Process Mining - Science topic

Firms struggle with improving end-to-end (E2E) processes due to difficulties in establishing shared E2E process understanding across firm levels. Creating behavioral visibility into processes might provide a solution, but traditional methods are limited in effectiveness. Thus, process mining (PM), offering data-driven process discovery and measurem...

There are billions of operations happening in a wide range of sectors on a daily basis. When it comes to the hospitality sector, it appears essential to handle POS operations in a more efficient way in restaurants. To fill the gap in the studies about event log data in the fast food restaurant POS context, an approach needs to be developed. Regardi...

The application of process mining for unstructured data might significantly elevate novel insights into disciplines where unstructured data is a common data format. To efficiently analyze unstructured data by process mining and to convey confidence into the analysis result, requires bridging multiple challenges. The purpose of this paper is to disc...

Modern cyber-physical systems based on the Industrial Internet of Things (IIoT) can be highly distributed and heterogeneous, and that increases the risk of failures due to misbehavior of interconnected components, or other interaction anomalies. In this paper, we introduce a conceptual architecture for IIoT anomaly detection based on the paradigms...

Collaboration is argued to be an important skill, not only in schools and higher education contexts but also in the workspace and other aspects of life. However, simply asking students to work together as a group on a task does not guarantee success in collaboration. Effective collaborative learning requires meaningful interactions among individual...

Bei dem Vortrag handelt es sich um eine Vorstellung verschiedener Entwicklungsphilosophien zur Erstellung höherer Petri-Netze. Speziell werden unter Anwendung der Entwicklungsphilosophien Informationsvertraulichkeits- und Datenschutznetze (ICPN) als Form höherer Petri-Netze modelliert. Es wird zwischen einstufigen (Augmentierung von Process Discove...

The increasing adoption of distributed systems has led to a growing need to understand and analyze user behavior in these environments. However, obtaining service-specific event logs for such systems, suitable for process mining and data modeling tasks, remains a significant challenge due to the complexities associated with distributed architecture...

The study of business process analysis and optimisation has attracted significant scholarly interest in the recent past, due to its integral role in boosting organisational performance. A specific area of focus within this broader research field is process mining (PM). Its purpose is to extract knowledge and insights from event logs maintained by i...

Process mining can be applied to systems for the management of Workflow, Business Processes and, in general, Process-Aware Information to discover and analyse implicit processes. In recent times, semantic interoperability has also become of crucial importance in the area of business processes. In particular, interoperability enables the discovery o...

Process mining organizatioQn (PMO) is an innovative approach based on artificial intelligence (AI) decision making suitable for designing healthcare processes for human resource (HR) organizations. The proposed work suggests some examples of PMO-based Business Process Model-ing and Notation (BPMN) workflows by highlighting the advances in HR manage...

Process mining has become one of the best programs that can outline the event logs of production processes in visualized detail. We have addressed the important problem that easily occurs in the industrial process called Bottleneck. The analysis process was focused on extracting the bottlenecks in the production line to improve the flow of producti...

Over the past few years, several software companies have emerged that offer process mining tools to assist enterprises in gaining insights into their process executions. However, the effective application of process mining technologies depends on analysts who need to be proficient in managing process mining projects and providing process insights a...

Manufacturers install and rely on a large number of sensors to operate and control their processes. However, the collected sensor data is rarely used to analyse and improve the higher-level, aggregated business processes. Process mining (PM) appears to be a promising solution, with the ability to automatically generate and analyse business process...

Event logs are the main source for business process mining techniques. However, readily available logs are produced only by part of the existing systems, which may not always be part of an investigated environment. Furthermore, logs that are created by a given information system may reflect only parts of the full process, while other parts may span...

The preservation of life on planet Earth was determined by a change in the technological structure: water energy; steam energy, coal; electric energy; hydrocarbon energy, the beginning of nuclear energy; nuclear energy; clean energy of quantum nanotechnology of a new technological order. The idea of saving the human race from extinction, expressed...

In the field of process mining, the discovery of process patterns from event logs remains a challenging topic and has always interested many researchers. Exploiting the process model remains a major challenge and is highly dependent on event log characteristics, such as dataset size, the completeness of the event trace, and especially the complexit...

Business process management (BPM) is a well-established discipline comprising a set of principles, methods, techniques, and tools to continuously improve the performance of business processes. Traditionally, most BPM decisions and activities are undertaken by business stakeholders based on manual data collection and analysis techniques. This is tim...

Industrial Internet of Things (IIoT) applications in Industry 4.0 collect and process Time Series (TS) originating from heterogeneous sources. Many data-driven techniques have been proposed over the years for unsupervised collective Anomaly Detection (AD) to detect anomalous TS and improve quality of service. These techniques build statistical and/...

The execution of operational processes generates event data stored in enterprise information systems. Process mining techniques analyze such event data to obtain insights vital for decision-makers to improve the reviewed process. In this context, event data visualizations are essential. We focus on visualizing variants describing process executions...

Based on two progressive aspects of the modeling problems in business process management (BPM), (1) in order to address the increasing complexity of user requirements on workflows underlying various BPM application scenarios, a more verifiable fundamental modeling method must be invented; (2) to address the diversification of software testing proce...

Process mining has significantly transformed business process management by introducing innovative data-based analysis techniques and empowering organizations to unveil hidden insights previously buried within their recorded data. The analysis is conducted on event logs structured by conceptual models. Traditional models were defined based on only...

The selection process for new students at Telkom University, also known as SMB Telkom University has been running for years and already has its process flow. However, the existing process flow can be further improved to better reflect the actual field processes and become more accurate. Process mining can enhance this process flow by creating a new...

Collaboration Patterns and Topic Trends in Business Process Management Conferences One of the most important scientific activities in which business process management (BPM) research attempts have been presented is the BPM Conferences that have been organized since 2003. These conferences guide the future of the field concerning different aspects...

Purpose Digitalization, innovation and changing customer requirements drive the continuous improvement of an organization's business processes. IT demand management (ITDM) as a methodology supports the holistic governance of IT and the corresponding business process change (BPC), by allocating resources to meet a company's requirements and strategi...

This paper explores the use of Learning Management Systems (LMS) in a primary school setting, focusing on Al Azhar 35 Islamic Primary School. LMS offers numerous advantages over traditional learning systems, and its application in primary schools is relatively rare in Indonesia. The study employs the Process Mining Project Methodology (PM2) to anal...

In the field of simulation, the key objective of a system designer is to develop a model that performs a specific task and accurately represents real-world systems or processes. A valid simulation model allows for a better understanding of the system’s behavior and improved decision-making in the real world. Face validity is a subjective measure th...

Nowadays, hospitals are facing the need for an accurate prediction of rehospitalizations. Rehospitalizations, indeed, represent both a high financial burden for the hospital and a proxy measure of care quality. The current work aims to address such a problem with an innovative approach, by building a Process Mining-Deep Learning model for the predi...

When designing simulations, the objective is to create a representation of a real-world system or process to understand, analyze, predict, or improve its behavior. Typically, the first step in assessing the credibility of a simulation model for its intended purpose involves conducting a face validity check. This entails a subjective assessment by i...

Process mining is a family of techniques that provide tools for gaining insights from processes in, for example, business, industrial, healthcare and administrative settings. Process discovery, as a field of process mining, aims to give a process model that describes a process given by an event log. A process model describes an underlying process w...

When processes execute through their business logic, their activities generate event logs, which contribute to trace sets. Since its introduction, the field of process mining has evolved, however, accuracy issues persist. In this paper, we explore the L* algorithm in the context of process mining, especially towards development of interactive proce...

User activity monitoring, process mining, and robotic process automation (RPA) are three related fields that have gained increasing attention in recent years due to their potential applications in various domains, including business process management and automation. User activity monitoring involves the use of software tools to track and analyze t...

The advancement of technology has led to a growing interest in assessing scientific inquiry within digital platforms. This shift towards dynamic and interactive inquiry assessments enables researchers to investigate not only the accuracy of student responses (product data) but also their steps and actions leading to those responses (process data)....

This paper investigates the interactions between agent-based modeling and process mining, which is an increasingly widespread applied discipline in the context of business process management. In particular, we explore a practical “green BPM” perspective. We propose a simulation approach to describe the environmental effects of two different health...

Background Knowledge of post-myocardial infarction (MI) disease risk to date is limited—yet the number of survivors of MI has increased dramatically in recent decades. We investigated temporally ordered sequences of all conditions following MI in nationwide electronic health record data through the application of process mining. Methods We conduct...

In the last 6 years, hospitals in developed countries have been trialling the use of command centres for improving organizational efficiency and patient care. However, the impact of these command centres has not been systematically studied in the past. It is a retrospective population-based study. Participants were patients who visited the Bradford...

Enterprise content management (ECM) platforms like IBM FileNet P8 are critical for managing unstructured content, but increasing workflow complexity strains traditional rule-based approaches. This article surveys strategies to evolve FileNet workflows using artificial intelligence (AI) and intelligent automation. Common ECM workflow limitations are...

This poster features research related to process mining in logistics. It is part of the Conversation Pieces series created by researcher Sebastian Piest and cartoonist LUVANE, that focuses on applications and future directions of Intelligence Amplification (IA).

Protecting our ecosystem by means of environmental remediation has become a major concern at this moment, with the aim of providing a healthy and green environment for the next generation. This is due to the fact that our surroundings have substantially deteriorated as a result of urbanization, intensifying industrialization processes, mining of na...

Article History Abstract − Process mining has been used extensively in recent years to develop the steps of the business and it is an important technique to remove waste and reduce process costs. The aims of this study indicate congestion, workflow which lived a long time, and stuff or system statistics in the banking real estate transaction proces...

The software product is high used by the society in general and its development complexity are inputs of this research that gears into the software development processes. The mapping and modelling of software processes, as well as their standardization are not trivial tasks in the industry of software. Therefore, process mining practices can be use...

Process mining is a well-known method for the systematic analysis of business processes. Process data is recorded in an event log and processed into end-to-end models using suitable software. The models enable a transparent view of actual processes and form the starting point for process optimization. Most process mining techniques assume that even...

Mining process models of medical behavior from electronic medical records is an effective way to optimize clinical pathways. However, clinical medical behavior is an extremely complex field with high nonlinearity and variability, and thus we need to adopt a more effective method. In this study, we developed a fuzzy process mining method for complex...

In recent years, process mining has become the prevalent method to take advantage of process data. Companies utilize different techniques to analyze and optimize their operations accelerating process data. However, businesses consist of complex processes which are connected and depend on each other. Analyzing single processes in isolation to repres...

Process mining event logs are traditionally formatted to reflect the execution of a collection of individual process instances, with a fixed case notion. In practice, process instances are often intertwined, and the scope of a particular process is less static. When flattening complex application data to traditional event log formats, like XES, pro...

The effective presentation of process models to non-expert users in a way that allows them to understand and query these models is a well-known research challenge. Conversational interfaces, with their low expertise requirements, offer a potential solution. While procedural models like Petri nets are not ideal for linguistic presentation, declarati...

In-depth analysis of customer journeys to broaden the understanding of customer behaviors and expectations in order to improve the customer experience is considered highly relevant in modern business practices. Recent studies predominantly focus on retrospective analysis of customer data, whereas more forward-directed concepts, namely predictions,...

Learning strategies are important catalysts of students' learning. Research has shown that students with effective learning strategies are more likely to have better academic achievement. This study aimed to investigate students' adoption of learning strategies in different course implementations, the transfer of learning strategies between courses...

Process discovery learns process models from event data and is a crucial discipline within process mining. Most existing approaches are fully automated, i.e., event data is provided, and a process model is returned. Thus, process analysts cannot interact and intervene besides parameter settings. In contrast, Incremental Process Discovery (IPD) enab...

Business Process Simulation (BPS) is an approach to analyze the performance of business processes under different scenarios. For example, BPS allows us to estimate what would be the cycle time of a process if one or more resources became unavailable. The starting point of BPS is a process model annotated with simulation parameters (a BPS model). BP...

Learning analytics dashboards (LADs) are often used to display real-time data indicating student learning trajectories and outcomes. Successful use of LADs requires teachers to orient their dashboard reviews with clear goals, apply appropriate strategies to interpret visualized information on LADs and monitor and evaluate their interpretations to m...

The study of learning processes can benefit from examining the digital traces left by students while browsing educational platforms. In this work, we propose an analysis of how process mining techniques can be combined with online educational technologies. In particular, we describe a process discovery experiment based on students’ movements betwee...

Software system developers must respond quickly to failures in order to avoid reputational and financial losses for their customers. Therefore, it is important to detect behavioral anomalies in the operation of software systems in a timely manner. At the moment, various tools for automatic monitoring of systems are being actively developed, but log...

Customer engagement has long been a major field of study in the market for digital products because a better understanding of customer behaviour enables service providers to optimise their offerings and retain the customers who are at risk of leaving. Process mining has been widely used in this context, as it is important to mine the changes in cus...

Logistics processes ensure that the right product is at the right location at the right time in the right quantity. Their efficiency is crucial to industrial operations, as they generate costs while not adding value to the product. Process mining techniques improve processes using real-life data. However, the application of process mining to logist...

The World Health Organization has estimated that air pollution will be one of the most significant challenges related to the environment in the following years, and air quality monitoring and climate change mitigation actions have been promoted due to the Paris Agreement because of their impact on mortality risk. Thus, generating a methodology that...

This article presents the domain engineering process carried out to obtain the requirements for the implementation of an Artificial Intelligence (AI) compliance framework aimed at the public sector. Owing to the current competitive and fast economy, which generates huge demand for increasingly efficient, reliable, and transparent intelligent system...

Motor disability includes the lack of sensation, movement, or coordination, and Assistive Technologies (AT) can help overcome these challenges. Motor-disabled students need different ATs and configurations depending on courses and individual needs, and some solutions can be expensive. Some affordable AT has roots in gaming but can also be used for...

One of the main use cases of process mining is to discover and analyze how users follow business assignments, providing valuable insights into process efficiency and optimization. In this paper, we present a comprehensive dataset consisting of 50 real business processes. The dataset holds significant potential for research in various applications,...

Business Process Management (BPM) heavily relies on event logs for process mining. However, traditional event logs may not always be available or may be harder to obtain for unlogged or unconventionally logged activities. To overcome these limitations, network traffic data can be used as an alternative source for constructing event logs. However, i...

This study aimed to demonstrate the application of process mining on video data of pigs, facilitating the analysis of behavioral patterns. Video data were collected over a period of 5 days from a pig pen in a mechanically ventilated barn and used for analysis. The approach in this study relies on a series of individual steps to allow process mining...

Agility is a contemporary approach to IT project management, which we can also use in education. Students learn through the gradual implementation of iterative projects with information exchange between team members. Agility is above all a mindset. Being agile is quite simply being able to adapt to an environment that changes. Furthermore, various...

As scientific experiments grow more data-intensive, HPC clusters have become the go-to infrastructure for handling expansive scientific workflows. This work explores the potential of process mining on SLURM-managed HPC cluster logs, targeting the description of workflows and bottleneck identification. The correlation of system-recorded jobs, consid...

SLURMminer is a tool designed to analyze SLURM systems in High-Performance Computing (HPC) clusters. It utilizes process mining techniques to generate event logs, extract process models, and visualize critical business intelligence metrics. The tool's unique log extraction approach for SLURM clusters allows for a detailed analysis of jobs and workf...

Learning has a temporal characteristic in nature, which means that it occurs over the passage of time. The research on the temporal aspects of learning faces several challenges, one of which is utilizing appropriate analytical techniques to exploit the temporal data. There is no coherent guide to selecting certain temporal techniques to lead to res...

Process model discovery covers the different methodologies used to mine a process model from traces of process executions, and it has an important role in artificial intelligence research. Current approaches in this area, with a few exceptions, focus on determining a model of the flow of actions only. However, in several contexts, (i) restricting t...

One aim of Process Mining (PM) is the discovery of process models from event logs of information systems. PM has been successfully applied to process-oriented enterprise systems but is less suited for communication- and document-oriented Enterprise Collaboration Systems (ECS). ECS event logs are very fine-granular and PM applied to their logs resul...

Event logs, as considered in process mining, document a large number of individual process executions. Moreover, each process execution consists of various executed activities. To cope with the vast amount of process executions in event logs, the concept of variants exists that group process executions with identical ordering relations among their...

Teacher feedback is the key to online collaborative discussion. To investigate the effects of different forms of teacher feedback intervention on learners' cognitive and emotional interactions in online collaborative discussion, this study collected collaborative discussion text data of online collaborative learners. Based on the framework of Commu...

The analysis of business processes based on their observed behavior recorded in event logs can be performed with process mining. This method can discover, monitor, and improve processes in various application domains. However, the process models produced by typical process discovery methods are difficult for humans to understand due to their high c...

PM4Py is a Python library providing a comprehensive array of tools for process mining. This paper presents an in-depth overview of the PM4Py library, including its integration with other Python libraries and its latest features, such as object-centric process mining. Furthermore, we discuss the significant impact of PM4Py within academia, industry,...

Der Begriff des "Process Mining" grassiert wie ein Buzzword in den Einkaufsabteilungen. Als Booster für Agilität, Promoter für resilientere Strukturen oder als Optimierer für die Schaffung von Lieferketten einer nächsten Generation. Doch was steckt dahinter und inwieweit kann Process Mining beim Aufbau widerstandsfähiger Lieferketten tatsächlich he...