Titlebook: Intelligent Information Systems; CAiSE Forum 2021, Me Selmin Nurcan,Axel Korthaus Conference proceedings 2021 Springer Nature Switzerland A

戰(zhàn)神

書目名稱Intelligent Information Systems影響因子(影響力)




書目名稱Intelligent Information Systems影響因子(影響力)學(xué)科排名




書目名稱Intelligent Information Systems網(wǎng)絡(luò)公開度




書目名稱Intelligent Information Systems網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Intelligent Information Systems被引頻次




書目名稱Intelligent Information Systems被引頻次學(xué)科排名




書目名稱Intelligent Information Systems年度引用




書目名稱Intelligent Information Systems年度引用學(xué)科排名




書目名稱Intelligent Information Systems讀者反饋




書目名稱Intelligent Information Systems讀者反饋學(xué)科排名

牛的細微差別

Benzodiazepines

先行

Sensor Data Stream Selection and Aggregation for the Ex Post Discovery of Impact Factors on Process ed by process execution data, including (external) sensor data streams, e.g., indicating an overheating machine. Challenges are to select the “right” sensors –possibly a multitude of sensors is available– and to specify how the sensor data streams are aggregated and used to calculate the impact on t

狂熱文化

不知疲倦

ISGE: A Conceptual Model-Based Method to Correctly Manage Genome Datathe diagnosis, treatment, and prevention of disease, providing long-term benefits and a transformative impact on personal health. It also has wealth and productivity implications. However, genomics is one of the most complex and data-intensive domains. Fuzzy definitions, data diversity, data heterog

發(fā)電機

calamity

Evaluating Fidelity of Explainable Methods for Predictive Process Analyticsiques have been used to increase the accuracy of predictions, the resulting predictive models lack transparency. Explainable machine learning methods can be used to interpret black-box models. However, it is unclear how fit for purpose these methods are in explaining process predictive models. In th

lethargy

Data-Driven Process Performance Measurement and Prediction: A Process-Tree-Based Approach by distilling historical process knowledge based on recorded historical event data. Few techniques focus on the prediction of process performance after process redesign. This paper proposes a foundational framework for a data-driven business process redesign approach, allowing the user to investiga

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