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It predicts the future behavior of ongoing cases based on historical process execution records, thereby supporting rational resource allocation and intelligent process decision making. Existing methods based on deep learning techniques mainly rely on single\u2010case trends to predict the next activity or remaining time, neglecting the impact of global structural context on the prediction of currently ongoing cases. To address this limitation, this paper proposes a Predictive Business Process Monitoring framework based on Graph contrastive Learning and Attention Mechanism (BPMGLAM). The framework focuses on both direct and indirect relationships between activities to construct a global graph structure. Through a contrastive learning strategy between global graph\u2010related subgraphs and local graphs, it enhances the structural representation capability of ongoing cases and integrates multi\u2010level event attribute information to generate reliable predictions of future activities. Experiments conducted on multiple real\u2010world event logs demonstrate that the proposed method achieves superior prediction performance.<\/jats:p>","DOI":"10.1002\/cpe.70474","type":"journal-article","created":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T08:21:34Z","timestamp":1764922894000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>BPMGLAM<\/scp>\n                    : A Predictive Business Process Monitoring Framework Based on Graph Contrastive Learning and Attention Mechanisms"],"prefix":"10.1002","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-8704-6156","authenticated-orcid":false,"given":"Xinsheng","family":"Fang","sequence":"first","affiliation":[{"name":"College of Mathematics and Big Data Anhui University of Science and Technology  Huainan China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8531-7215","authenticated-orcid":false,"given":"Xianwen","family":"Fang","sequence":"additional","affiliation":[{"name":"College of Mathematics and Big Data Anhui University of Science and Technology  Huainan China"},{"name":"Anhui Province Engineering Laboratory for Big Data Analysis and Early Warning Technology of Coal Mine Safety  Huainan China"}]},{"given":"Ke","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Mathematics and Big Data Anhui University of Science and Technology  Huainan China"}]}],"member":"311","published-online":{"date-parts":[[2025,12,5]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-59536-8_30"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-37453-2_21"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-35166-3_25"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611976700.59"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975673.14"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.3233\/IDA-215755"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CBI.2017.46"},{"key":"e_1_2_12_9_1","unstructured":"Z. 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