{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/binet-multi-perspective-business-process","title":"BINet: Multi-perspective Business Process Anomaly Classification","arxiv_id":"1902.03155","date":"2019-02-08","proceeding":null,"authors":["Timo Nolle","Stefan Luettgen","Alexander Seeliger","Max Mühlhäuser"],"abstract":"In this paper, we introduce BINet, a neural network architecture for\nreal-time multi-perspective anomaly detection in business process event logs.\nBINet is designed to handle both the control flow and the data perspective of a\nbusiness process. Additionally, we propose a set of heuristics for setting the\nthreshold of an anomaly detection algorithm automatically. We demonstrate that\nBINet can be used to detect anomalies in event logs not only on a case level\nbut also on event attribute level. Finally, we demonstrate that a simple set of\nrules can be used to utilize the output of BINet for anomaly classification. We\ncompare BINet to eight other state-of-the-art anomaly detection algorithms and\nevaluate their performance on an elaborate data corpus of 29 synthetic and 15\nreal-life event logs. BINet outperforms all other methods both on the synthetic\nas well as on the real-life datasets.","url_abs":"http://arxiv.org/abs/1902.03155v1","url_pdf":"http://arxiv.org/pdf/1902.03155v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"binet-multi-perspective-business-process","repo_url":"https://github.com/tnolle/binet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"binet-multi-perspective-business-process","repo_url":"https://github.com/StephenPauwels/edbn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"binet-multi-perspective-business-process","repo_url":"https://github.com/StephenPauwels/edbn_ecmlpkdd","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"anomaly-classification","task_name":"Anomaly Classification"},{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}