# Event Stream-Based Process Discovery using Abstract Representations

## TL;DR

This paper introduces a generic architecture for process discovery from online event streams, enabling real-time process model learning with finite memory, and demonstrates its effectiveness through implementations and evaluations.

## Contribution

It presents a novel architecture that adapts existing process discovery techniques to streaming data, facilitating real-time process model discovery.

## Key findings

- Architecture enables process discovery on streams
- Implementations in ProM demonstrate practicality
- Evaluation confirms effectiveness in streaming context

## Abstract

The aim of process discovery, originating from the area of process mining, is to discover a process model based on business process execution data. A majority of process discovery techniques relies on an event log as an input. An event log is a static source of historical data capturing the execution of a business process. In this paper we focus on process discovery relying on online streams of business process execution events. Learning process models from event streams poses both challenges and opportunities, i.e. we need to handle unlimited amounts of data using finite memory and, preferably, constant time. We propose a generic architecture that allows for adopting several classes of existing process discovery techniques in context of event streams. Moreover, we provide several instantiations of the architecture, accompanied by implementations in the process mining tool-kit ProM (http://promtools.org). Using these instantiations, we evaluate several dimensions of stream-based process discovery. The evaluation shows that the proposed architecture allows us to lift process discovery to the streaming domain.