Dogan, OnurDe Leoni, Massimiliano2025-03-202025-03-202024978-3-031-56106-1978-3-031-56107-81865-13481865-1356https://doi.org/10.1007/978-3-031-56107-8_5https://hdl.handle.net/20.500.14034/25325th International Conference on Process Mining (ICPM) -- OCT 23-27, 2023 -- Rome, ITALYProcess mining utilizes event data to gain insights into the execution of processes. While techniques are valuable, their effectiveness may be hindered when dealing with highly complex processes that have a vast number of variants. Additionally, because the recorded events in information systems are at a low-level, process mining techniques may not align with the higher-level concepts understood at the business level. Without abstracting event sequences to higher-level concepts, the outcomes of process mining, such as discovering a model, can become overly complex and challenging to interpret, rendering them less useful. Some research has been conducted on event abstraction, often requiring significant domain knowledge that may not be readily accessible. Alternatively, unsupervised abstraction techniques may yield less accurate results and rely on stronger assumptions. This paper introduces a technique that addresses the challenge of limited domain knowledge by utilizing a straightforward approach. The technique involves dividing traces into batch sessions, taking into account relationships between subsequent events. Each session is then abstracted as a single high-level activity execution. This abstraction process utilizes a combination of automatic clustering and visualization methods. The proposed technique was evaluated using a randomly generated process model with high variability. The results demonstrate the significant advantages of the proposed abstraction in effectively communicating accurate knowledge to stakeholders.eninfo:eu-repo/semantics/closedAccessEvent log abstractionParallel relationshipsClusteringVisual analyticsProcess discoveryConference Object10.1007/978-3-031-56107-8_55035869N/AWOS:0012678490000062-s2.0-85192168138Q3