CoChat
Sign Up
Cited by 23Business Process Modeling and AnalysisSoftware System Performance and ReliabilitySoftware Testing and Debugging Techniques

DeepAlign: Alignment-Based Process Anomaly Correction Using Recurrent Neural Networks

Open access
Nolle, Timo, Seeliger, Alexander, Thoma, Nils, Mühlhäuser, Max·2020·Lecture Notes in Computer Science
DOI 10.1007/978-3-030-49435-3_20PMC PMC7266459
Open this paper in CoChat — free account

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1007/978-3-030-49435-3_20

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Tools like Deep Align have demonstrated the potential of machine learning to improve the accuracy of sequence alignments.

    “For instance, tools like Deep Align have demonstrated the potential of machine learning to improve the accuracy of sequence alignments [72].”

    From Whole-Genome Alignment: Methods, Challenges, and Future Directions (2024) · cites this paper as [72]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 17

  1. [2]Berti, A., van Zelst, S.J., van der Aalst, W.: Process Mining for Python (PM4Py): bridging the gap between process-and data science, pp. 13–16 (2019)
  2. [3]Bezerra F Wainer J Algorithms for anomaly detection of traces in logs of process aware information systems Inf. Syst. 2013 38 1 33 44 10.1016/j.is.2012.04.004 · DOI
  3. [4]Böhmer K Rinderle-Ma S Debruyne C Multi-perspective anomaly detection in business process execution events On the Move to Meaningful Internet Systems: OTM 2016 Conferences 2016 Cham Springer 80 98
  4. [5]Jagadeesh Chandra Bose RP van der Aalst W Hull R Mendling J Tai S Trace alignment in process mining: opportunities for process diagnostics Business Process Management 2010 Heidelberg Springer 227 242
  5. [6]Burattin, A.: PLG2: multiperspective process randomization with online and offline simulations. In: BPM 2016 (Demos), pp. 1–6 (2016)
  6. [7]Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014) · arXiv
  7. [9]Leemans, S.J., Fahland, D., Van Der Aalst, W.M.: Process and deviation exploration with inductive visual miner. In: BPM 2014 (Demos), vol. 1295, no. 46, 8 (2014)
  8. [10]Leemans SJJ Syring AF van der Aalst WMP Hildebrandt T van Dongen BF Röglinger M Mendling J Earth movers’ stochastic conformance checking Business Process Management Forum 2019 Cham Springer 127 143
  9. [11]Mannhardt, F., De Leoni, M., Reijers, H.A.: The multi-perspective process explorer. In: BPM 2015 (Demos), vol. 1418, pp. 130–134 (2015)
  10. [12]Mannhardt F de Leoni M Reijers HA van der Aalst WMP Balanced multi-perspective checking of process conformance Computing 2015 98 4 407 437 10.1007/s00607-015-0441-1 · DOI
  11. [13]Nolle T Luettgen S Seeliger A Mühlhäuser M Analyzing business process anomalies using autoencoders Mach. Learn. 2018 107 11 1875 1893 10.1007/s10994-018-5702-8 · DOI
  12. [15]Nolle, T., Seeliger, A., Mühlhäuser, M.: BINet: multivariate business process anomaly detection using deep learning. In: Proceedings of the 16th International Conference on Business Process Management - BPM 2018, pp. 271–287 (2018)
  13. [16]Pauwels, S., Calders, T.: An anomaly detection technique for business processes based on extended dynamic Bayesian networks. In: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, pp. 494–501. ACM (2019)
  14. [17]Sun, Q., Lee, S., Batra, D.: Bidirectional beam search: forward-backward inference in neural sequence models for fill-in-the-blank image captioning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition - CVPR 2017, pp. 6961–6969 (2017)
  15. [18]Tax N van Zelst SJ Teinemaa I Gulden J Reinhartz-Berger I Schmidt R Guerreiro S Guédria W Bera P An experimental evaluation of the generalizing capabilities of process discovery techniques and black-box sequence models Enterprise, Business-Process and Information Systems Modeling 2018 Cham Springer 165 180
  16. [19]van Dongen B Carmona J Chatain T Taymouri F Dubois E Pohl K Aligning modeled and observed behavior: a compromise between computation complexity and quality Advanced Information Systems Engineering 2017 Cham Springer 94 109
  17. [20]Weijters, A., Ribeiro, J.: Flexible heuristics miner (FHM). In: Proceedings of the 2011 IEEE Symposium on Computational Intelligence and Data Mining - CIDM 2011, pp. 310–317. IEEE (2011)

Cited by

Sorted by citations

Authors
Nolle, T., Seeliger, A., Thoma, N., & Mühlhäuser, M.
Affiliations
Technische Universität Darmstadt (DE)
Corresponding author
Nolle, Timo
ORCID
Venue
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Published
2020
Pages
319-333
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-030-49435-3_20
PMC
PMC7266459
OpenAlex
W2991375408
Cited by
23
References
1
Citations by year
2026: 1 · 2025: 4 · 2024: 5 · 2023: 4 · 2022: 5 · 2021: 2 · 2020: 2
FWCI
5.67
Type
conference paper
Language
en
Open access
Licence
Locations
  • Lecture notes in computer science (publishedVersion)
  • PubMed Central (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • TUbilio (Technical University of Darmstadt) (submittedVersion)
  • arXiv (Cornell University)
Funders
Bundesministerium für Bildung und Forschung (01IS17050, 01IS18022D)
Topics
  • Social Sciences › Business, Management and Accounting › Management Information Systems › Business Process Modeling and Analysis
  • Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
  • Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
Keywords
conformance checking, process mining, sequence alignment, recurrent neural networks, next event prediction, event logs

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review