PELP: Pioneer Event Log Prediction Using Sequence-to-Sequence Neural Networks

• URL: http://arxiv.org/abs/2312.09741v1
• Date: Fri, 15 Dec 2023 12:30:30 GMT
• Title: PELP: Pioneer Event Log Prediction Using Sequence-to-Sequence Neural Networks
• Authors: Wenjun Zhou, Artem Polyvyanyy, James Bailey
• Abstract summary: We present our approach to solving the event log prediction problem using the sequence-to-sequence deep learning approach. We evaluate and analyze the prediction outcomes on a variety of synthetic logs and seven real-life logs.
• Score: 13.221876371019718
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Process mining, a data-driven approach for analyzing, visualizing, and improving business processes using event logs, has emerged as a powerful technique in the field of business process management. Process forecasting is a sub-field of process mining that studies how to predict future processes and process models. In this paper, we introduce and motivate the problem of event log prediction and present our approach to solving the event log prediction problem, in particular, using the sequence-to-sequence deep learning approach. We evaluate and analyze the prediction outcomes on a variety of synthetic logs and seven real-life logs and show that our approach can generate perfect predictions on synthetic logs and that deep learning techniques have the potential to be applied in real-world event log prediction tasks. We further provide practical recommendations for event log predictions grounded in the outcomes of the conducted experiments.

Related papers

• Generating Feasible and Plausible Counterfactual Explanations for Outcome Prediction of Business Processes [45.502284864662585]
  We introduce a data-driven approach, REVISEDplus, to generate plausible counterfactual explanations. First, we restrict the counterfactual algorithm to generate counterfactuals that lie within a high-density region of the process data. We also ensure plausibility by learning sequential patterns between the activities in the process cases.
  arXiv  Detail & Related papers  (2024-03-14T09:56:35Z)
• Detecting Anomalous Events in Object-centric Business Processes via Graph Neural Networks [55.583478485027]
  This study proposes a novel framework for anomaly detection in business processes. We first reconstruct the process dependencies of the object-centric event logs as attributed graphs. We then employ a graph convolutional autoencoder architecture to detect anomalous events.
  arXiv  Detail & Related papers  (2024-02-14T14:17:56Z)
• A Discussion on Generalization in Next-Activity Prediction [1.2289361708127877]
  We show that there is an enormous amount of example leakage in all of the commonly used event logs. We argue that designing robust evaluations requires a more profound conceptual engagement with the topic of next-activity prediction.
  arXiv  Detail & Related papers  (2023-09-18T09:42:36Z)
• Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment [72.50906475214457]
  The goal of sequential event prediction is to estimate the next event based on a sequence of historical events. In practice, the next-event prediction models are trained with sequential data collected at one time. We propose a framework with hierarchical branching structures for learning context-specific representations.
  arXiv  Detail & Related papers  (2022-10-24T07:54:13Z)
• Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful Models [77.34726150561087]
  We define explainability through the interpretability of the explanations and the faithfulness of the explainability model in the field of process outcome prediction. This paper contributes a set of guidelines named X-MOP which allows selecting the appropriate model based on the event log specifications.
  arXiv  Detail & Related papers  (2022-03-30T05:59:50Z)
• Interpreting Process Predictions using a Milestone-Aware Counterfactual Approach [0.0]
  We explore the use of a popular model-agnostic counterfactual algorithm, DiCE, in the context of predictive process analytics. The analysis reveals that the algorithm is limited when being applied to derive explanations of process predictions. We propose an approach that supports deriving milestone-aware counterfactuals at different stages of a trace to promote interpretability.
  arXiv  Detail & Related papers  (2021-07-19T09:14:16Z)
• Process Model Forecasting Using Time Series Analysis of Event Sequence Data [0.23099144596725568]
  We develop a technique to forecast the entire process model from historical event data. Our implementation demonstrates the accuracy of our technique on real-world event log data.
  arXiv  Detail & Related papers  (2021-05-03T18:00:27Z)
• Process Discovery for Structured Program Synthesis [70.29027202357385]
  A core task in process mining is process discovery which aims to learn an accurate process model from event log data. In this paper, we propose to use (block-) structured programs directly as target process models. We develop a novel bottom-up agglomerative approach to the discovery of such structured program process models.
  arXiv  Detail & Related papers  (2020-08-13T10:33:10Z)
• Cause vs. Effect in Context-Sensitive Prediction of Business Process Instances [0.440401067183266]
  This paper addresses the issue of context being cause or effect of the next event and its impact on next event prediction. We leverage previous work on probabilistic models to develop a Dynamic Bayesian Network technique. We evaluate our technique with two real-life data sets and benchmark it with other techniques from the field of predictive process monitoring.
  arXiv  Detail & Related papers  (2020-07-15T08:58:15Z)
• Video Prediction via Example Guidance [156.08546987158616]
  In video prediction tasks, one major challenge is to capture the multi-modal nature of future contents and dynamics. In this work, we propose a simple yet effective framework that can efficiently predict plausible future states.
  arXiv  Detail & Related papers  (2020-07-03T14:57:24Z)
• An empirical comparison of deep-neural-network architectures for next activity prediction using context-enriched process event logs [0.0]
  Researchers have proposed a variety of predictive business process monitoring (PBPM) techniques. These techniques rely on deep neural networks (DNNs) and consider information about the context, in which the process is running. We evaluate the predictive quality of three promising DNN architectures, combined with five proven encoding techniques and based on five context-enriched real-life event logs.
  arXiv  Detail & Related papers  (2020-05-03T21:33:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.