arXiv subjects

Andrea Maldonado

Publications and source records attributed to Andrea Maldonado.

4 recordsLinked to original sources

arXiv2026

The PM-EdgeMap: Towards Real-Time Process Mining on the Edge-Cloud Continuum

Smart factories are evolving into Cyber-Physical Systems (CPS), demanding increased autonomy. This necessitates real-time decision making, facilitated by insights derived from sensor data. Process mining offers a valuable approach to gain such insights and guide actions. The edge computing paradigm supports this real-time requirement by enabling network communication between sensors and leveraging nearby computing resources. This paper investigates the implications of performing real-time process mining algorithms on the edge. Within this paper, we first propose a formalism to describe relevant datasets and the computing topology. We then evaluate the edge computing approach through a case study involving an edge-based conformance checking algorithm. The results demonstrate the feasibility and benefits of edge-based real-time process mining for enhanced autonomous control in smart factories.

cs.DC↗

arXiv2026

Know Your Streams: On the Conceptualization, Characterization, and Generation of Intentional Event Streams

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 Yet, the evaluation of SPM algorithms remains rooted in outdated practices, relying on static logs or artificially streamified data that fail to reflect the complexities of real-world streams.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.

cs.DB↗

arXiv2026

SHAining on Process Mining: Explaining Event Log Characteristics Impact on Algorithms

Process mining aims to extract and analyze insights from event logs, yet algorithm metric results vary widely depending on structural event log characteristics. Existing work often evaluates algorithms on a fixed set of real-world event logs but lacks a systematic analysis of how event log characteristics impact algorithms individually. Moreover, since event logs are generated from processes, where characteristics co-occur, we focus on associational rather than causal effects to assess how strong the overlapping individual characteristic affects evaluation metrics without assuming isolated causal effects, a factor often neglected by prior work. We introduce SHAining, the first approach to quantify the marginal contribution of varying event log characteristics to process mining algorithms' metrics. Using process discovery as a downstream task, we analyze over 22,000 event logs covering a wide span of characteristics to uncover which affect algorithms across metrics (e.g., fitness, precision, complexity) the most. Furthermore, we offer novel insights about how the value of event log characteristics correlates with their contributed impact, assessing the algorithm's robustness.

cs.LG↗

arXiv2026

AVOCADO: The Streaming Process Mining Challenge

Streaming process mining deals with the real-time analysis of streaming data. Event streams require algorithms capable of processing data incrementally. To systematically address the complexities of this domain, we propose AVOCADO, a standardized challenge framework that provides clear structural divisions: separating the concept and instantiation layers of challenges in streaming process mining for algorithm evaluation. The AVOCADO evaluates algorithms on streaming-specific metrics like accuracy, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Processing Latency, and robustness. This initiative seeks to foster innovation and community-driven discussions to advance the field of streaming process mining. We present this framework as a foundation and invite the community to contribute to its evolution by suggesting new challenges, such as integrating metrics for system throughput and memory consumption, and expanding the scope to address real-world stream complexities like out-of-order event arrival.

cs.DB↗