91 research outputs found

    Conformance checking using activity and trace embeddings

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    Conformance checking describes process mining techniques used to compare an event log and a corresponding process model. In this paper, we propose an entirely new approach to conformance checking based on neural network-based embeddings. These embeddings are vector representations of every activity/task present in the model and log, obtained via act2vec, a Word2vec based model. Our novel conformance checking approach applies the Word Mover’s Distance to the activity embeddings of traces in order to measure fitness and precision. In addition, we investigate a more efficiently calculated lower bound of the former metric, i.e. the Iterative Constrained Transfers measure. An alternative method using trace2vec, a Doc2vec based model, to train and compare vector representations of the process instances themselves is also introduced. These methods are tested in different settings and compared to other conformance checking techniques, showing promising results

    DeepAlign: Alignment-based Process Anomaly Correction using Recurrent Neural Networks

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    In this paper, we propose DeepAlign, a novel approach to multi-perspective process anomaly correction, based on recurrent neural networks and bidirectional beam search. At the core of the DeepAlign algorithm are two recurrent neural networks trained to predict the next event. One is reading sequences of process executions from left to right, while the other is reading the sequences from right to left. By combining the predictive capabilities of both neural networks, we show that it is possible to calculate sequence alignments, which are used to detect and correct anomalies. DeepAlign utilizes the case-level and event-level attributes to closely model the decisions within a process. We evaluate the performance of our approach on an elaborate data corpus of 252 realistic synthetic event logs and compare it to three state-of-the-art conformance checking methods. DeepAlign produces better corrections than the rest of the field reaching an overall F1F_1 score of 0.95720.9572 across all datasets, whereas the best comparable state-of-the-art method reaches 0.64110.6411

    Conformance Testing for Stochastic Cyber-Physical Systems

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    Conformance is defined as a measure of distance between the behaviors of two dynamical systems. The notion of conformance can accelerate system design when models of varying fidelities are available on which analysis and control design can be done more efficiently. Ultimately, conformance can capture distance between design models and their real implementations and thus aid in robust system design. In this paper, we are interested in the conformance of stochastic dynamical systems. We argue that probabilistic reasoning over the distribution of distances between model trajectories is a good measure for stochastic conformance. Additionally, we propose the non-conformance risk to reason about the risk of stochastic systems not being conformant. We show that both notions have the desirable transference property, meaning that conformant systems satisfy similar system specifications, i.e., if the first model satisfies a desirable specification, the second model will satisfy (nearly) the same specification. Lastly, we propose how stochastic conformance and the non-conformance risk can be estimated from data using statistical tools such as conformal prediction. We present empirical evaluations of our method on an F-16 aircraft, an autonomous vehicle, a spacecraft, and Dubin's vehicle

    Process mining meets model learning: Discovering deterministic finite state automata from event logs for business process analysis

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    Within the process mining field, Deterministic Finite State Automata (DFAs) are largely employed as foundation mechanisms to perform formal reasoning tasks over the information contained in the event logs, such as conformance checking, compliance monitoring and cross-organization process analysis, just to name a few. To support the above use cases, in this paper, we investigate how to leverage Model Learning (ML) algorithms for the automated discovery of DFAs from event logs. DFAs can be used as a fundamental building block to support not only the development of process analysis techniques, but also the implementation of instruments to support other phases of the Business Process Management (BPM) lifecycle such as business process design and enactment. The quality of the discovered DFAs is assessed wrt customized definitions of fitness, precision, generalization, and a standard notion of DFA simplicity. Finally, we use these metrics to benchmark ML algorithms against real-life and synthetically generated datasets, with the aim of studying their performance and investigate their suitability to be used for the development of BPM tools

    Process Mining Handbook

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    This is an open access book. This book comprises all the single courses given as part of the First Summer School on Process Mining, PMSS 2022, which was held in Aachen, Germany, during July 4-8, 2022. This volume contains 17 chapters organized into the following topical sections: Introduction; process discovery; conformance checking; data preprocessing; process enhancement and monitoring; assorted process mining topics; industrial perspective and applications; and closing

    ESSAYS ON SOURCING DECISIONS: A BEHAVIORAL PERSPECTIVE

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    This dissertation examines how managers make and perceive supply chain governance decisions. A plethora of supply chain management literature suggests that managers will a priori choose a governance form that will manage risks while pursuing benefits. A number of theories have been used to inform this view: agency, resource-based view and transaction cost economics. Agency theory, the resource-based view and transaction cost economics all share the common assumption that a manager is considering both the risks and benefits of their decisions. In addition each of these perspectives assumes managers are boundedly rational. Taken together these two assumptions suggest managers have imperfect information, the inability to explicate the perfect contract, or limits on their ability to process relevant information when they consider risks and benefits. Yet, other than suggesting that managers have limited cognitive ability (bounded rationality), these perspectives are silent about the influence of cognitive processes on managers consideration of risks, benefits, and ultimately their decision-making. Thus there is a gap in the extant supply chain management literature of our understanding of how cognitive processes such as attention, emotions, feeling, memory or social context may result in a cognitive or decision-making bias. Specifically, evidence from psychology suggests that managers may inadvertently overlook or misperceive risks and benefits because of biased attention and memory (i.e., availability and salience), emotions and feelings, and social considerations (e.g., bandwagon pressure). As a result of a gap in our understanding, supply chains may be overly risky (costly), while not receiving offsetting benefits (value creation). This dissertation addresses this gap
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