PerSpect: An Interactive Tool for Performance Pattern Mining
Moritz Schlegelmilch, Piotr Zaniewski, Volodymyr Diemieniev, Jasper Saathoff, Tsung-Hao Huang, Gyunam Park
June, 2026Abstract
Complex processes can be difficult to understand from event data. Traditional process models effectively visualize control flows, but lack temporal context for performance analysis. To address this, the Performance Spectrum was introduced to map the dynamics of all steps and cases over time. However, its manual interpretation is challenging due to the overlapping patterns and noise. In this tool demonstration paper, we present PerSpect, an interactive, web-based user interface that significantly enhances the original Performance Spectrum approach. To facilitate interactive analysis, the tool automates the targeted detection of established operational behaviors (e.g., batching and FIFO), alongside advanced filtering capabilities and a side-by-side comparison function for evaluating the same segment across different process variants.
Publication
Intelligent Information Systems - CAiSE 2026 Forum and Doctoral Consortium, Verona, Italy, June 8-12, 2026, Proceedings
Practical problem
Process analysts who want to know why cases are slow usually look at a discovered process model, but such models show only the order of activities and nothing about time: they cannot reveal that cases pile up for months and are then released in one burst, or that work is handled strictly first-in-first-out in one period and chaotically in another. The Performance Spectrum visualization was invented to expose exactly these temporal patterns by drawing every case’s hop between two activities on a timeline. In practice, however, the picture of a real log is a dense tangle of overlapping lines, so distinguishing batches from noise requires an expert staring at the screen, the existing ProM plugin and commercial implementation only highlight selections, and nothing lets the analyst extract the cases behind a pattern for further study.
Research questions
- How can the detection of operational patterns such as simultaneous, first-in-first-out, and non-first-in-first-out batching in a Performance Spectrum be automated instead of left to visual inspection?
- How can analysts isolate, compare, and export the cases behind a temporal pattern across process variants within an interactive workflow?
Contributions
PerSpect, an open-source web-based tool (GitHub) that reimplements the Performance Spectrum as the foundation of a pattern-oriented analysis workflow.- Automated batch detection in a spectrum segment via DBSCAN clustering on start, end, or both timestamps, with user-set epsilon and minimum-sample parameters and a FIFO versus non-FIFO distinction.
- Global and segment-specific filters (variant, date range, duration quartile, interactive case selection) whose effects propagate across all displayed spectra, with tracked filter history.
- Side-by-side comparison of named spectrum configurations, process and batch statistics, frequency diagrams, and export of the filtered cases as an XES event log.
- A client-server architecture with a FastAPI backend, SQLite metadata store, in-memory LRU caching of logs, and a Vue single-page frontend.
Key findings
- This is a tool demonstration paper; it reports no controlled experiments, only a use case and a demonstration video.
- On the Road Traffic Fine Management log, the batch filter on the segment Add Penalty to Send for Credit Collection shows that over 97% of cases follow a sequential batching pattern formed by only 11 batches with an average interval of 410 days, i.e. a long-term backlog cleared in rare bursts.
- Setting epsilon to 0 minutes recovers simultaneous batches automatically, and overlapping start-batches and end-batches in the same segment can be separated and displayed individually.