Know Your Streams: On the Conceptualization, Characterization, and Generation of Intentional Event Streams
This work addresses the problem of inadequate benchmarking for SPM algorithms in IoT and sensor-driven systems, though it is incremental as it builds on existing stream literature and conceptual foundations.
The paper tackles the challenge of evaluating Streaming Process Mining (SPM) algorithms by addressing the gap between real-world event streams and outdated evaluation practices, proposing a prototype generator called Stream of Intent that produces reproducible, intentional event streams for targeted benchmarking.
The shift toward IoT-enabled, sensor-driven systems has transformed how operational data is generated, favoring continuous, real-time event streams (ES) over static event logs. This evolution presents new challenges for Streaming Process Mining (SPM), which must cope with out-of-order events, concurrent activities, incomplete cases, and concept drifts. Yet, the evaluation of SPM algorithms remains rooted in outdated practices, relying on static logs or artificially streamified data that fail to reflect the complexities of real-world streams. To address this gap, we first perform a comprehensive review of data stream literature to identify stream characteristics currently not reflected in the SPM community. Next, we use this information to extend the conceptual foundation for ES. Finally, we propose Stream of Intent, a prototype generator to produce ES with specific features. Our evaluation shows excellence in producing reproducible, intentional ES for targeted benchmarking and adaptive algorithm development in SPM.