Decision Mining in ProM

Lecture notes in computer sciencePublished 1 January 2006

A. Rozinat, Wil M. P. van der Aalst

DOI10.1007/11841760_33

Source URL

Citations284

SJR quartileQ2

SJR score0.35

SNIP0.55

TL;DR

This paper describes how machine learning techniques can be leveraged for decision mining, and presents a Decision Miner implemented within the ProM framework.

Abstract

Process-aware Information Systems typically log events (e.g., in transaction logs or audit trails) related to the actual business process executions. Proper analysis of these execution logs can yield important knowledge that can help organizations to improve the quality of their services. Starting from a process model, which can be discovered by conventional process mining algorithms, we analyze how data attributes influence the choices made in the process based on past process executions. Decision mining, also referred to as decision point analysis, aims at the detection of data dependencies that affect the routing of a case. In this paper we describe how machine learning techniques can be leveraged for this purpose, and we present a Decision Miner implemented within the ProM framework.

Keywords

Computer ScienceBusiness, Management and Accounting

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