Mining processes in dentistry

Process Mining is a research area that meets the gap between business processes and various IT systems. Most of the works in this area focus on the control flow perspective, while very few of them address the organizational aspect. The organization perspective of process mining supports the discovery of social network within organization by analyzing events logs recorded during real process execution. For process owners, it is very important to know how users perform their activities in process. In this paper, we introduce a process discovery method that combines an organizational perspective with probabilistic approach. Combining these two approaches we are able to fit distribution of users work and distribution of instance generation in process. We use different statistic methods like Cullen and Frey graph, Kolmogorov-Smirnov statistic test, Carmen-von-Mises statistic test and Anderson-Darling statistic test. After finding appropriate distribution we estimate its parameters. Research conducted and presented in this paper reveals that the information about users behaviour in process is significantly useful in further analysis: in simulations, to identify bottlenecks, to improve productivity of resource management and to identify task complexity in process.

Nowadays, it is crucial for several organizations to look for solutions allowing to reduce costs incurred by the execution of their business processes. Previously, we proposed a general approach for the extension of business process models with cost perspective based on process mining, independently of the model notation. This approach includes a cost data analysis based on applying classification algorithms selected dependently on the case in hand. Moreover, the proposed cost data analysis allows extracting knowledge about factors influencing on cost at process and activity levels. These factors include attributes corresponding only to the organizational and data perspectives. However, the control-flow perspective is not taken into account while it may also have influence on the business process incurred costs at the process level. Moreover, the proposed approach for selecting the appropriate classification algorithm is generally presented and illustrated. Thus, in this paper, we propose to improve the cost data analysis approach by including the control-flow perspective and by providing formal details about the context-aware classification algorithm selection as well as a detailed illustration of the proposed approach. This leads to improve decision making support for business process incurred cost reduction.

Process mining mainly focuses on the control flow perspective at present. In comparison, role-based process mining stresses the importance of roles in business processes and their interactive relationships. Though some scholars come to pay attention to role identification, their studies are not sufficient in the analysis of role complexity. In this paper, a role coupling complexity metric based on information flow in the process is proposed, and the design structure matrix DSM is used for role identification in business processes. Then, some typical process logs are mined by an improved particle swarm optimization method. As the coupling complexity between roles is increasingly reduced, our method can recognize roles with lower complexity. Finally, experiments are performed to verify the effectiveness of the method.

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.

Business Process Management (BPM) has been increasingly used in recent years in the healthcare domain to analyze, optimize, harmonize and compare clinical and healthcare processes. The main aim of this methodology is to model the interactions between medical and organizational activities needed to deliver health services, measure their complexity, variability and deviations to improve the quality of care and its efficiency. Among the different tools, languages and notations developed in the decades, UML (Unified Modeling Language) represents a widely adopted technique to model, analyze and compare business processes in healthcare. We adopted its diagrams in the MOCHA project to compare the different ways of organizing, coordinating and delivering child care across 30 EU/EEA countries both from an organization and control-flow perspectives. This paper provides an overview of the main components used to represent the business process using UML diagrams, also highlighting how we customized them to capture the specificity of the healthcare domain taking into account that processes are reconstructed on the basis of country experts’ responses to questionnaires. The benefits of the application of this methodology are demonstrated by providing examples of comparing different aspects of child care.

As organizations change, their information systems can evolve from simple systems to complex systems, which are hard to understand, and therefore hard to maintain or extend. Process mining can help organizations in trying to understand the information systems by analyzing the system.In this paper we propose a methodology to perform process diagnostics, based on process mining. Given an event log of an information system within an organization, process diagnostics gives a broad overview of the organization's process(es) within a short period of time. In the process diagnostics methodology, several perspectives of the process are highlighted. The outcome covers the control flow perspective, the performance perspective and the organizational perspective. We used the methodology on a case study for a Dutch governmental organization.

Business process mining is a new method that amalgamates business process modelling and analysis with data mining and machine learning techniques, whereby knowledge from event logs stored in today's information system is extracted to automatically construct business process models, identify bottlenecks, and improve the business processes. Since process mining has recently received attention among the research community, there are many challenges. The most important issues have been defined in the process mining manifesto. However, none of the published works investigated, or the defined challenges, have been solved and are still being encountered. Therefore, this paper provides a comprehensive and critical review of the literature in the context of process mining challenges. The contribution of this paper is to increase the maturity of the field of process mining by providing researchers with the state-of-the-art of the most important challenges of process mining.

Process mining analyzes business processes using event logs. Existing tools generate models to facilitate this task and improve the original business process, but the results are often unsatisfactory due to the complexity of the obtained models. Among the challenges faced in this context, we identify the misalignment with specific business requirements, preventing managers from accessing key data and making effective decisions. In this paper, we propose a requirement-driven approach centered on meta-modeling, which can help the development of process mining tools specially tailored to organizational needs. Thus, we introduce a requirement-driven method to address the critical challenge of model misalignment with required information. The method employs Model-Driven Engineering to simplify how process mining results are formulated, analyzed, and interpreted. The proposed method is iterative and involves several steps. First, a service manager defines a specific business question. Second, service managers and developers collaboratively establish a meta-model representing the target data. Third, developers extract relevant data using appropriate analysis techniques and visualize it. Finally, service managers and developers jointly interpret these visualizations to inform strategic decisions. This requirement-driven methodology empowers developers to concentrate on relevant information. Unlike general-purpose frameworks (e.g., ProM, Disco), this method emphasizes specificity, iterative refinement, and close stakeholder collaboration. By reducing cognitive overload through focused modeling and filtering of irrelevant data, organizations adopting this approach can achieve faster response times to business questions and develop specialized in-house analytical tools. This requirement-driven methodology, therefore, improves decision-making capabilities within process mining and across related analytical domains. We illustrate our methodology through a real business process taken from the literature owned by the VOLVO group. We use several examples of process mining to illustrate the benefits of the proposed methodology compared to existing tools which are unable to provide the required information. • A requirement-driven meta-model method for standardized process-mining assessment. • An iterative approach for low-cost development of tailored in-house tools. • Improves decisions through focused data extraction and need-aligned analysis.

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 time-consuming and potentially leads to suboptimal decisions, as only a restricted subset of data and options are considered. Over the past decades, a rich set of data-driven techniques has emerged to support and automate various activities and decisions across the BPM lifecycle, particularly within the process mining field. More recently, the uptake of artificial intelligence (AI) methods for BPM has led to a range of approaches for proactive business process monitoring. Given their common data requirements and overlapping goals, process mining and AI-driven approaches to business process optimization are converging. This convergence is leading to a promising emerging concept, which we call (AI-)augmented process execution: a collection of data analytics and artificial intelligence methods for continuous and automated improvement and adaptation of business processes. This article gives an outline of research at the intersection between process mining and AI-driven process optimization, classifies the researched techniques based on their scope and objectives, and positions augmented process execution as an additional layer on top of this stack.

Process Mining (PM) is a research area that focuses on helping organizations optimize their business processes. PM provides process analysis tools and techniques with a wide range of applications described in the literature. Additionally, PM can be associated with Root Cause Analysis (RCA), a problem-solving technique based on the assumption that a problem can only be solved by addressing its underlying cause. In this study, we performed a case on a real-world Brazilian telecommunication company that provides telephony, television, and broadband Internet subscription services. We applied PM and RCA to identify the possible causes for short and long-duration services, high rework rates, and activity repetition. We split the dataset into three aspects: Support, Cancellation, and Sales related services and analyzed them separately. For RCA, we applied both decision tree classification and rule mining. We identified 53 rules over 9 decision trees that were validated by process specialists from the tech company.

The performance of business processes is a critical success factor for companies on highly competitive markets. The improvement of business processes requires a deep understanding of as-is processes and existing weaknesses that hamper their performance. Traditionally, process mapping and weakness detection are conducted manually based on workshops and interviews with employees involved in the process. Consequently, these practices are not merely time-consuming and costly, but at the same time liable to subjective influences of the interviewed participants. For process mapping, these challenges can be overcome with the data based technology of process mining, that can model as-is-processes based on event log data from companies' information systems. For weakness detection in business processes, however, this technology is not applicable yet since this step requires domain knowledge about process weaknesses, which is not available for processing with process mining. This paper presents an approach to model domain knowledge about weaknesses in business processes to enable their automated and databased detection in event logs with process mining. Thereby, the weakness detection within business process improvement can be conducted more objectively at lower efforts.

This paper describes a business process analysis method that helps determining if a business process complies with the requirements put forward by enterprise ontology’s transaction pattern. The method starts by discovering the business process through the application of process mining techniques to the events that are generated by the applications that support the execution of the process. This step discovers the actual implementation of the process from its event trace. Next, the discovered process is analysed against enterprise ontology’s transaction pattern to determine whether the process complies with the structure and sequencing of its coordination and production acts. The paper shows that combining process mining with enterprise ontology contributes to the analysis of business processes, especially in terms of determining the boundaries of authority and responsibility of the process. The feasibility and the limitations of the method are discussed using a case study that analyses a semi-automated business process.

Digital transformation is forcing companies to rethink their processes to meet current customer needs. Business Process Management (BPM) can provide a means to structure and address this change. However, most BPM approaches face limitations on the number of processes they can optimize at the same time, due to complexity and resource constraints. The article is devoted to data mining as a tool for modeling and improving the company's business processes. Process Mining is a collection of data-driven diagnostic and business process improvement methods that combine machine learning and BPM. Among the advantages of Process Mining is more efficient management decision making. The possibility of introducing Process Mining methods into the work of a telecommunications company for the automatic collection of information about business processes and building a map of business processes is analyzed. The use of Process Mining methods will allow a telecommunications company to optimize the work of its departments and increase customer satisfaction. In addition, the implementation of this system contributes to a better analysis of the results of the execution of business processes for providing access to the Internet. This will improve the regulations for these processes, control of their compliance and the procedure for making managerial decisions at a higher quality level.

This chapter introduces semantic business process mining of SAP transaction logs. SAP systems are promising domains for semantic process mining as they contain transaction logs that are linked to large amounts of structured data. A challenge with process mining these transaction logs is that the core of SAP systems was not originally designed from the business process management perspective. The business process layer was added later without full rearrangement of the system. As a result, system logs produced by SAP are not process-based, but transaction-based. This means that the system does not produce traces of process instances that are needed for process mining. In this chapter, we show how data available in SAP systems can enrich process instance logs with ontologically structured concepts, and evaluate techniques for mapping executed transaction sequences with predefined process hierarchies.

The aim of process mining is to identify and extract process patterns from data logs to reconstruct an overall process flowchart. As business processes become more and more complex there is a need for managers to understand the processes they already have in place. To undertake such a task manually would be extremely time consuming so the practice of process mining attempts to automatically reconstruct the correct representation of a process based on a set of process execution traces. This paper outlines an alternative approach to business process mining utilising a Genetic Programming (GP) technique coupled with a graph based representation. The graph based representation allows greater flexibility in the analysis of process flowchart structure and offers the possibility of mining complex business processes from incomplete or problematic event logs. A number of event logs have been mined by the GP technique featured in this paper and the results of the experimentation point towards the potential of this novel process mining approach.