{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,17]],"date-time":"2024-08-17T15:49:04Z","timestamp":1723909744981},"reference-count":17,"publisher":"World Scientific Pub Co Pte Ltd","issue":"11n12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Soft. Eng. Knowl. Eng."],"published-print":{"date-parts":[[2021,12]]},"abstract":" Remaining activity sequence prediction (i.e. activity suffix prediction) aims at recommending the most likely future behaviors for ongoing process instances (i.e. traces), which enables process managers to rationally allocate resources and detect process deviations in advance. Recently, techniques of neural networks have found promising applications in activity suffix prediction by training a prediction model for next activity and iteratively performing the model to achieve the whole sequence prediction. However, the iterative prediction accumulates the deviations of each iteration and the result also lacks interpretability. In this paper, we propose a novel method to predict activity suffixes from the perspective of control flow and data flow for ongoing traces, where process discovery and trace replay techniques are employed to simulate executions of traces under real conditions and Long Short-Term Memory (LSTM) is applied to characterize the correlation between executed information and future execution. Sequence matching between historical prefix traces and ongoing traces is performed based on the above information to select the optimal-matched (i.e. most similar) activity suffix for ongoing process instances. Experiments on real-life datasets demonstrate that the proposed method outperforms other methods. <\/jats:p>","DOI":"10.1142\/s0218194021400209","type":"journal-article","created":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T09:01:56Z","timestamp":1643101316000},"page":"1741-1760","source":"Crossref","is-referenced-by-count":2,"title":["Remaining Activity Sequence Prediction for Ongoing Process Instances"],"prefix":"10.1142","volume":"31","author":[{"given":"Xiaoxiao","family":"Sun","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, P. R. China"}]},{"given":"Yuke","family":"Ying","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, P. R. China"}]},{"given":"Siqing","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, P. 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