PGraphD*: Methods for Drift Detection and Localisation Using Deep Learning Modelling of Business Processes

Khadijah Muzzammil Hanga; Yevgeniya Kovalchuk; Mohamed Medhat Gaber

doi:10.3390/e24070910

PGraphD*: Methods for Drift Detection and Localisation Using Deep Learning Modelling of Business Processes

Khadijah Muzzammil Hanga^*
, Yevgeniya Kovalchuk
, Mohamed Medhat Gaber

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

1 Citation (SciVal)

Original language	English
Article number	910
Journal	Entropy
Volume	24
Issue number	7
DOIs	https://doi.org/10.3390/e24070910
Publication status	Published (VoR) - Jul 2022

Funding

Funding: This research was partially funded by the Petroleum Technology Development Fund (PTDF) of Nigeria.

Keywords

business process management
concept drift detection
concept drift localisation
deep learning
graph streams
long short-term memory
process mining

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10.3390/e24070910

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title = "PGraphD*: Methods for Drift Detection and Localisation Using Deep Learning Modelling of Business Processes",

keywords = "business process management, concept drift detection, concept drift localisation, deep learning, graph streams, long short-term memory, process mining",

author = "Hanga, \{Khadijah Muzzammil\} and Yevgeniya Kovalchuk and Gaber, \{Mohamed Medhat\}",

note = "Funding Information: Funding: This research was partially funded by the Petroleum Technology Development Fund (PTDF) of Nigeria. Publisher Copyright: {\textcopyright} 2022 by the authors. Licensee MDPI, Basel, Switzerland.",

year = "2022",

month = jul,

doi = "10.3390/e24070910",

language = "English",

volume = "24",

journal = "Entropy",

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T2 - Methods for Drift Detection and Localisation Using Deep Learning Modelling of Business Processes

AU - Hanga, Khadijah Muzzammil

AU - Kovalchuk, Yevgeniya

AU - Gaber, Mohamed Medhat

N1 - Funding Information: Funding: This research was partially funded by the Petroleum Technology Development Fund (PTDF) of Nigeria. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

PY - 2022/7

Y1 - 2022/7

KW - business process management

KW - concept drift detection

KW - concept drift localisation

KW - deep learning

KW - graph streams

KW - long short-term memory

KW - process mining

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UR - https://www.scopus.com/pages/publications/85133554362#tab=citedBy

U2 - 10.3390/e24070910

DO - 10.3390/e24070910

M3 - Article

AN - SCOPUS:85133554362

SN - 1099-4300

VL - 24

JO - Entropy

JF - Entropy

IS - 7

M1 - 910

ER -

PGraphD*: Methods for Drift Detection and Localisation Using Deep Learning Modelling of Business Processes

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