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

2024-09-09

IEEE Transactions on Services Computing

AI Summary

In real-time process monitoring, rapidly inferring the exact marking state of a running case’s event prefix within a Petri net model remains challenging. This paper proposes an *n*-gram index-based method for constant-time state computation, eliminating traditional alignment and token replay. By preconstructing an *n*-gram index, encoding markings compactly, and employing efficient prefix matching, the approach directly maps observed prefixes to reachable markings—bypassing unreachable states and avoiding high computational overhead. Experiments demonstrate accuracy comparable to optimal prefix alignment while achieving throughput exceeding 100,000 traces per second. To our knowledge, this is the first method enabling millisecond-latency, high-accuracy, and scalable online state awareness. It establishes a real-time foundation for downstream tasks such as log animation and short-term simulation.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Intelligent Robots: State Estimation

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Security and Privacy: Large-scale security measurements

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 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 on the length of the trace using an index that represents states as <inline-formula><tex-math notation="LaTeX">$n$</tex-math><alternatives><mml:math><mml:mi>n</mml:mi></mml:math><inline-graphic xlink:href="chapelacampa-ieq1-3547235.gif"/></alternatives></inline-formula>-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.

Problem

• Efficiently compute process state from trace prefixes
• Avoid computationally expensive prefix-alignment methods
• Achieve high throughput with n-gram indexing

Innovation

• Uses n-gram indexing for state computation
• Computes process state in constant time
• Achieves high throughput with prefix-alignment accuracy