Heuristics Miners for Streaming Event Data

TL;DR

This paper introduces a framework for adapting the Heuristics Miner algorithm to streaming event data, enabling real-time process discovery in evolving environments where storing all data is infeasible.

Contribution

It proposes a novel framework for streaming process mining and develops stream-aware versions of the Heuristics Miner algorithm, with implementation and experimental validation.

Findings

Effective stream-aware Heuristics Miner variants are developed.

Experimental results demonstrate improved reliability in evolving environments.

Framework enables process discovery without storing all event data.

Abstract

More and more business activities are performed using information systems. These systems produce such huge amounts of event data that existing systems are unable to store and process them. Moreover, few processes are in steady-state and due to changing circumstances processes evolve and systems need to adapt continuously. Since conventional process discovery algorithms have been defined for batch processing, it is difficult to apply them in such evolving environments. Existing algorithms cannot cope with streaming event data and tend to generate unreliable and obsolete results. In this paper, we discuss the peculiarities of dealing with streaming event data in the context of process mining. Subsequently, we present a general framework for defining process mining algorithms in settings where it is impossible to store all events over an extended period or where processes evolve while being analyzed. We show how the Heuristics Miner, one of the most effective process discovery algorithms for practical applications, can be modified using this framework. Different stream-aware versions of the Heuristics Miner are defined and implemented in ProM. Moreover, experimental results on artificial and real logs are reported.