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

Detecting money laundering in transaction monitoring using hidden Markov model

The purpose of the thesis is to introduce, build and test HMM as a method of detecting suspicious financial transactions that might be correlated with money laundering. HMM is a statistical Markov model in which the system being modelled is assumed to be Markov process with unobserved (i.e., hidden) states. These hidden states however generate observable outcomes. HMM fits the context of transaction monitoring in the fight against money laundering as the intent of a transaction (part of money laundering scheme or not) is and only some parameters of the transaction can be observed. The model was built and tested on artificial datasets provided by Salv Technologies and commonly used k-means clustering model was chosen for comparison. Analysis and testing showed that overall, HMM outperforms k-means clustering. Based on analysis, it can be concluded that in essence, HMM can be used in transaction monitoring but getting high precision needs expert knowledge and practical testing. A brief overview of money laundering, anomaly detection methods and HMM are given. Empirical part includes application of HMM on 3 different study cases using R software

Improved Behavior Monitoring and Classification Using Cues Parameters Extraction from Camera Array Images

Behavior monitoring and classification is a mechanism used to automatically identify or verify individual based on their human detection, tracking and behavior recognition from video sequences captured by a depth camera. In this paper, we designed a system that precisely classifies the nature of 3D body postures obtained by Kinect using an advanced recognizer. We proposed novel features that are suitable for depth data. These features are robust to noise, invariant to translation and scaling, and capable of monitoring fast human bodyparts movements. Lastly, advanced hidden Markov model is used to recognize different activities. In the extensive experiments, we have seen that our system consistently outperforms over three depth-based behavior datasets, i.e., IM-DailyDepthActivity, MSRDailyActivity3D and MSRAction3D in both posture classification and behavior recognition. Moreover, our system handles subject's body parts rotation, self-occlusion and body parts missing which significantly track complex activities and improve recognition rate. Due to easy accessible, low-cost and friendly deployment process of depth camera, the proposed system can be applied over various consumer-applications including patient-monitoring system, automatic video surveillance, smart homes/offices and 3D games

A next click recommender system for web-based service analytics with context-aware LSTMs

Software companies that offer web-based services instead of local installations can record the user’s interactions with the system from a distance. This data can be analyzed and subsequently improved or extended. A recommender system that guides users through a business process by suggesting next clicks can help to improve user satisfaction, and hence service quality and can reduce support costs. We present a technique for a next click recommender system. Our approach is adapted from the predictive process monitoring domain that is based on long short-term memory (LSTM) neural networks. We compare three different configurations of the LSTM technique: LSTM without context, LSTM with context, and LSTM with embedded context. The technique was evaluated with a real-life data set from a financial software provider. We used a hidden Markov model (HMM) as the baseline. The configuration LSTM with embedded context achieved a significantly higher accuracy and the lowest standard deviation

Modelos ocultos de Markov: uma abordagem em controle de processos

The hidden Markov model methodology is being widely used in all fields of knowledge these days. With its strong mathematical structure, this methodology is able to model several problems because it is very malleable and unrestricted, a fact that does not occur in the Markov chain methodology. This methodology can be seen as an extension of the Markov chain, since it can contain more than one Markov chain. Problems for monitoring control charts and online control can be modeled via the Markov chain, since the sampling interval is regular and the condition of the process to be Markovian is generally assumed. Dorea et al. (2012) assumed the existence of an unobservable internal system so that monitoring by online control is only part of the process. Based on this assumption, one can model this process via the hidden Markov model, since the process comprises two Markov chains (one observable and one non-observable). This work will present the methodology of the hidden Markov model and then apply the methodology in the planning of online statistical control in order to establish an estimate for the false alarm and non-detection probabilities.A metodologia do modelo oculto de Markov está sendo amplamente utilizada em todos os campos do conhecimento nos dias de hoje. Com sua forte estrutura matemática, esta metodologia é capaz de modelar diversos problemas por ser bastante maleável e irrestrita, fato que não ocorre na metodologia de cadeia de Markov. Pode-se enxergar esta metodologia como uma extensão de cadeia de Markov, uma vez que ela pode conter mais de uma cadeia de Markov. Problemas de monitoramento de cartas de controle e controle on-line podem ser modelados via cadeia de Markov, visto que o intervalo de amostragem é regular e que a condição do processo ser Markoviano geralmente é assumida. Dorea et al. (2012) assumiram a existência de um sistema interno não observável de modo que o monitoramento por controle on-line é apenas uma parte do processo. Com base nesta suposição, pode-se modelar este processo via modelo oculto de Markov, já que o processo compreende duas cadeias de Markov (uma observável e outra não observável). Este trabalho apresentará a metodologia do modelo oculto de Markov para, em seguida, aplicar a metodologia no planejamento do controle estatístico on-line a fim de estabelecer uma estimação para as probabilidades de alarme falso e a de não detecção

An agent-based implementation of hidden Markov models for gas turbine condition monitoring

This paper considers the use of a multi-agent system (MAS) incorporating hidden Markov models (HMMs) for the condition monitoring of gas turbine (GT) engines. Hidden Markov models utilizing a Gaussian probability distribution are proposed as an anomaly detection tool for gas turbines components. The use of this technique is shown to allow the modeling of the dynamics of GTs despite a lack of high frequency data. This allows the early detection of developing faults and avoids costly outages due to asset failure. These models are implemented as part of a MAS, using a proposed extension of an established power system ontology, for fault detection of gas turbines. The multi-agent system is shown to be applicable through a case study and comparison to an existing system utilizing historic data from a combined-cycle gas turbine plant provided by an industrial partner