Traditional information systems (IS) engineering assumes stable schemas, explicit keys, and curated event logs. In modern OLTP environments, schemas drift, keys are sparse, and execution traces are dispersed across loosely connected tables, making manual process trace construction costly and error prone. We propose a schema-agnostic pipe-line that automatically reconstructs process execution traces directly from raw relational data. The pipeline (i) identifies columns that function like keys or timestamps, (ii) discovers table-to-table connections using statistical signals rather than predefined schemas, (iii) assembles and orders events for each case while accommodating multiple date fields, and (iv) learns likely ordering and flow relations across systems using a Temporal Convolutional Network which models long-range dependencies and patterns. Evaluations on TPC-H/E benchmarks, synthetic corpora, and a real industry dataset show that our pipeline reconstructs high-fidelity event traces and accurate trace orderings, correctly predicting the next event with 85% accuracy and recovering about 82% of ground-truth precedence relations. By eliminating dependence on predefined schemas, ER diagrams and domain templates, our work offers a generalizable and scalable pathway for automated reconstruction of execution behaviour in dynamic and continuously evolving IS environments.

翻译：摘要：传统信息系统工程依赖于稳定的模式、显式的主键以及精心编排的事件日志。而在现代在线事务处理（OLTP）环境中，模式会发生漂移，主键稀疏分布，执行轨迹分散在松散关联的数据表中，这使得人工构建过程轨迹既昂贵又易出错。本文提出了一种模式无关的流水线，可直接从原始关系数据中自动重建过程执行轨迹。该流水线（i）识别具备键或时间戳功能的列；（ii）基于统计信号而非预定义模式发现表间关联；（iii）为每个案例组装并排序事件，同时兼容多日期字段；（iv）利用时序卷积网络学习跨系统的可能排序与流关系，该网络能够建模长距离依赖与模式。在TPC-H/E基准、合成语料库及真实工业数据集上的评估表明：本流水线可重建高保真事件轨迹与准确的轨迹排序，下一事件预测准确率达85%，并能恢复约82%的真实前序关系。由于消除了对预定义模式、实体关系图及领域模板的依赖，本工作为动态且持续演进的信息系统环境中执行行为的自动化重建提供了可泛化、可扩展的路径。