Model business process improvement by statistical analysis of the users' conduct in the process

Business processes in dentistry are quickly evolving towards 'digital dentistry'. This means that many steps in the dental process will increasingly deal with computerized information or computerized half products. A complicating factor in the improvement of process performance in dentistry, however, is the large number of independent dental professionals that are involved in the entire process. In order to reap the benefits of digital dentistry, it is essential to obtain an accurate view on the current processes in practice. In this paper, so called process mining techniques are applied in order to demonstrate that, based on automatically stored data, detailed process knowledge can be obtained on dental processes, e.g. it can be discovered how dental processes are actually executed. To this end, we analyze a real case of a private dental practice, which is responsible for the treatment of patients (diagnosis, placing of implants and the placement of the final restoration), and the dental lab that is responsible for the production of the final restoration. To determine the usefulness of process mining, the entire process has been investigated from three different perspectives: (1) the control-flow perspective, (2) the organizational perspective and (3) the performance perspective. The results clearly show that process mining is useful to gain a deep understanding of dental processes. Also, it becomes clear that dental process are rather complex, which require a considerable amount of flexibility. We argue that the introduction of workflow management technology is needed in order to make digital dentistry a success.

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.

Some Thoughts About Data Type, Distribution, and Statistical Significance

Customer behaviour within business processes can change over time, making it difficult for market understanding and decision making. Detecting such variations, also referred to as concept drift, can provide insight into the evolution of the business environment, offer opportunities for model refinement and provide target-oriented services to improve customer satisfaction. Concept drift in the control-flow perspective has been extensively studied but there is a research gap in detecting process duration drift. In this paper, we use gamma mixture models (GMMs) with an expectation-maximization (EM) algorithm to fit process durations and then detect variations in their histogram, density and cumulative distributions. Specifically, three metrics: the overall difference in back-to-back histograms, the Kullback-Leibler (KL) divergence and the maximum difference in cumulative distributions are used to evaluate how different the process durations are. Furthermore, three corresponding statistical tests: the multinomial test, log-likelihood ratio (LLR) test and Kolmogorov-Smirnov (KS) test are applied to determine whether, or not, the differences are statistically significant. The approach is applied to a public real-life hospital billing process where two concept drift occurrences are discovered. The main contribution of this paper is the approach aiming for detecting process duration changes.

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.

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.

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 processes are designed to execute strategies that aim at achieving organisational goals. During the last decade, several methods have been proposed that prescribe the use of goal-oriented requirements engineering techniques for supporting different business process management activities, in particular business process modelling. The integration of goal modelling and business process modelling aims at increasing the alignment between business strategies and the processes with their supporting IT systems. This new research area, which the authors call Goal-Oriented Requirements Engineering for Business Processes (GORE-for-BP), is developing rapidly, but without a clear conceptualization of the focus and scope of the proposed GORE-for-BP methods. Furthermore, an overview is lacking of which methods exist and what their level of maturity is. This paper therefore presents a research review of the GORE-for-BP area, with the aim of identifying relevant methods and assessing their focus, scope, and maturity. This study used Systematic Literature Review and Method Meta-Modelling as research methods to identify and evaluate the state of the GORE-for-BP research area and to propose a research agenda for directing future research in the area. Nineteen methods were identified, which is an indication of an active research area. Although some similarities were found with respect to how goal models are transformed into business process models (or vice-versa), there is also considerable divergence in modelling languages used and the extent of coverage of typical requirements engineering and business process management lifecycle phases. Furthermore, the exploitation of requirements engineering techniques in the full business process management lifecycle is currently under researched. Also, the maturity of the methods found in terms of the formalisation of the transformation activity, the elaboration of method guidelines, and the extent to which methods are validated, can be further improved.

The need to extend business process languages with the capability to model complex data objects along with the control flow perspective has lead to significant practical and theoretical advances in the field of Business Process Modeling (BPM).On the practical side, there are several suites for control flow and data modeling; nonetheless, when it comes to formal verification, the data perspective is abstracted away due to the intrinsic difficulty of handling unbounded data. On the theoretical side, there is significant literature providing decidability results for expressive data-aware processes. However, they struggle to produce a concrete impact as being far from real BPM architectures and, most of all, not providing actual verification tools. In this paper we aim at bridging such a gap: we provide a concrete framework which, on the one hand, being based on Petri Nets and relational models, is close to the widely used BPM suites, and on the other is grounded on solid formal basis which allow to perform formal verification tasks. Moreover, we show how to encode our framework in an action language so as to perform reachability analysis using virtually any state-of-the-art planner.

This article reviews and compares different existing statistical tests utilized in significance analysis of gene expression from DNA microarray data. Microarray gene expression study facilitates the investigation of genes that are differentially expressed with respect to different classes of samples. Many gene expression studies also aim to find groups of genes that do something together, or to find out molecular similarities among a group of samples. In all respects, it is very important to know whether there is a difference between two sets of results, and whether that difference is likely to occur due to random variations of the datasets. Hence instead of simple data analysis, statistical tests are the proper way to check statistical relevance. Basically, the statistical significance test is compulsory for yielding an ordered list of relevant genes in terms of differential expression to the investigator. This article surveys a few important statistical tests which include both parametric and nonparametric tests and compares those statistical tests with respect to different performance metrics. The statistical tests have been applied on several artificial and real-life microarray gene expression datasets. Most of the time, real-life datasets contain noise and noisy data can have undesirable effect on the results of any data mining analysis. Therefore, performance analysis on noisy datasets for different statistical tests has also been performed to facilitate the comparative study. Furthermore, correlation analysis of the resultant genes from different statistical methods has been accomplished. Keywords: Analysis, correlation analysis, eigenvector, nonparametric tests, P-value, parametric tests, principal component, statistical tests.

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 technology aims to automatically generate process models by analyzing events, thereby assisting the design and redesign of process models. Although many process mining methods have appeared, they all have deficiencies. These methods focus on mining from the behavioral aspects described by the log, while ignoring the structural nature of the process model itself. The complexity of the process describes the simplicity and ease of understanding of the process. Higher process complexity affects the readability of the process. Genetic programming has strong robustness. Its individual representation based on tree structure can describe the special structure of the process. The introduction of process complexity fitness enables it to consider the complexity of the process model itself while mining log behavior, so as to achieve nonlinear mining of complex processes. This paper analyzes the process mining based on genetic programming and proposes process individuals based on tree structure. By realizing the combination of process complexity measurement and process mining technology, noncomplex process mining can be realized. In this paper, the process complexity is combined with the process mining algorithm based on genetic programming, and a measure of process structural complexity is proposed, which is converted into complexity fitness and introduced into the fitness function of genetic programming, to realize the use of genetic programming. The research results show that the improved new adaptive genetic programming robust scheduling algorithm provides a new idea and method for the evaluation of college sports under different sports conditions. This makes the college sports evaluation management system more intelligent and improves the rational allocation of physical education teaching.

A high level of competitiveness of a company is one of the key factors that guarantees stable long-term economic development and a stable position of the enterprise in the market. Competition is a trigger for activating innovative processes and introducing modern information technologies into the enterprise infrastructure. The lack of control and regulation system, the low level of automation of processes, their inconsistency with strategic goals and duplication significantly reduce the overall level of competitiveness of the enterprise and negatively affects economic profit. As a tool that can increase a company’s competitive position in the market, the work explores the digital technology of in-depth analysis, that is Process Mining. This article aims to explore how businesses gain a competitive edge by using business process mining technology. It also examines the current trends in implementing process analytics across different industries. Process Mining technology is the focus of the study. The subject is the impact of business process mining technology on the competitiveness of a company. The study used theoretical and empirical scientific approaches, methods of statistical analysis, and systems analysis techniques. By analyzing competitive advantages, a correlation between the implementation of process analytics and the company’s increased efficiency was established. The most popular areas of application of Process Mining are procurement, customer service and information technology. The leaders in the number of successful implementations of business process analysis technology are the financial services, telecommunications, and oil and gas industries.