Efficient Online Computation of Business Process State From Trace Prefixes via N-Gram Indexing
David Chapela-Campa, Marlon Dumas

TL;DR
This paper introduces a novel n-gram indexing method for real-time process state computation from trace prefixes, significantly improving speed while maintaining accuracy in process mining tasks.
Contribution
It presents an efficient n-gram based indexing approach for constant-time process state computation from trace prefixes, outperforming traditional alignment methods in speed.
Findings
Achieves constant-time state computation for trace prefixes.
Maintains comparable accuracy to prefix-alignment methods.
Processes hundreds of thousands of traces per second.
Abstract
This paper addresses the following problem: Given a process model and an event log containing trace prefixes of ongoing cases of a process, map each case to its corresponding state (i.e., marking) in the model. This state computation operation is a building block of other process mining operations, such as log animation and short-term simulation. An approach to this state computation problem is to perform a token-based replay of each trace prefix against the model. However, when a trace prefix does not strictly follow the behavior of the process model, token replay may produce a state that is not reachable from the initial state of the process. An alternative approach is to first compute an alignment between the trace prefix of each ongoing case and the model, and then replay the aligned trace prefix. However, (prefix-)alignment is computationally expensive. This paper proposes a method…
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Taxonomy
TopicsBusiness Process Modeling and Analysis · Service-Oriented Architecture and Web Services
