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
[背景]现有研究提示镉暴露与心血管疾病(CVD)发生存在关联,但目前的流行病学结论尚不一致。[目的]通过meta分析,系统评估镉暴露与CVD之间的关系。[方法]系统检索PubMed、Cochrane Library、Web of Science、知网、万方和中国生物医学数...[背景]现有研究提示镉暴露与心血管疾病(CVD)发生存在关联,但目前的流行病学结论尚不一致。[目的]通过meta分析,系统评估镉暴露与CVD之间的关系。[方法]系统检索PubMed、Cochrane Library、Web of Science、知网、万方和中国生物医学数据库六大库,从中收集建库至2024年7月30日发表的镉暴露与人群患CVD关系的观察性研究。在遵循纳入与排除标准的基础上,对检索到的文献进行系统筛选,并提取纳入研究的基本信息,包括研究对象的基本信息、研究的结局和数据结果。本研究使用《纽卡斯尔-渥太华评分表》与美国卫生保健质量和研究机构推荐的评价横断面研究的11个条目进行文献质量评价。运用Stata16.0软件对数据进行meta分析、亚组分析、敏感性分析及发表偏倚评估。[结果]共纳入15篇文献(18项研究数据),文献质量均为中等及以上,其中CVD病例10593例,对照组86801人。meta分析结果显示:CVD病例组[标准化均数差(SMD)=0.44,95%置信区间(95%CI):0.30~0.58]镉暴露水平总体高于对照组(P<0.05)。亚组分析结果显示:来自美洲(SMD=0.46,95%CI:0.29~0.63)和欧洲(SMD=0.14,95%CI:0.09~0.19)的CVD病例组镉暴露水平均高于对照组(P<0.05);CVD病例组的血镉(SMD=0.52,95%CI:0.31~0.73)和尿镉(SMD=0.34,95%CI:0.16~0.52)水平均高于对照组(P<0.05);横断面研究中CVD病例组(SMD=0.34,95%CI:0.23~0.46)镉暴露水平高于对照组(P<0.05)。敏感性分析显示meta分析方法稳健,发表偏倚评估显示不存在发表偏倚。[结论]meta分析表明,镉暴露可能会增加心血管疾病的发生风险,提示应进一步控制人群镉暴露的范围和剂量。展开更多
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[背景]现有研究提示镉暴露与心血管疾病(CVD)发生存在关联,但目前的流行病学结论尚不一致。[目的]通过meta分析,系统评估镉暴露与CVD之间的关系。[方法]系统检索PubMed、Cochrane Library、Web of Science、知网、万方和中国生物医学数据库六大库,从中收集建库至2024年7月30日发表的镉暴露与人群患CVD关系的观察性研究。在遵循纳入与排除标准的基础上,对检索到的文献进行系统筛选,并提取纳入研究的基本信息,包括研究对象的基本信息、研究的结局和数据结果。本研究使用《纽卡斯尔-渥太华评分表》与美国卫生保健质量和研究机构推荐的评价横断面研究的11个条目进行文献质量评价。运用Stata16.0软件对数据进行meta分析、亚组分析、敏感性分析及发表偏倚评估。[结果]共纳入15篇文献(18项研究数据),文献质量均为中等及以上,其中CVD病例10593例,对照组86801人。meta分析结果显示:CVD病例组[标准化均数差(SMD)=0.44,95%置信区间(95%CI):0.30~0.58]镉暴露水平总体高于对照组(P<0.05)。亚组分析结果显示:来自美洲(SMD=0.46,95%CI:0.29~0.63)和欧洲(SMD=0.14,95%CI:0.09~0.19)的CVD病例组镉暴露水平均高于对照组(P<0.05);CVD病例组的血镉(SMD=0.52,95%CI:0.31~0.73)和尿镉(SMD=0.34,95%CI:0.16~0.52)水平均高于对照组(P<0.05);横断面研究中CVD病例组(SMD=0.34,95%CI:0.23~0.46)镉暴露水平高于对照组(P<0.05)。敏感性分析显示meta分析方法稳健,发表偏倚评估显示不存在发表偏倚。[结论]meta分析表明,镉暴露可能会增加心血管疾病的发生风险,提示应进一步控制人群镉暴露的范围和剂量。
