TY - GEN
T1 - Analysing Business Process Anomalies Using Discrete-Time Markov chains
AU - Yang, Lingkai
AU - McClean, Sally
AU - Donnelly, Mark
AU - Khan, Kashaf
AU - Burke, Kevin
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - 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.
AB - 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.
KW - Discrete-Time Markov chains
KW - Hitting probability
KW - Process anomaly detection
KW - Šidák correction
UR - http://www.scopus.com/inward/record.url?scp=85105334040&partnerID=8YFLogxK
U2 - 10.1109/HPCC-SmartCity-DSS50907.2020.00163
DO - 10.1109/HPCC-SmartCity-DSS50907.2020.00163
M3 - Conference contribution
AN - SCOPUS:85105334040
T3 - Proceedings - 2020 IEEE 22nd International Conference on High Performance Computing and Communications, IEEE 18th International Conference on Smart City and IEEE 6th International Conference on Data Science and Systems, HPCC-SmartCity-DSS 2020
SP - 1258
EP - 1265
BT - Proceedings - 2020 IEEE 22nd International Conference on High Performance Computing and Communications, IEEE 18th International Conference on Smart City and IEEE 6th International Conference on Data Science and Systems, HPCC-SmartCity-DSS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd IEEE International Conference on High Performance Computing and Communications, 18th IEEE International Conference on Smart City and 6th IEEE International Conference on Data Science and Systems, HPCC-SmartCity-DSS 2020
Y2 - 14 December 2020 through 16 December 2020
ER -