標(biāo)題: Titlebook: Business Process Management Workshops; BPM 2023 Internation Jochen De Weerdt,Luise Pufahl Conference proceedings 2024 The Editor(s) (if app [打印本頁] 作者: intrinsic 時(shí)間: 2025-3-21 16:46
書目名稱Business Process Management Workshops影響因子(影響力)
書目名稱Business Process Management Workshops影響因子(影響力)學(xué)科排名
書目名稱Business Process Management Workshops網(wǎng)絡(luò)公開度
書目名稱Business Process Management Workshops網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Business Process Management Workshops被引頻次
書目名稱Business Process Management Workshops被引頻次學(xué)科排名
書目名稱Business Process Management Workshops年度引用
書目名稱Business Process Management Workshops年度引用學(xué)科排名
書目名稱Business Process Management Workshops讀者反饋
書目名稱Business Process Management Workshops讀者反饋學(xué)科排名
作者: leniency 時(shí)間: 2025-3-21 22:03 作者: climax 時(shí)間: 2025-3-22 00:36
Medienanalyse als Beobachtung und als Kritik view their operational processes from any perspective using a single source of truth. However, OCED is not suitable for low-level machine data that contain a mixture of continuous measurements (e.g., time series data describing position, temperature, force, speed, etc.) and discrete events. Therefo作者: Herpetologist 時(shí)間: 2025-3-22 06:28
1865-1348 Processing for Business Process Management (NLP4BPM 2023).? 1st International Workshop on Object-Centric Processes from A to Z (OBJECTS 2023).? 3rd Internation978-3-031-50973-5978-3-031-50974-2Series ISSN 1865-1348 Series E-ISSN 1865-1356 作者: Implicit 時(shí)間: 2025-3-22 11:14 作者: 現(xiàn)暈光 時(shí)間: 2025-3-22 14:42
An Experiment on?Transfer Learning for?Suffix Prediction on?Event Logs two sequential deep learning architectures (GPT and LSTM). Base models are trained on two public event logs and used as starting point for transfer learning on eight event logs from different domains. The experiments show that even with half of the available training budget and without using very l作者: 高度贊揚(yáng) 時(shí)間: 2025-3-22 19:41
ProtoNER: Few Shot Incremental Learning for?Named Entity Recognition Using Prototypical Networkse need to retain original training dataset for longer duration as well as data re-annotation which is very time consuming task, (2) No intermediate synthetic data generation which tends to add noise and results in model’s performance degradation, and (3) Hybrid loss function which allows model to re作者: 持久 時(shí)間: 2025-3-22 21:47 作者: 新奇 時(shí)間: 2025-3-23 03:07
https://doi.org/10.1007/978-3-031-50974-2business process management; business process modeling; business process monitoring; business process o作者: BLA 時(shí)間: 2025-3-23 08:48 作者: Harass 時(shí)間: 2025-3-23 13:23
Lecture Notes in Business Information Processinghttp://image.papertrans.cn/b/image/192353.jpg作者: INCUR 時(shí)間: 2025-3-23 15:15
Joachim R. H?flich,Maren Hartmannsibilities may justify deviations. In these cases, we consider deviations as correct behaviors rather than errors. On the other hand, responsibilities can either be met or neglected in the execution trace. Thus, we prefer alignments where neglected responsibilities are minimized..The paper proposes 作者: Estimable 時(shí)間: 2025-3-23 20:55 作者: Lumbar-Stenosis 時(shí)間: 2025-3-23 23:03
https://doi.org/10.1007/978-3-322-92573-2e been improving the prediction accuracy for this suffix prediction task. Training such models with many parameters on large event logs requires expensive hardware and is often time consuming. Transfer learning addresses this issue by starting from a pre-trained model to be used as starting point fo作者: ALB 時(shí)間: 2025-3-24 05:26
https://doi.org/10.1007/978-3-322-95690-3he development of foundation models for other domains and data modalities (e.g., images and code). In this paper, we argue that business process data has unique characteristics that warrant the creation of a new class of foundation models to handle tasks like activity prediction, process optimizatio作者: Clumsy 時(shí)間: 2025-3-24 07:17
https://doi.org/10.1007/978-3-322-95690-3ains, such as the loan application process and the traffic fine process. However, care processes tend to be more dynamic and complex. For example, at any stage of a care process, a multitude of actions is possible. In this paper, we follow the reinforcement approach and train a Markov decision proce作者: 新陳代謝 時(shí)間: 2025-3-24 13:54
https://doi.org/10.1007/978-3-531-92218-8anding and data extraction domain. Several transformer based models such as LayoutLMv2 [.], LayoutLMv3 [.], and LiLT [.] have emerged achieving state of the art results. However, addition of even a single new class to the existing model requires (a) re-annotation of entire training dataset to includ作者: Ledger 時(shí)間: 2025-3-24 18:27
Medienanalyse als Beobachtung und als Kritikg at cross-domain collaboration in production while exploiting semantically adequate and context-aware data at different levels of granularity. The . (IoT), in the context of production also referred to as . or the Industrial Internet of Things, provides a wide range of data assets. However, these a作者: Headstrong 時(shí)間: 2025-3-24 20:03
Sinnstrukturen der Medienkommunikationse systems, thus allowing companies to analyze the data and gain valuable insights into user-product interactions. To analyze the underlying behavior recorded in data, process-mining techniques can be used. However, to apply process mining, the low-level measurements have to be transformed into an e作者: Inelasticity 時(shí)間: 2025-3-24 23:58 作者: 有節(jié)制 時(shí)間: 2025-3-25 03:39 作者: 脫落 時(shí)間: 2025-3-25 08:49 作者: BANAL 時(shí)間: 2025-3-25 13:47 作者: 衰老 時(shí)間: 2025-3-25 18:30
,Die Sucht nach der ?ffentlichkeit,been proposed in research and practice to partially or fully automate the creation of business process models. We conducted a systematic literature review (LR) on automatic generation of business process models. The LR demonstrates that there exist various approaches for automated generation of busi作者: Legion 時(shí)間: 2025-3-25 20:16
https://doi.org/10.1007/978-3-8349-8566-8omplex process models. Event abstraction addresses this issue by transforming low-level event logs into abstracted event logs, enabling the derivation of business-level process models. However, practitioners often struggle to choose suitable event abstraction methods. This is primarily due to the la作者: 你不公正 時(shí)間: 2025-3-26 03:59
,Die Sucht nach der ?ffentlichkeit,s at which each event is recorded. Time-aware process mining is a growing subfield of research, and as tools that seek to discover timing-related properties in processes develop, so does the need for conformance-checking techniques that can tackle time constraints and provide insightful quality meas作者: 多節(jié) 時(shí)間: 2025-3-26 07:32
Business Process Management Workshops978-3-031-50974-2Series ISSN 1865-1348 Series E-ISSN 1865-1356 作者: 歡笑 時(shí)間: 2025-3-26 12:32 作者: Intruder 時(shí)間: 2025-3-26 14:20
A Discussion on?Generalization in?Next-Activity Predictionp learning techniques and evaluate their prediction performance using publicly available event logs. This paper presents empirical evidence that calls into question the effectiveness of these current evaluation approaches. We show that there is an enormous amount of example leakage in all of the com作者: buoyant 時(shí)間: 2025-3-26 17:53 作者: 金哥占卜者 時(shí)間: 2025-3-26 22:59 作者: HAUNT 時(shí)間: 2025-3-27 01:36
Using Reinforcement Learning to?Optimize Responses in?Care Processes: A Case Study on?Aggression Incains, such as the loan application process and the traffic fine process. However, care processes tend to be more dynamic and complex. For example, at any stage of a care process, a multitude of actions is possible. In this paper, we follow the reinforcement approach and train a Markov decision proce作者: 察覺 時(shí)間: 2025-3-27 07:06
ProtoNER: Few Shot Incremental Learning for?Named Entity Recognition Using Prototypical Networksanding and data extraction domain. Several transformer based models such as LayoutLMv2 [.], LayoutLMv3 [.], and LiLT [.] have emerged achieving state of the art results. However, addition of even a single new class to the existing model requires (a) re-annotation of entire training dataset to includ作者: Reverie 時(shí)間: 2025-3-27 11:49
Experiences from the Internet-of-Production: Using “Data-Models-in-the-Middle” to?Fight Complexity ag at cross-domain collaboration in production while exploiting semantically adequate and context-aware data at different levels of granularity. The . (IoT), in the context of production also referred to as . or the Industrial Internet of Things, provides a wide range of data assets. However, these a作者: 宇宙你 時(shí)間: 2025-3-27 15:50 作者: Curmudgeon 時(shí)間: 2025-3-27 19:16
An Object-Centric Approach to?Handling Concurrency in?IoT-Aware Processesnd activities that involve physical resources. Traditional approaches to handling concurrency in BPM systems are not suitable for automating IoT-aware processes due to novel challenges raised by the IoT. We propose to handle concurrency in IoT based on object-centric processes implemented in the PHI作者: 負(fù)擔(dān) 時(shí)間: 2025-3-27 23:07 作者: 畸形 時(shí)間: 2025-3-28 02:54 作者: 發(fā)炎 時(shí)間: 2025-3-28 08:49 作者: Generalize 時(shí)間: 2025-3-28 10:46 作者: 疏遠(yuǎn)天際 時(shí)間: 2025-3-28 14:53
Navigating Event Abstraction in Process Mining: A Comprehensive Analysis of Sub-problems, Data, and omplex process models. Event abstraction addresses this issue by transforming low-level event logs into abstracted event logs, enabling the derivation of business-level process models. However, practitioners often struggle to choose suitable event abstraction methods. This is primarily due to the la作者: contradict 時(shí)間: 2025-3-28 20:57
Timed Alignments with Mixed Movess at which each event is recorded. Time-aware process mining is a growing subfield of research, and as tools that seek to discover timing-related properties in processes develop, so does the need for conformance-checking techniques that can tackle time constraints and provide insightful quality meas作者: 有其法作用 時(shí)間: 2025-3-29 01:15
Joachim R. H?flich,Maren Hartmanna formal framework for responsibilities in a process model, including the definition of cost functions to determine optimal alignments. It also outlines a branch-and-bound algorithm for their computation.作者: Coronation 時(shí)間: 2025-3-29 03:42 作者: gain631 時(shí)間: 2025-3-29 10:57 作者: Arresting 時(shí)間: 2025-3-29 13:10
https://doi.org/10.1007/978-3-322-95690-3havior. We used the reinforcement learning algorithms Q-learning and SARSA to find optimal policies. Results showed that the policies derived from these algorithms are similar to the most frequent actions currently used but provide the staff members with a few more options in certain situations.作者: 金桌活畫面 時(shí)間: 2025-3-29 18:40
Regionale Publikumszeitschriften,e to address concurrency in IoT-aware processes. The approach is demonstrated in a scenario from smart manufacturing. The results show that PHILharmonicFlows offers a flexible and comprehensible solution for coordinating concurrent activities in IoT settings with constrained physical resources.作者: 等級(jí)的上升 時(shí)間: 2025-3-29 21:55 作者: Accomplish 時(shí)間: 2025-3-30 03:30
,Die Sucht nach der ?ffentlichkeit,tic model generation, there is still considerable scope for improvement. New technologies are continually being developed to support automatic model generation. Additionally, the increasing digitalization of processes and organizations generates vast amounts of data that can be harnessed for modeling.作者: Perennial長(zhǎng)期的 時(shí)間: 2025-3-30 04:43
A Discussion on?Generalization in?Next-Activity Predictiondesigning robust evaluations requires a more profound conceptual engagement with the topic of next-activity prediction, and specifically with the notion of generalization to new data. To this end, we present various prediction scenarios that necessitate different types of generalization to guide future research.作者: craving 時(shí)間: 2025-3-30 09:57 作者: 收集 時(shí)間: 2025-3-30 15:58
Using Reinforcement Learning to?Optimize Responses in?Care Processes: A Case Study on?Aggression Inchavior. We used the reinforcement learning algorithms Q-learning and SARSA to find optimal policies. Results showed that the policies derived from these algorithms are similar to the most frequent actions currently used but provide the staff members with a few more options in certain situations.作者: micturition 時(shí)間: 2025-3-30 17:18
An Object-Centric Approach to?Handling Concurrency in?IoT-Aware Processese to address concurrency in IoT-aware processes. The approach is demonstrated in a scenario from smart manufacturing. The results show that PHILharmonicFlows offers a flexible and comprehensible solution for coordinating concurrent activities in IoT settings with constrained physical resources.作者: 注入 時(shí)間: 2025-3-31 00:23
An Event-Centric Metamodel for?IoT-Driven Process Monitoring and?Conformance Checking and conformance checking systems that is agnostic with respect to process characteristics such as level of automation, system support, and modeling paradigm. We demonstrate the applicability of the metamodel by instantiating it for processes represented by different modeling paradigms.作者: reaching 時(shí)間: 2025-3-31 02:51
State of?the?Art: Automatic Generation of?Business Process Modelstic model generation, there is still considerable scope for improvement. New technologies are continually being developed to support automatic model generation. Additionally, the increasing digitalization of processes and organizations generates vast amounts of data that can be harnessed for modeling.作者: FLING 時(shí)間: 2025-3-31 07:12
Conference proceedings 202423, in Utrecht, The Netherlands, during September 2023..Papers from the following workshops are included:.? 7th International Workshop on Artificial Intelligence for Business Process Management (AI4BPM 2023).? 7th International Workshop on Business Processes Meet Internet-of-Things (BP-Meet-IoT 2023作者: hankering 時(shí)間: 2025-3-31 11:51
