Combining Case-Based Reasoning and Process Mining to improve collaborative decision-making in products design

Process mining in healthcare: A literature review

This exploratory study analyzes student behavior in a Massive Open Online Course (MOOC). MOOCs represent a global educational phenomenon transforming teaching and inspiring new research perspectives on learning methods in higher education institutions. Understanding how students organize their learning sequences and how these relate to their academic performance is crucial for optimizing digital educational processes. The objective of this study is to identify and characterize the learning sequences performed by students during their study sessions in a MOOC, using process mining (PM) techniques. The methodology involved analyzing a dataset collected between July 2017 and January 2018, comprising 27,922 students and approximately 3.5 million recorded interactions. Process mining techniques were employed to examine these learning sequences. Results indicate that most interactions correspond to assessments and video lectures, while forums were the least utilized activity. Additionally, two student profiles were identified: “Comprehensive” learners, who follow expected sequences and engage in longer, more intensive study sessions, and “Strategic” learners, who prioritize assessments. This study advances the current understanding of online learning and situates its findings within the broader literature by contrasting them with similar classification patterns reported by other authors.

Process mining techniques enable the analysis of a wide variety of processes using event data. For example, event logs can be used to automatically learn a process model (e.g., a Petri net or BPMN model). Next to the automated discovery of the real underlying process, there are process mining techniques to analyze bottlenecks, to uncover hidden inefficiencies, to check compliance, to explain deviations, to predict performance, and to guide users towards “better” processes. Dozens (if not hundreds) of process mining techniques are available and their value has been proven in many case studies. However, existing techniques focus on the analysis of a single process rather than the comparison of different processes. In this paper, we propose comparative process mining using process cubes. An event has attributes referring to the dimensions of the process cube. Through slicing, dicing, rolling-up, and drilling-down we can view event data from different angles and produce process mining results that can be compared. To illustrate the process cube concept, we focus on educational data. In particular, we analyze data of students watching video lectures given by the first author. The dimensions of the process cube allow us to compare the process of students that passed the course versus the process of students that failed. We can also analyze differences between male and female students, between different parts of the course, and between Dutch students and international students. The initial analysis provided in this paper is used to elicit requirements for better tool support facilitating comparative process mining.

Increasingly organizations are using process mining to understand the way that operational processes are executed. Process mining can be used to systematically drive innovation in a digitalized world. Next to the automated discovery of the real underlying process, there are process-mining techniques to analyze bottlenecks, to uncover hidden inefficiencies, to check compliance, to explain deviations, to predict performance, and to guide users towards “better” processes. Dozens (if not hundreds) of process-mining techniques are available and their value has been proven in many case studies. However, process mining stands or falls with the availability of event logs. Existing techniques assume that events are clearly defined and refer to precisely one case (i.e. process instance) and one activity (i.e., step in the process). Although there are systems that directly generate such event logs (e.g., BPM/WFM systems), most information systems do not record events explicitly. Cases and activities only exist implicitly. However, when creating or using process models “raw data” need to be linked to cases and activities. This paper uses a novel perspective to conceptualize a database view on event data. Starting from a class model and corresponding object models it is shown that events correspond to the creation, deletion, or modification of objects and relations. The key idea is that events leave footprints by changing the underlying database. Based on this an approach is described that scopes, binds, and classifies data to create “flat” event logs that can be analyzed using traditional process-mining techniques.

Semantics has been a major challenge when applying the process mining (PM) technique to real-time business processes. The several theoretical and practical efforts to bridge the semantic gap has spanned the advanced notion of the semantic-based process mining (SPM). Fundamentally, the SPM devotes its methods to the idea of making use of existing (semantic) technologies to support the analysis of PM techniques. In principle, the semantic-based process mining method is applied through the acquisition and representation of abstract knowledge about the domain processes in question. To this effect, this paper demonstrates how the semantic concepts and process modelling (reasoning) methods are used to improve the outcomes of PM techniques from the syntactic to a more conceptual level. To do this, the study proposes an SPM-based framework that shows to be intelligent with a high level of semantic reasoning aptitudes. Technically, this paper introduces a process mining approach that uses information (semantics) about different activities that can be found in any given process to make inferences and generate rules or patterns through the method for annotation, semantic reasoning, and conceptual assertions. In turn, the method is theoretically applied to enrich the informative values of the resultant models. Also, the study conducts and systematically reviews the current tools and methods that are used to support the outcomes of the process mining as well as evaluates the results of the different methods to determine the levels of impact and its implications for process mining.

Security is an important issue that every organisation should address. One approach to secure systems could be the use of process mining techniques. Process mining is an emerging discipline which can extract knowledge from event logs that are available in information systems. In the security context, process mining is used to analyse security trails to detect anomalies in process execution. While some of the data mining projects are already implemented in banking, insurance and telecom sector the interesting question is: What is happening in public sector? This paper investigates the research on process mining techniques in security domain and tries to discover the examples of its implementation especially in public sector. The systematic review has been conducted in order to give an overview of state-of-the-art process mining techniques used in security context, to classify the main areas of development, algorithms, tools and to identify possible future research course.

Process mining (PM) in the healthcare sector is a key discipline in terms of improving processes based on the data stored in clinical information systems. This discipline has been complemented by other techniques, such as process simulation (PS), in order to facilitate decision-making across the sector. In turn, this has enhanced the management of organizational indicators and strengthened the analysis, optimization, and improvement of processes. However, the combination of PM and PS in healthcare still presents a number of challenges, including the incorporation of additional medical specialties, the involvement of healthcare experts, the optimization of data quality, and the promotion of further research that seeks to strengthen the combination of these two areas. The present paper contributes to overcoming these challenges by introducing PROcess Mining & Simulation for Healthcare Analysis: PROMSHA-Methodology, a methodology that provides the necessary steps for process analysis by combining PM and PS in the field of healthcare. The methodology herein addresses certain challenges related to the quality of medical data and includes the participation of experts. To evaluate the usefulness of PROMSHA, a case study was conducted in the pediatric ophthalmology department of a Costa Rican hospital, in which PM techniques were applied to detect spaghetti models and repetitive activities, as well as to identify the causes of waiting lists and bottlenecks by means of several simulated scenarios.

Process mining techniques allow for extracting information from event logs. For example, the audit trails of a workflow management system or the transaction logs of an enterprise resource planning system can be used to discover models describing processes, organizations, and products. ProM is a generic tool for implementing process mining tools in a standard environment. Prom aims at improving this by providing techniques and tools for discovering process, organizational, social, and performance information from event logs. In this paper we introduce the challenging process mining domain and discuss a heuristics driven process mining algorithm; the so-called “Heuristics Miner" in detail. Heuristics Miner is an applied applicable mining algorithm that can deal with noise, and can be used to express the main behavior registered in an event log. In the “case study” section of this paper, we used an students' registration event log consisted of 299 cases and 569 events. The data was received from one of the universities in Thailand. Finally, the HM algorithm focused on the control flow perspective and generated a process model in the form of a Heuristics Net for the given event log.

Process mining-driven analysis of COVID-19’s impact on vaccination patterns

The shipbuilding industry is characterized by order production, and various processes are performed simultaneously in the construction of ships. Therefore, effective management of the production process and productivity improvement form important key factors in the industry. For decades, researchers and process managers have attempted to improve processes by using business process analysis (BPA). However, conventional BPA is time-consuming, expensive, and mainly based on subjective results generated by employees, which may not always correspond to the actual conditions. This paper proposes a method to improve the production process of offshore plant modules by analysing the process mining data obtained from the shipbuilding industry. Process mining uses information accumulated from the system-provided event logs to generate a process model and determine the values hidden within the process. The discovered process is visualized as a process model. Subsequently, alternatives are proposed by brainstorming problems (such as bottlenecks or idle time) in the process. The results of this study can aid in productivity improvement (idle time or bottleneck reduction in the production process) in conjunction with a six-sigma technique or ERP system. In future, it is necessary to study the standardization of the module production processes and development of the process monitoring system.

Process mining techniques and applications – A systematic mapping study

This paper gives an overview of relevant research in the area of process mining. Process mining techniques are able to extract knowledge from event logs. The major objective of process mining is to discover, monitor and improve real processes. Process mining aims to exploit event data in a meaningful way to identify and anticipate problems, and recommend countermeasures. Additionally, process mining places the existing massive volumes of data in the context of processes. Since extracting data is an integral part of any process mining procedure, data preparation or data pre-processing requires certain efforts. Examples have been given to indicate how the chosen process mining technique deals with incompleteness in the event log data. Experiments have been made on the real data collected from information system for accommodation services.

Process Mining is one research area to find useful information from various processes in business execution log data. As BPMS, ERP, SCM, etc so called process recognizing information system are widely spread, process mining research are getting emphasis recently. Also execution log data of one enterprise are frequently utilized for checking current status of management or analysis of resource efficiency. But since analysis result depends on measurement criteria and method, it is very important to select systematic process mining algorithm based on business model or enterprise strategy. In this paper many process mining techniques are introduced and compared using process mining tool, ProM.

In the last decade, as an emerging technique for business processes management, process mining (PM) has been applied in many domains, including manufacturing, supply-chain, government, healthcare, and software engineering. Particularly in healthcare, where most processes are complex, variable, dynamic, and multi-disciplinary in nature, the application of this technique is growing yet challenging. Several literature reviews, as secondary studies, reveal the state of PM applications in healthcare from different perspectives, such as clinical pathways, oncology processes, and hospital management. In this article, we present the results of a systematic mapping (SM) study which we conducted to structure the information available in the primary studies. SM is a well-accepted method to identify and categorize research literature, in which the number of primary studies is rapidly growing. We searched for studies between 2005 and 2017 in the electronic digital libraries of scientific literature, and identified 172 studies out of the 2428 initially found on the topic of PM in healthcare. We created a concept map based on the information provided by the primary studies and classified these studies according to a number of attributes including the types of research and contribution, application context, healthcare specialty, mining activity, process modeling type and notation/language, and mining algorithm. We also reported the demographics and bibliometrics trends in this domain; namely, publication volume, top-cited papers, most contributing researchers and countries, and top venues. The results of mapping showed that, despite the healthcare data and technique related challenges, the field is rapidly growing and open for further research and practice. The researchers who are interested in the field could use the results to elicit opportunities for further research. The practitioners who are considering applications of PM, on the other hand, could observe the most common aims and specialties that PM techniques are applied.

Process mining is an efficient method that can analyze the full population of transactions using the event log of business processes. Conventional rule-based process mining techniques can detect anomalies; however, it tends to trigger a large number of false alarms. To improve the efficiency of anomaly detection using process mining, this study adopts a deep learning-based classification approach to detect anomalies in the traces of event logs. This approach contributes to the literature by proposing a non-rule-based process mining technique based on deep learning. Results demonstrate that the proposed non-rule-based process mining method can help auditors focus on transactional anomalies. Keywords: Process mining, deep learning, anomaly detection, fraudulent activities.