Probabilistic Process Monitoring in Process-Aware Information Systems

Complex information systems generate large amount of event logs that represent the states of system dynamics. By monitoring these logs, we can learn the process models that describe the underlying business procedures, predict the future development of the systems, and check whether the process models match the expected ones. Most of the existing process monitoring techniques are derived from the workflow management systems used to cope with the logs generated by systems with deterministic outcomes. In this dissertation, however, I consider novel techniques that handle event log data, monitor system deviations, and infer the development of systems based on probabilistic process models. In particular, I present a novel process monitoring approach based on maximizing the information divergences of the system state dynamics and demonstrate its efficiency in detecting abrupt changes, as well as long-term system deviation. In addition, a new process modeling technique, Classification Tree hidden (semi-) Markov Model (CTHMM), is proposed. I show that CTHMM derived from Classification and Regression Tree and hidden semi-Markov model (HSMM) with hidden system states identified by Classification Tree can help discover and predict relevant system state sequences in temporal-probabilistic manners. The main contributions of this dissertation can be summarized as follows: 1) a new approach used in process monitoring that helps detect anomalies of dynamic systems from the point of views of both system change-point and long-term system deviation; 2) a unique HMM/HSMM learning technique that solves the problem of hidden state splitting and estimates HMM/HSMM parameters simultaneously; 3) a novel temporal-probabilistic process model that generates human-comprehensible IF-THEN system state definitions used to help infer evolutions of discrete dynamic systems

Toward Transparent Sequence Models with Model-Based Tree Markov Model

In this study, we address the interpretability issue in complex, black-box Machine Learning models applied to sequence data. We introduce the Model-Based tree Hidden Semi-Markov Model (MOB-HSMM), an inherently interpretable model aimed at detecting high mortality risk events and discovering hidden patterns associated with the mortality risk in Intensive Care Units (ICU). This model leverages knowledge distilled from Deep Neural Networks (DNN) to enhance predictive performance while offering clear explanations. Our experimental results indicate the improved performance of Model-Based trees (MOB trees) via employing LSTM for learning sequential patterns, which are then transferred to MOB trees. Integrating MOB trees with the Hidden Semi-Markov Model (HSMM) in the MOB-HSMM enables uncovering potential and explainable sequences using available information

Process Discovery using Classification Tree Hidden Semi-Markov Model

Various and ubiquitous information systems are being used in monitoring, exchanging, and collecting information. These systems are generating massive amount of event sequence logs that may helps us understand underlying phenomenon. By analyzing these logs, we can learn process models that describe system procedures, predict the development of the system, or check whether the changes are expected. In this paper, we consider a novel technique that models these sequences of events in temporal-probabilistic manners. Specifically, we propose a probabilistic process model that combines hidden semi-Markov model and classification trees learning. Our experimental result shows that the proposed approach can answer a kind of question–“what are the most frequent sequence of system dynamics relevant to a given sequence of observable events?”. For example, “Given a series of medical treatments, what are the most relevant patients’ health condition pattern changes in different times?

Process Monitoring Using Maximum Sequence Divergence

Process Monitoring involves tracking a system's behaviors, evaluating the current state of the system, and discovering interesting events that require immediate actions. In this paper, we propose a process monitoring approach that helps detect the changes of dynamic systems, monitor the divergence of the system development, and evaluate the significance of the deviation. We begin with the discussion of the data reduction and symbolic data representation. Timeseries representation methods are also discussed and used as examples in the proposed approach to discretize the raw data into sequences of system states. Markov Chains and stationary state distributions are continuously generated for sequences to represent the snapshots of the system dynamics in different time frames. We use the Generalized Jensen-Shannon Divergence as a measure to monitor the changes of the stationary symbol probability distributions and evaluate the significance of the system deviation. We prove that the proposed approach is able to detect the deviation of the systems we monitor and assess the deviation significance in probabilistic manne