Efficient Online Computation of Business Process State From Trace Prefixes via N-Gram Indexing

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 that, given a trace prefix of an ongoing case, computes its state in constant time using an index that represents states as n-grams. An empirical evaluation shows that the proposed approach has an accuracy comparable to that of the prefix-alignment approach, while achieving a throughput of hundreds of thousands of traces per second.