为了提升高频时序数据的存储效率和提取性能,结合应用数据的特点和时序数据库(time series database,TSDB)的编码方法,对棒线材连轧过程数据编码进行了联合优化。首先,为了优化应用端,针对棒线材连轧过程数据的结构特点和应用特点,建立...为了提升高频时序数据的存储效率和提取性能,结合应用数据的特点和时序数据库(time series database,TSDB)的编码方法,对棒线材连轧过程数据编码进行了联合优化。首先,为了优化应用端,针对棒线材连轧过程数据的结构特点和应用特点,建立了基于工作模式的复合信源模型;然后,为了优化TSDB端,针对在InfluxDB存储复合信源数据时定长划分数据块引起的时间戳编码冗余问题,提出了基于变长数据块的编码优化方法;最后,为了保证优化后系统的稳定性,提出了重写时间结构合并树(time-structured merge-tree,TSM)文件的非源码改造方案。使用实际轧机的棒材连轧过程数据进行测试,测试结果表明,经过编码优化后,TSM文件的总编码长度和时间戳的编码长度分别降低了37.6%和91.3%,所提方法不仅提升了数据存储效率,而且大幅度提高了数据提取性能。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
文摘为了提升高频时序数据的存储效率和提取性能,结合应用数据的特点和时序数据库(time series database,TSDB)的编码方法,对棒线材连轧过程数据编码进行了联合优化。首先,为了优化应用端,针对棒线材连轧过程数据的结构特点和应用特点,建立了基于工作模式的复合信源模型;然后,为了优化TSDB端,针对在InfluxDB存储复合信源数据时定长划分数据块引起的时间戳编码冗余问题,提出了基于变长数据块的编码优化方法;最后,为了保证优化后系统的稳定性,提出了重写时间结构合并树(time-structured merge-tree,TSM)文件的非源码改造方案。使用实际轧机的棒材连轧过程数据进行测试,测试结果表明,经过编码优化后,TSM文件的总编码长度和时间戳的编码长度分别降低了37.6%和91.3%,所提方法不仅提升了数据存储效率,而且大幅度提高了数据提取性能。
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.
