Titlebook: Business Process Management; 19th International C Artem Polyvyanyy,Moe Thandar Wynn,Manfred Reichert Conference proceedings 2021 Springer N

polysomnography

書(shū)目名稱Business Process Management影響因子(影響力)




書(shū)目名稱Business Process Management影響因子(影響力)學(xué)科排名




書(shū)目名稱Business Process Management網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Business Process Management網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Business Process Management被引頻次




書(shū)目名稱Business Process Management被引頻次學(xué)科排名




書(shū)目名稱Business Process Management年度引用




書(shū)目名稱Business Process Management年度引用學(xué)科排名




書(shū)目名稱Business Process Management讀者反饋




書(shū)目名稱Business Process Management讀者反饋學(xué)科排名

DEFER

Cognitive Effectiveness of Representations for Process Miningangle. We observe this to be a problem, because it hardly takes into account how effective these representations are for users and for which analysis tasks they are useful. We aim to rectify this research problem by developing a cognitive perspective for researching process mining. To this end, we b

Aggressive

RuM: Declarative Process Mining, Distilledlly predetermined, but can strongly depend on dynamic decisions made based on the current circumstances of a case. A common example is the adaptation of a standard treatment process to the needs of a specific patient. However, high flexibility does not mean chaos: certain key process rules still del

Bucket

Applications of Automated Planning for Business Process Managementhe same theme at the 19th International Conference on Business Process Management (BPM 2021). We hope that this report is able to quickly onboard newcomers into this field with a broad overview of the associated challenges and opportunities, as well as provide established practitioners in the field

In-Situ

顯而易見(jiàn)

Weighing the Pros and Cons: Process Discovery with Negative Examplesamples to also be available in industry, hence we propose to treat process discovery as a . problem. This approach opens the door to many well-established methods and metrics from machine learning, in particular to improve the distinction between what should and should not be allowed by the output m

墊子

A Method for Debugging Process Discovery Pipelines to Analyze the Consistency of Model Propertiesss behavior and to infer actionable insights. To this end, analysts configure discovery pipelines in which logs are filtered, enriched, abstracted, and process models are derived. While pipeline operations are necessary to manage log imperfections and complexity, they might, however, influence the n

linear

Extracting Decision Models from Textual Descriptions of Processes DMN have appeared in recent years, to serve as a central resource for synchronizing the people and systems with respect to decisions. However, the modeling of DMN specifications can be tedious and error-prone, hampering its adoption in practice. This paper presents a technique to automatically obta

Largess

Robust and Generalizable Predictive Models for Business Processesme, remaining time to completion, or the next activity of a running process can be crucial to provide decision information and enable timely intervention by case managers. These models fundamentally assume that the process logs used for training and inference follow the same data distribution and pa

軌道

Incremental Predictive Process Monitoring: The Next Activity Caseplication of the learned model during the test phase. Real-life processes, however, are often dynamic and prone to changes over time. Therefore, all state-of-the-art methods need regular retraining on new data to be kept up?to date. It is, however, not straightforward to determine when to retrain no