In complex processes, various events can happen in different sequences. The prediction of the next event activity given an a-priori process state is of importance in such processes. Recent methods leverage deep learning techniques such as recurrent neural networks to predict event activities from raw process logs. However, deep learning techniques cannot efficiently model logical behaviors of complex processes. In this paper, we take advantage of Petri nets as a powerful tool in modeling logical behaviors of complex processes. We propose an approach which first discovers Petri nets from event logs utilizing a recent process mining algorithm. In a second step, we enhance the obtained model with time decay functions to create timed process state samples. Finally, we use these samples in combination with token movement counters and Petri net markings to train a deep learning model that predicts the next event activity. We demonstrate significant performance improvements and outperform the state-of-the-art methods on eight out of nine real-world benchmark event logs in accuracy.
@cite_15 developed a method which models a process in a probabilistic and instance-specific way. This model is able to predict next event activities and can be translated into a Markov chain. Their approach has been implemented on a simulated automobile insurance claim process.
@cite_15
1998640084 1584802047 2169601390 1511986666
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. The goal of our work is to examine the utility of predictive analytics for case-oriented semi-structured business processes. As a first step towards this goal, this paper describes an approach to leverage case history to predict outcomes at decision points in case-oriented semi-structured processes, and examine how the contents of documents at these decision points influence their outcomes. We apply an ant-colony optimization (ACO) based algorithm to create a probabilistic activity graph from traces, and use it to identify key decision points in a given process. For each activity node that represents a decision point in the mined probabilistic graph, the likelihood of different outcomes from the node can be correlated with the contents of documents accessed by the activity node. This is achieved by using a standard decision tree learning algorithm. We validate our approach on correlated case instance traces generated by a simulator that we constructed to implement non-deterministic executions of an automobile insurance claims scenario. In practice we find that our approach can lead to useful predictions at different stages of execution in a semi-structured case oriented process. The probabilistic prediction of road traffic scenarios is addressed. One result is a probabilistic occupancy of traffic participants, and the other result is the collision risk for autonomous vehicles when executing a planned maneuver. The probabilistic occupancy of surrounding traffic participants helps to plan the maneuver of an autonomous vehicle, whereas the computed collision risk helps to decide if a planned maneuver should be executed. Two methods for the probabilistic prediction are presented and compared: 1) Markov chain abstraction and 2) Monte Carlo simulation. The performance of both methods is evaluated with respect to the prediction of the probabilistic occupancy and the collision risk. For each comparison test, we use the same models that generate the probabilistic behavior of traffic participants, where the generation of these data is not compared with real-world data. However, the results independently show the behavior generation that Markov chains are preferred for the probabilistic occupancy, whereas Monte Carlo simulation is clearly preferred for determining the collision risk. Most tasks require a person or an automated system to reason -- to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.