Analysing Business Process Anomalies Using Discrete-time Markov chains

Lingkai Yang, Sally I McClean, Mark Donnelly, Kashaf Khan, Kevin Burke

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

1 Citation (Scopus)
34 Downloads (Pure)


Within a business context, anomalies can be viewed as indicators for inefficiencies or fraud, which impact upon product quality and customer satisfaction. The development of approaches to monitor, detect and predict anomalous business processes remains an important research topic. In this paper, we propose a method, combining Discrete-Time Markov chains (DTMCs) and hitting probabilities (HP), for detecting anomalies occurring in the execution of business processes. Our method extends standard DTMCs to be able to estimate the probability of occurring for a process instance even though it is partially recorded (i.e., the initial executions are missing). The proposed method, denoted as HPDTMC, does not rely on prior knowledge about anomalies and the business process and can be trained on datasets already consisting of anomalies. A Šidák correction is applied to balance the probability of instances of varying length since naturally, process instances with more executions have lower sequence probability and more likely to be detected as anomalies by using DTMCs. We demonstrate the effectiveness of the method by evaluating it on two artificial datasets and one real-life dataset against seven classic anomaly detection methods. In the experiments, our approach reached an F1 score of 0.904 on average. Moreover, the proposed method outperforms competitors under noisy conditions. The main contribution of this paper is the proposed noise-robust method which is able to detect fully or partially recorded process instances of varying lengths.

Original languageEnglish
Title of host publication2020 IEEE 22nd International Conference on High Performance Computing and Communications; IEEE 18th International Conference on Smart City; IEEE 6th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)
Number of pages8
ISBN (Electronic)978-1-7281-7649-9
ISBN (Print)978-1-7281-7650-5
Publication statusPublished (in print/issue) - 26 Apr 2021
EventIEEE International Conference on High Performance Computing and Communications (HPCC): 2020 IEEE 22nd International Conference - Yanuca Island, Cuvu, Fiji
Duration: 14 Dec 202016 Dec 2020


ConferenceIEEE International Conference on High Performance Computing and Communications (HPCC)

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This research is supported by BTIIC (the BT Ireland Innovation Centre), funded by BT and Invest Northern Ireland.

Publisher Copyright:
© 2020 IEEE.


  • Discrete-Time Markov chains
  • Hitting probability
  • Process anomaly detection
  • Šidák correction


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