Business processes described by formal or semi-formal models are realized via information systems.Event logs generated from these systems are probably not consistent with the existing models due to insufficient design...Business processes described by formal or semi-formal models are realized via information systems.Event logs generated from these systems are probably not consistent with the existing models due to insufficient design of the information system or the system upgrade.By comparing an existing process model with event logs,we can detect inconsistencies called deviations,verify and extend the business process model,and accordingly improve the business process.In this paper,some abnormal activities in business processes are formally defined based on Petri nets.An efficient approach to detect deviations between the process model and event logs is proposed.Then,business process models are revised when abnormal activities exist.A clinical process in a healthcare information system is used as a case study to illustrate our work.Experimental results show the effectiveness and efficiency of the proposed approach.展开更多
Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental con...Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental concept drift,gradually alter the behavior or structure of processes,making their detection and localization a challenging task.Traditional process mining techniques frequently assume process stationarity and are limited in their ability to detect such drift,particularly from a control-flow perspective.The objective of this research is to develop an interpretable and robust framework capable of detecting and localizing incremental concept drift in event logs,with a specific emphasis on the structural evolution of control-flow semantics in processes.We propose DriftXMiner,a control-flow-aware hybrid framework that combines statistical,machine learning,and process model analysis techniques.The approach comprises three key components:(1)Cumulative Drift Scanner that tracks directional statistical deviations to detect early drift signals;(2)a Temporal Clustering and Drift-Aware Forest Ensemble(DAFE)to capture distributional and classification-level changes in process behavior;and(3)Petri net-based process model reconstruction,which enables the precise localization of structural drift using transition deviation metrics and replay fitness scores.Experimental validation on the BPI Challenge 2017 event log demonstrates that DriftXMiner effectively identifies and localizes gradual and incremental process drift over time.The framework achieves a detection accuracy of 92.5%,a localization precision of 90.3%,and an F1-score of 0.91,outperforming competitive baselines such as CUSUM+Histograms and ADWIN+Alpha Miner.Visual analyses further confirm that identified drift points align with transitions in control-flow models and behavioral cluster structures.DriftXMiner offers a novel and interpretable solution for incremental concept drift detection and localization in dynamic,process-aware systems.By integrating statistical signal accumulation,temporal behavior profiling,and structural process mining,the framework enables finegrained drift explanation and supports adaptive process intelligence in evolving environments.Its modular architecture supports extension to streaming data and real-time monitoring contexts.展开更多
Spectrogram analysis of seven natural gamma-ray logging of Member 1 of the Qingshankou Formation （K2qn^1） and Member 1 and 2 of the Nenjiang Formation （K2n^1-2） of Late Cretaceous age in the Songliao Basin reveals...Spectrogram analysis of seven natural gamma-ray logging of Member 1 of the Qingshankou Formation （K2qn^1） and Member 1 and 2 of the Nenjiang Formation （K2n^1-2） of Late Cretaceous age in the Songliao Basin reveals sedimentary cyclicities controlled by Milankovitch climate periodicities. The recognition of Milankovitch cycles allows estimation of an average accumulation rate of ～7.55- 8.62 cm/ka for the K2qn^1 sections, and ～6.69-10.16 cm/ka for the K2n^1-2 sections. Two marine transgression events occurred during the deposition of K2qn^1 and K2n^1-2 and their ages are at ～0.74- 1.10 Ma and ～2.38-4.84 Ma, respectively. Identification of Milankoviteh cycles from fine-grained deep lake sedimentary rocks in the Songliao Basin may provide great potential for high-resolution stratigraphic subdivisions and correlations.展开更多
1 IntroductionNowadays in China, there are more than six hundred million netizens [1]. On April 11, 2015, the nmnbet of simultaneous online users of the Chinese instant message application QQ reached two hundred milli...1 IntroductionNowadays in China, there are more than six hundred million netizens [1]. On April 11, 2015, the nmnbet of simultaneous online users of the Chinese instant message application QQ reached two hundred million [2]. The fast growth ol the lnternet pusnes me rapid development of information technology （IT） and communication technology （CT）. Many traditional IT service and CT equipment providers are facing the fusion of IT and CT in the age of digital transformation, and heading toward ICT enterprises. Large global ICT enterprises, such as Apple, Google, Microsoft, Amazon, Verizon, and AT＆T, have been contributing to the performance improvement of IT service and CT equipment.展开更多
业务流程合规性检查可以帮助企业及早发现潜在问题,保证业务流程的正常运行和安全性。提出一种基于改进BERT(Bidirectional Encoder Representations from Transformers)和轻量化卷积神经网络(CNN)的业务流程合规性检查方法。首先,根据...业务流程合规性检查可以帮助企业及早发现潜在问题,保证业务流程的正常运行和安全性。提出一种基于改进BERT(Bidirectional Encoder Representations from Transformers)和轻量化卷积神经网络(CNN)的业务流程合规性检查方法。首先,根据历史事件日志中的轨迹提取轨迹前缀,构造带拟合情况标记的数据集;其次,使用融合相对上下文关系的BERT模型完成轨迹特征向量的表示;最后,使用轻量化CNN模型构建合规性检查分类器,完成在线业务流程合规性检查,有效提高合规性检查的准确率。在5个真实事件日志数据集上进行实验,结果表明,该方法相比Word2Vec+CNN模型、Transformer模型、BERT分类模型在准确率方面有较大提升,且与传统BERT+CNN相比,所提方法的准确率最高可提升2.61%。展开更多
基金supported by the National Natural Science Foundation of China(61170078,61472228,61903229,61902222)the “Taishan Scholar” Construction Project of Shandong Province,China,the Natural Science Foundation of Shandong Province(ZR2018MF001)+1 种基金the Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents(2017RCJJ044)the Key Research and Development Program of Shandong Province(2018GGX101011)
文摘Business processes described by formal or semi-formal models are realized via information systems.Event logs generated from these systems are probably not consistent with the existing models due to insufficient design of the information system or the system upgrade.By comparing an existing process model with event logs,we can detect inconsistencies called deviations,verify and extend the business process model,and accordingly improve the business process.In this paper,some abnormal activities in business processes are formally defined based on Petri nets.An efficient approach to detect deviations between the process model and event logs is proposed.Then,business process models are revised when abnormal activities exist.A clinical process in a healthcare information system is used as a case study to illustrate our work.Experimental results show the effectiveness and efficiency of the proposed approach.
文摘Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental concept drift,gradually alter the behavior or structure of processes,making their detection and localization a challenging task.Traditional process mining techniques frequently assume process stationarity and are limited in their ability to detect such drift,particularly from a control-flow perspective.The objective of this research is to develop an interpretable and robust framework capable of detecting and localizing incremental concept drift in event logs,with a specific emphasis on the structural evolution of control-flow semantics in processes.We propose DriftXMiner,a control-flow-aware hybrid framework that combines statistical,machine learning,and process model analysis techniques.The approach comprises three key components:(1)Cumulative Drift Scanner that tracks directional statistical deviations to detect early drift signals;(2)a Temporal Clustering and Drift-Aware Forest Ensemble(DAFE)to capture distributional and classification-level changes in process behavior;and(3)Petri net-based process model reconstruction,which enables the precise localization of structural drift using transition deviation metrics and replay fitness scores.Experimental validation on the BPI Challenge 2017 event log demonstrates that DriftXMiner effectively identifies and localizes gradual and incremental process drift over time.The framework achieves a detection accuracy of 92.5%,a localization precision of 90.3%,and an F1-score of 0.91,outperforming competitive baselines such as CUSUM+Histograms and ADWIN+Alpha Miner.Visual analyses further confirm that identified drift points align with transitions in control-flow models and behavioral cluster structures.DriftXMiner offers a novel and interpretable solution for incremental concept drift detection and localization in dynamic,process-aware systems.By integrating statistical signal accumulation,temporal behavior profiling,and structural process mining,the framework enables finegrained drift explanation and supports adaptive process intelligence in evolving environments.Its modular architecture supports extension to streaming data and real-time monitoring contexts.
文摘Spectrogram analysis of seven natural gamma-ray logging of Member 1 of the Qingshankou Formation （K2qn^1） and Member 1 and 2 of the Nenjiang Formation （K2n^1-2） of Late Cretaceous age in the Songliao Basin reveals sedimentary cyclicities controlled by Milankovitch climate periodicities. The recognition of Milankovitch cycles allows estimation of an average accumulation rate of ～7.55- 8.62 cm/ka for the K2qn^1 sections, and ～6.69-10.16 cm/ka for the K2n^1-2 sections. Two marine transgression events occurred during the deposition of K2qn^1 and K2n^1-2 and their ages are at ～0.74- 1.10 Ma and ～2.38-4.84 Ma, respectively. Identification of Milankoviteh cycles from fine-grained deep lake sedimentary rocks in the Songliao Basin may provide great potential for high-resolution stratigraphic subdivisions and correlations.
基金supported in part by Ministry of Education/China Mobile joint research grant under Project No.5-10Nanjing University of Posts and Telecommunications under Grants No.NY214135 and NY215045
文摘1 IntroductionNowadays in China, there are more than six hundred million netizens [1]. On April 11, 2015, the nmnbet of simultaneous online users of the Chinese instant message application QQ reached two hundred million [2]. The fast growth ol the lnternet pusnes me rapid development of information technology （IT） and communication technology （CT）. Many traditional IT service and CT equipment providers are facing the fusion of IT and CT in the age of digital transformation, and heading toward ICT enterprises. Large global ICT enterprises, such as Apple, Google, Microsoft, Amazon, Verizon, and AT＆T, have been contributing to the performance improvement of IT service and CT equipment.
文摘业务流程合规性检查可以帮助企业及早发现潜在问题,保证业务流程的正常运行和安全性。提出一种基于改进BERT(Bidirectional Encoder Representations from Transformers)和轻量化卷积神经网络(CNN)的业务流程合规性检查方法。首先,根据历史事件日志中的轨迹提取轨迹前缀,构造带拟合情况标记的数据集;其次,使用融合相对上下文关系的BERT模型完成轨迹特征向量的表示;最后,使用轻量化CNN模型构建合规性检查分类器,完成在线业务流程合规性检查,有效提高合规性检查的准确率。在5个真实事件日志数据集上进行实验,结果表明,该方法相比Word2Vec+CNN模型、Transformer模型、BERT分类模型在准确率方面有较大提升,且与传统BERT+CNN相比,所提方法的准确率最高可提升2.61%。
