🤖 AI Summary
Modern OLTP systems often suffer from frequent schema changes, missing primary/foreign keys, and fragmented execution traces, rendering traditional approaches—reliant on fixed schemas and manual modeling—costly and error-prone. This work proposes a fully automated pipeline that operates without predefined schemas by identifying quasi-key and timestamp columns, discovering inter-table relationships through statistical signals, and assembling and ordering events accordingly. To capture long-range dependencies across system events, the method incorporates a Temporal Convolutional Network (TCN). By eliminating dependence on ER diagrams, domain-specific templates, and stable schemas, the approach enables generalizable and scalable reconstruction of execution traces in dynamic information systems. Experimental results on TPC-H/E, synthetic, and real-world industrial datasets demonstrate 85% accuracy in event prediction and recovery of approximately 82% of true predecessor relationships, yielding high-fidelity process traces.
📝 Abstract
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.