where it may be profitable to switch voltage or modify an a priori voltage schedule.

Additive models of probabilistic processes

We propose a new model of probabilistic processes. In this model, a probability is assigned to the action of a prefix and a probability distribution is assigned to the components of a parallel composition. In addition, the probability of a transition of a probabilistic summation is evaluated as the sum of the probabilities of the same transition of summands multiplied by the probabilities associated to them in the summation. The concepts of strong bisimulation degree and (weak) bisimulation degree are introduced. These notions provide us with continuous spectra of strong bisimilarities, (weak) bisimilarities and observation congruences which equate probabilistic processes with different degrees of belief. Various equational laws of probabilistic processes with respect to these equivalence relations are presented and substitutivities of these equivalence relations under various combinators are established.

Using multivariate nested distributions to model semiconductor manufacturing processes

This paper demonstrates the advantages of modeling semiconductor process variability using a multivariate nested distribution. This distribution allows estimation not only of correlation among various model parameters, but also allows each of those variations to he apportioned among the various stages of the process (i.e., wafer-to-wafer, lot-to-lot, etc.). This permits matched devices to be more accurately simulated, without having to develop customized models for each configuration of matching. The technique also provides focus for process improvement efforts into those areas with the maximum potential reward. Test structures have been designed and fabricated to facilitate extraction of the parameters for the multivariate nested distribution. Using data from a sample of these structures, a process model is built and analyzed; Monte Carlo techniques are then employed using SPICE and a probabilistic process model to predict the performance of a multiplying digital-to-analog converter (MDAC), and the results are compared to measured data from fabricated circuits. Simulations performed using a model built using the multivariate nested approach are shown to provide superior results when compared to simulations using currently accepted multivariate normal models.

Verifying automata specification of distributed probabilistic real-time systems

In this paper, a qualitative model checking algorithm for verification of distributed probabilistic real-time systems (DPRS) is presented. The model of DPRS, called real-time probabilistic process model (RPPM), is over continuous time domain. The properties of DPRS are described by using deterministic timed automata (DTA). The key part in the algorithm is to map continuous time to finite time intervals with flag variables. Compared with the existing algorithms, this algorithm uses more general delay time equivalence classes instead of the unit delay time equivalence classes restricted by event sequence, and avoids generating the equivalence classes of states only due to the passage of time. The result shows that this algorithm is cheaper.

Pull-Driven Scheduling for Pipe-Spool Installation: Simulation of Lean Construction Technique

Many construction processes include installation of unique materials in specific locations in the facility being built: materials and locations must match before installation can take place. Mismatches due to delay and uncertainty in supplying materials or completing prerequisite work at those locations hamper field productivity. This is illustrated here using a model of a materials-management process with a matching problem that typifies fast-track process-plant projects. The uniqueness of materials and locations combined with the unpredictability in duration and variation in execution quality of various steps in the supply chain allow for different ways to sequence material delivery and work area completion. Several alternatives are described. Their impact on process execution is illustrated by means of probabilistic process models. One model reflects total lack of coordination between delivery and work area completion prior to the start of construction; a second one describes perfect coordination. The corresponding materials staging buffers and construction progress are plotted based on output from discrete-event simulation models. A third probabilistic model then illustrates the use of the lean construction technique called pull-driven scheduling. Real-time feedback regarding the status of progress on site is provided to the fabricator off site so process steps can be re-sequenced opportunistically. This yields smaller buffers and earlier project completion and, when properly accounted for, increased productivity.

Priority as extremal probability

Abstract We extend the stratified model of probabilistic processes to obtain a very general notion of process priority. The main idea is to allow probability guards of value 0 to be associated with alternatives of a probabilistic summation expression. Such alternatives can be chosen only if the non-zero alternatives are precluded by contextual constraints. We refer to this model as one of “extremal probability” and to its signature as PCCS ζ . We provide PCCS ζ with a structural operational semantics and a notion of probabilistic bisimulation , which is shown to be a congruence. Of particular interest is the abstraction PCCS π of PCCS ζ in which all non-zero probability guards are identified. PCCS π represents a customized framework for reasoning about priority, and covers all features of process algebras proposed for reasoning about priority that we know of.