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Process executions in organizations generate a large variety of data. Processmining is a data-driven analytical approach for analyzing this data from abusiness process point of view. Online conformance checking deals with findingdiscrepancies between real-life and modeled process behavior on data streams.The current state-of-the-art output of online conformance checking is aprefix-alignment, which is used for pinpointing the exact deviations in termsof the trace and the model while accommodating a trace's unknown termination ina streaming setting. However, producing prefix-alignments entails a state spacesearch to find the shortest path from a common start state to a common endstate between the trace and the model. This is computationally expensive andmakes the method infeasible in an online setting. Previously, the trie datastructure has been shown to be efficient for constructing alignments, utilizinga proxy log representing the process model in a finite way. This paperintroduces a new approximate algorithm (IWS) on top of the trie for onlineconformance checking. The algorithm is shown to be fast, memory-efficient, andable to output both a prefix and a complete alignment event-by-event whilekeeping track of previously seen cases and their state. Comparative analysisagainst the current state-of-the-art algorithm for finding prefix-alignmentsshows that the IWS algorithm achieves, in some cases, an order of magnitudefaster execution time while having a smaller error cost. In extreme cases, theIWS finds prefix-alignments roughly three orders of magnitude faster than thecurrent state of the art. The IWS algorithm includes a discounted decay timesetting for efficient memory usage and a look-ahead limit for improvingcomputation time. Finally, the algorithm is stress tested for performance usinga simulation of high-traffic event streams.

I Will Survive: An Online Conformance Checking Algorithm Using Decay Time