Browsing by Author "Tommassini, Riccardo"

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    I Will Survive: An Event-driven Conformance Checking Approach Over Process Streams
    (ACM DIGITAL LIBRARY, 2023) Raun, Kristo; Tommassini, Riccardo; Awad, Ahmed
    Online conformance checking deals with finding discrepancies be tween real-life and modeled behavior on data streams. The current state-of-the-art output of online conformance checking is a prefix alignment, which is used for pinpointing the exact deviations in terms of the trace and the model while accommodating a trace’s unknown termination in an online setting. Current methods for producing prefix-alignments are computationally expensive and hinder the applicability in real-life settings. This paper introduces a new approximate algorithm – I Will Survive (IWS). The algorithm utilizes the trie data structure to improve the calculation speed, while remaining memory-efficient. Comparative analysis on real-life and synthetic datasets shows that the IWS algorithm can achieve an order of magnitude faster execution time while having a smaller error cost, compared to the current state of the art. In extreme cases, the IWS finds prefix alignments roughly three orders of magnitude faster than previous approximate methods. The IWS algorithm includes a discounted decay time setting for more efficient memory usage and a look ahead limit for improving computation time. Finally, the algorithm is stress tested for performance using a simulation of high-traffic event streams.