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Titlebook: Business Process Management Forum; BPM 2024 Forum, Krak Andrea Marrella,Manuel Resinas,Michael Rosemann Conference proceedings 2024 The Edi

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樓主: 氣泡
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發(fā)表于 2025-3-23 10:26:02 | 只看該作者
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發(fā)表于 2025-3-23 14:46:53 | 只看該作者
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發(fā)表于 2025-3-23 20:03:36 | 只看該作者
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發(fā)表于 2025-3-23 22:57:51 | 只看該作者
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發(fā)表于 2025-3-24 04:06:51 | 只看該作者
A Framework to?Support the?Validation of?Process Mining Inquiriesvelop expectations about what the results might be. Based on these expectations, they then make inquiries and translate them into queries against the log. However, during the analysis, analysts need to evaluate and compare the results of their queries to be able to validate them against their expect
16#
發(fā)表于 2025-3-24 08:53:51 | 只看該作者
Microservices-Aware Business Process Modellingre and processes around business outcomes to reap MSA’s benefits. Also, MSA requires the domain model for each microservice to be minimal and avoid coupling with other microservices’ domain entities. However, such coupling might already occur during the design of the business process and then propag
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發(fā)表于 2025-3-24 13:57:42 | 只看該作者
18#
發(fā)表于 2025-3-24 16:00:21 | 只看該作者
T. H. Crook,B. A. Johnson,G. J. Larrabee object-oriented databases storing process data..This paper introduces a characteristics framework from three perspectives originating from studying seminal works in process mining (what should be analyzed), object-centric process modeling (how it should be modeled), and database storage (how it sho
19#
發(fā)表于 2025-3-24 22:25:46 | 只看該作者
Evaluating Antidepressants and Anxiolyticserent real-life processes. Further, we investigate how predictable next activities in event sequences are. Our study indicates that ordering the events as executed does not necessarily increase the entropy rates of the process. We discuss these findings and their implications for future research.
20#
發(fā)表于 2025-3-25 01:24:31 | 只看該作者
Otto Benkert,Wolfgang Maier,Karl Rickelstion of temporal tiers, that takes process precedence into account, without the need for heuristic conflict resolution between process discovery and causal discovery. We demonstrate the effectiveness of our approach through experiments using synthetically generated data, and show a proof-of-concept
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