Generating Reliable Process Event Streams and Time Series Data Based on Neural Networks

Tobias Herbert, Juergen Mangler, Stefanie Rinderle-Ma

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Domains such as manufacturing and medicine crave for continuous monitoring and analysis of their processes, especially in combination with time series as produced by sensors. Time series data can be exploited to, for example, explain and predict concept drifts during runtime. Generally, a certain data volume is required in order to produce meaningful analysis results. However, reliable data sets are often missing, for example, if event streams and times series data are collected separately, in case of a new process, or if it is too expensive to obtain a sufficient data volume. Additional challenges arise with preparing time series data from multiple event sources, variations in data collection frequency, and concept drift. This paper proposes the GENLOG approach to generate reliable event and time series data that follows the distribution of the underlying input data set. GENLOG employs data resampling and enables the user to select different parts of the log data to orchestrate the training of a recurrent neural network for stream generation. The generated data is sampled back to its original sample rate and is embedded into the originating log data file. Overall, GENLOG can boost small data sets and consequently the application of online process mining.

Original languageEnglish
Title of host publicationEnterprise, Business-Process and Information Systems Modeling - 22nd International Conference, BPMDS 2021, and 26th International Conference, EMMSAD 2021, Held at CAiSE 2021, Proceedings
EditorsAdriano Augusto, Asif Gill, Selmin Nurcan, Iris Reinhartz-Berger, Rainer Schmidt, Jelena Zdravkovic
PublisherSpringer Science and Business Media Deutschland GmbH
Pages81-95
Number of pages15
ISBN (Print)9783030791858
DOIs
StatePublished - 2021
Event22nd International Conference on Business Process Modeling, Development and Support, BPMDS 2021 and 26th International Conference on Exploring Modeling Methods for Systems Analysis and Development, EMMSAD 2021 Held at CAiSE 2021 - Virtual, Online
Duration: 28 Jun 202129 Jun 2021

Publication series

NameLecture Notes in Business Information Processing
Volume421
ISSN (Print)1865-1348
ISSN (Electronic)1865-1356

Conference

Conference22nd International Conference on Business Process Modeling, Development and Support, BPMDS 2021 and 26th International Conference on Exploring Modeling Methods for Systems Analysis and Development, EMMSAD 2021 Held at CAiSE 2021
CityVirtual, Online
Period28/06/2129/06/21

Keywords

  • Deep learning
  • Recurrent neural network
  • Reliable dataset boosting
  • Synthetic log data
  • Time series generation

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