Challenges and Use Cases of Process Discovery in Process Mining

Abstract

To date, the amount of data collected are significant and the growth is exponential in various fields over the last decades. Most companies typically address the issue of storage system capacities and therefore such situation creates the issue handling "big data." However, most companies sometimes may find it a challenge to obtain useful knowledge from a large amount of information. As such, numerous organizations are expected to tackle the data-related difficulties. In contrast, there is a necessity to improve and support business processes in progressively changing conditions. Generally, it is possible to analyses the available data and propose improvements because some tools and methods exist to facilitate such cause. Process mining may offer effective approaches to complement the business process. Process mining allows organizations to take advantage of the information within the systems, but can also be used to check process conformance, predict execution challenges, and identify bottlenecks. For instance, healthcare has rapidly changed over decades of research and development. Through scientific discovery, more diseases can be treated. However, it also affects the feasibility of health institutions to accommodate more complicated health treatment processes and systems effectively. Indeed, many solutions exist to cure a particular disease, since machines are becoming further complex, requiring medical staff to be equipped with necessary training. Also, it significantly increases the cost of health care. This paper presents the insights of process mining, highlighting the possible approaches used to gather and analyses the data using feasible method in process mining including real discovery processes. The paper also discusses the challenges of process mining.

Machine learning approach for predicting production delays: a quarry company case study

“…Predictive modelling approaches in business process management provide a way to streamline operational business processes [ 16 ]. Process mining can discover the process workflows in the company, activity actions, and the mechanism of machines [ 17 ], as well as allowing the identification or diagnosis of fact-based problems [ 18 ]. Process mining explores the discrepancy between data of events, i.e.…”

“…Process mining explores the discrepancy between data of events, i.e. observed behavior, and models of processes to detect anomalies, compliance checks, predicts delays, facilitates decision-making, and suggest process redesigns [ 17 ]. Nevertheless, machine learning algorithms could be adopted into the process mining techniques, in producing predictive analysis and models.…”

Machine Learning Approach for Predicting Production Delays: A Quarry Company case study

“…Predictive modelling approaches in business process management provide a way to streamline operational business processes [11]. Process mining can discover the process work ows in the company, activity actions, and the mechanism of machines [12], as well as allowing the identi cation or diagnosis of fact-based problems [13]. Process mining explores the discrepancy between data of events, i.e.…”

“…Process mining explores the discrepancy between data of events, i.e. observed behavior, and models of processes to detect anomalies, compliance checks, predicts delays, facilitates decision-making, and suggest process redesigns [12]. Nevertheless, machine learning algorithms could be adopted into the process mining techniques, in producing predictive analysis and models.…”