Robabilistic Model Checking News Today : Breaking News, Live Updates & Top Stories | Vimarsana

Stay updated with breaking news from Robabilistic model checking. Get real-time updates on events, politics, business, and more. Visit us for reliable news and exclusive interviews.

Top News In Robabilistic Model Checking Today - Breaking & Trending Today

"Integrating Process Mining with Probabilistic Model Checking via Conti" by Fawad Ali Mangi, Guoxin Su et al.

Process mining represents a methodological approach that facilitates the in-depth analysis of business operations with the aim of revealing significant insights pertaining to their efficacy, efficiency, and regulatory compliance. In a seamless business setting, it's essential to evaluate, refine, and confirm these process models. Traditional methods for checking these models excel in validating their accuracy but cannot handle the inherent probabilistic and real-time behavior of these models. To address this, our research paper presents a new methodology that enhances probabilistic model checking (PMC) for process models. This is made possible by leveraging continuous time Markov chains (CTMCs) as the basic model. This amalgamation creates an inclusive analytical structure that improves the reliability and accuracy of process mining results, thereby enabling corporations to derive deeper insights from their processes and make decisions based on data. ....

Continuous Time Markov Chain , Petri Net , Robabilistic Model Checking , Process Discovery , Process Mining ,

"PM2PMC: A Probabilistic Model Checking Approach in Process Mining" by Fawad Ali Mangi, Guoxin Su et al.

The field of process mining is experiencing rapid growth, utilizing data science techniques to analyze business processes and uncover insights about their performance, efficiency, and compliance. The process mining approach begins with process discovery, which involves creating process models based on event logs; and depending on the type of models discovered, the correctness of models needs to be guaranteed. Traditional model checking methods are good approaches to verify the correctness of process models, but those methods cannot model the probabilistic nature of the discovered process models. We define an approach and provides an implementation that enables probabilistic property checking of process mining models. The proposed method extracts a skeleton model by process mining, transforms the extracted model into a formal model, and verifies requirements by a probabilistic model checking technique. The PM2PMC approach has several advantages including its ability to analyze systems w ....

Petri Nets , Robabilistic Model Checking , Process Discovery , Process Mining , Eplay Algorithms ,

"Multi-objective Task Assignment and Multiagent Planning with Hybrid GP" by Thomas Robinson and Guoxin Su

Allocation and planning with a collection of tasks and a group of agents is an important problem in multiagent systems. One commonly faced bottleneck is scalability, as in general the multiagent model increases exponentially in size with the number of agents. We consider the combination of random task assignment and multiagent planning under multiple-objective constraints, and show that this problem can be decentralised to individual agent-task models. We present an algorithm of point-oriented Pareto computation, which checks whether a point corresponding to given cost and probability thresholds for our formal problem is feasible or not. If the given point is infeasible, our algorithm finds a Pareto-optimal point which is closest to the given point. We provide the first multi-objective model checking framework that simultaneously uses GPU and multi-core acceleration. Our framework manages CPU and GPU devices as a load balancing problem for parallel computation. Our experiments demonstr ....

Gpu And Multi Core Acceleration , Ultiagent System , Robabilistic Model Checking , Task Assignment ,

"Quantitative Verification for Monitoring Event-Streaming Systems" by Guoxin Su, Li Liu et al.

High-performance data streaming technologies are increasingly adopted in IT companies to support the integration of heterogeneous and possibly distributed applications. Compared with the traditional message queuing middleware, a streaming platform enables the implementation of event-streaming systems (ESS) which include not only complex queues but also pipelines that transform and react to the streams of data. By analysing the centralised data streams, one can evaluate the Quality-of-Service for other systems and components that produce or consume those streams. We consider the exploitation of probabilistic model checking as a performance monitoring technique for ESS systems. Probabilistic model checking is a mature, powerful verification technique with successful application in performance analysis. However, an ESS system may contain quantitative parameters that are determined by event streams observed in a certain period of time. In this paper, we present a novel theoretical framewor ....

Discrete Time Markov Chain , Event Stream , Arametric Model Checking , Performance Monitoring , Robabilistic Model Checking , Statistical Inference ,