What Averages Do Not Tell -- Predicting Real Life Processes with Sequential Deep Learning

• URL: http://arxiv.org/abs/2110.10225v1
• Date: Tue, 19 Oct 2021 19:45:05 GMT
• Title: What Averages Do Not Tell -- Predicting Real Life Processes with Sequential Deep Learning
• Authors: Istv\'an Ketyk\'o, Felix Mannhardt, Marwan Hassani, Boudewijn van Dongen
• Abstract summary: Process Mining concerns discovering insights on business processes from their execution data that are logged by systems. Many Deep Learning techniques have been successfully adapted for predictive Process Mining that aims to predict process outcomes. Traces in Process Mining are multimodal sequences and very differently structured than natural language sentences or images.
• Score: 0.1376408511310322
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep Learning is proven to be an effective tool for modeling sequential data as shown by the success in Natural Language, Computer Vision and Signal Processing. Process Mining concerns discovering insights on business processes from their execution data that are logged by supporting information systems. The logged data (event log) is formed of event sequences (traces) that correspond to executions of a process. Many Deep Learning techniques have been successfully adapted for predictive Process Mining that aims to predict process outcomes, remaining time, the next event, or even the suffix of running traces. Traces in Process Mining are multimodal sequences and very differently structured than natural language sentences or images. This may require a different approach to processing. So far, there has been little focus on these differences and the challenges introduced. Looking at suffix prediction as the most challenging of these tasks, the performance of Deep Learning models was evaluated only on average measures and for a small number of real-life event logs. Comparing the results between papers is difficult due to different pre-processing and evaluation strategies. Challenges that may be relevant are the skewness of trace-length distribution and the skewness of the activity distribution in real-life event logs. We provide an end-to-end framework which enables to compare the performance of seven state-of-the-art sequential architectures in common settings. Results show that sequence modeling still has a lot of room for improvement for majority of the more complex datasets. Further research and insights are required to get consistent performance not just in average measures but additionally over all the prefixes.

Related papers

• EBES: Easy Benchmarking for Event Sequences [17.277513178760348]
  Event sequences are common data structures in various real-world domains such as healthcare, finance, and user interaction logs. Despite advances in temporal data modeling techniques, there is no standardized benchmarks for evaluating their performance on event sequences. We introduce EBES, a comprehensive benchmarking tool with standardized evaluation scenarios and protocols.
  arXiv  Detail & Related papers  (2024-10-04T13:03:43Z)
• Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation [69.60321475454843]
  We propose DPCPL, the first pre-training and prompt-tuning paradigm tailored for Multi-Behavior Sequential Recommendation. In the pre-training stage, we propose a novel Efficient Behavior Miner (EBM) to filter out the noise at multiple time scales. Subsequently, we propose to tune the pre-trained model in a highly efficient manner with the proposed Customized Prompt Learning (CPL) module.
  arXiv  Detail & Related papers  (2024-08-21T06:48:38Z)
• Avoiding Post-Processing with Event-Based Detection in Biomedical Signals [69.34035527763916]
  We propose an event-based modeling framework that directly works with events as learning targets. We show that event-based modeling (without post-processing) performs on par with or better than epoch-based modeling with extensive post-processing.
  arXiv  Detail & Related papers  (2022-09-22T13:44:13Z)
• Process-BERT: A Framework for Representation Learning on Educational Process Data [68.8204255655161]
  We propose a framework for learning representations of educational process data. Our framework consists of a pre-training step that uses BERT-type objectives to learn representations from sequential process data. We apply our framework to the 2019 nation's report card data mining competition dataset.
  arXiv  Detail & Related papers  (2022-04-28T16:07:28Z)
• ProcessTransformer: Predictive Business Process Monitoring with Transformer Network [0.06445605125467573]
  We propose ProcessTransformer, an approach for learning high-level representations from event logs with an attention-based network. Our model incorporates long-range memory and relies on a self-attention mechanism to establish dependencies between a multitude of event sequences and corresponding outputs.
  arXiv  Detail & Related papers  (2021-04-01T18:58:46Z)
• Subtask Analysis of Process Data Through a Predictive Model [5.7668512557707166]
  This paper develops a computationally efficient method for exploratory analysis of such process data. The new approach segments a lengthy individual process into a sequence of short subprocesses to achieve complexity reduction. We use the process data from PIAAC 2012 to demonstrate how exploratory analysis of process data can be done with the new approach.
  arXiv  Detail & Related papers  (2020-08-29T21:11:01Z)
• 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)
• Temporally Correlated Task Scheduling for Sequence Learning [143.70523777803723]
  In many applications, a sequence learning task is usually associated with multiple temporally correlated auxiliary tasks. We introduce a learnable scheduler to sequence learning, which can adaptively select auxiliary tasks for training. Our method significantly improves the performance of simultaneous machine translation and stock trend forecasting.
  arXiv  Detail & Related papers  (2020-07-10T10:28:54Z)
• Multi-Task Learning for Dense Prediction Tasks: A Survey [87.66280582034838]
  Multi-task learning (MTL) techniques have shown promising results w.r.t. performance, computations and/or memory footprint. We provide a well-rounded view on state-of-the-art deep learning approaches for MTL in computer vision.
  arXiv  Detail & Related papers  (2020-04-28T09:15:50Z)

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.