![]() In: Weske, M., Montali, M., Weber, I., vom Brocke, J. Relating Processes and Models, Springer, Cham (2018)ĭe Koninck, P., vanden Broucke, S., De Weerdt, J.: act2vec, trace2vec, log2vec, and model2vec: representation learning for business processes. 45(1), 5–32 (2001)īurattin, A.: PLG2: multiperspective processes randomization and simulation for online and offline settings (2015)Ĭarmona, J., van Dongen, B.F., Solti, A., Weidlich, M.: Conformance Checking. 38(1), 33–44 (2013)īöhmer, K., Rinderle-Ma, S.: Multi-perspective anomaly detection in business process execution events. īezerra, F., Wainer, J.: Algorithms for anomaly detection of traces in logs of process aware information systems. īarbon Junior, S., Ceravolo, P., Damiani, E., Marques Tavares, G.: Evaluating trace encoding methods in process mining. 161–168 (2020)īarbon, S., Jr., Ceravolo, P., Damiani, E., Tavares, G.M.: Using meta-learning to recommend process discovery methods (2021). In: 2020 2nd International Conference on Process Mining (ICPM), pp. īarbon, S., Jr., Ceravolo, P., Damiani, E., Omori, N.J., Tavares, G.M.: Anomaly detection on event logs with a scarcity of labels. KeywordsĪdam, S.P., Alexandropoulos, S.-A.N., Pardalos, P.M., Vrahatis, M.N.: No free lunch theorem: a Review. This performance demonstrates that traditional process mining analysis can be leveraged when matched with intelligent decision support approaches. Our proposed Meta-learning method outperforms the baseline reaching an F-score of 0.73. Results indicate that event log characteristics influence the representational capability of encodings differently. ![]() We used three encoding techniques from different families, 80 log descriptors, 168 event logs, and six anomaly types for experiments. Our method extracts meta-features from an event log and recommends the most suitable encoding technique to increase the anomaly detection performance. ![]() In this work, we combine the representational power of encoding with a Meta-learning strategy to enhance the detection of anomalous traces in event logs towards fitting the best discriminative capability between common and irregular traces. The considerable number of techniques and reduced availability of experts pose an additional challenge to detecting anomalous traces for particular event log scenarios. However, in many real-world environments, the log is noisy and the model unavailable, requiring more robust techniques and expert assistance to perform conformance checking. These methods rely on the comparison of the event log obtained and the designed process model. Conformance checking techniques are usually employed in these situations. Focusing on mitigating this phenomenon, organizations spend efforts to detect anomalous traces in their business processes to save resources and improve process execution. ![]() Anomalous traces diminish the event log’s quality due to bad execution or security issues, for instance. ![]()
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