Process Mining Model Research Articles

A process mining model to detect adverse patient safety events: Blood products watch

Introduction Patient safety incidents and adverse events are reported voluntarily by healthcare staff. The Global Trigger Tool, developed by the Institute for Healthcare Improvement, is used to capture unreported events. The objective of the new model is to use process mining in the context of adverse event detection to increase reporting efficiency. Methods In the first phase, the process mining model is used to discover the actual process map by querying inpatient processes using the provided log data. Transfusions of blood or blood products within 24 h after surgery, a subset of The Transfusion of Blood or Use of Blood Products trigger of Institute for Healthcare Improvement (GTT-C1) was deployed as a process pattern within the actual process map. Only detected patients were reviewed in the last phase. Results The model ran on a single hospital data set, which included 2870 patients, 1048 of which included surgeries. The model reduced the analyzed data to 57 patients. The expert group analyzed these 57 patients and defined 12 adverse events. The calculated adverse event ratio was 1.1%, comparable with other manual Global Trigger Tool studies. In addition, the reviewers’ time required decreased by 94.6%, from 138.28 to 7.52 h. Two reviewers validated the model by reviewing a sample of patients. Conclusions Process mining can be used in healthcare institutions to conduct routine surveillance and detect unreported safety events. This allows healthcare providers to allocate financial and human resources to more urgent needs and increase efficiency. Further research is needed to evaluate the model with other triggers.

A Multiperspective Fraud Detection Method for Multiparticipant E-Commerce Transactions

Detection and prevention of fraudulent transactions in e-commerce platforms have always been the focus of transaction security systems. However, due to the concealment of e-commerce, it is not easy to capture attackers solely based on the historic order information. Many works try to develop technologies to prevent frauds, which have not considered the dynamic behaviors of users from multiple perspectives. This leads to an inefficient detection of fraudulent behaviors. To this end, this article proposes a novel fraud detection method that integrates machine learning and process mining models to monitor real-time user behaviors. First, we establish a process model concerning the business-to-customer (B2C) e-commerce platform, by incorporating the detection of user behaviors. Second, a method for analyzing abnormalities that can extract important features from event logs is presented. Then, we feed the extracted features to a support vector machine (SVM)-based classification model that can detect fraud behaviors. We demonstrate the effectiveness of our method in capturing dynamic fraudulent behaviors in e-commerce systems through the experiments.

White-box validation of quantitative product lines by statistical model checking and process mining

We propose a novel methodology to validate software product line (PL) models by integrating Statistical Model Checking (SMC) with Process Mining (PM). We consider the feature-oriented language QFLan from the PL engineering domain. QFLan allows to model PL equipped with rich cross-tree and quantitative constraints, as well as aspects of dynamic PLs such as the staged configurations. This richness allows us to easily obtain models with infinite state-space, calling for simulation-based analysis techniques, like SMC. For example, we use a running example with infinite state space. SMC is a family of analysis techniques based on the generation of samples of the dynamics of a system. SMC aims at estimating properties of a system like the probability of a given event (e.g., installing a feature), or the expected value of quantities in it (e.g., the average price of products from the studied family). Instead, PM is a family of data-driven techniques that uses logs collected on the execution of an information system to identify and reason about its underlying execution process. This often regards identifying and reasoning about process patterns, bottlenecks, and possibilities for improvement. In this paper, to the best of our knowledge, we propose, for the first time, the application of Process Mining (PM) techniques to the byproducts of Statistical Model Checking (SMC) simulations. This aims to enhance the utility of SMC analyses. Typically, if SMC gives unexpected results, the modeler has to discover whether these come from actual characteristics of the system, or from bugs in the model. This is done in a black-box manner, only based on the obtained numerical values. We improve on this by using PM to get a white-box perspective on the dynamics of the system observed by SMC. Roughly speaking, we feed the samples generated by SMC to PM tools, obtaining a compact graphical representation of the observed dynamics. This mined PM model is then transformed into a mined QFLan model, making it accessible to PL engineers. Using two well-known PL models, we show that our methodology is effective (helps in pinpointing issues in models, and in suggesting fixes), and that it scales to complex models. We also show that it is general, by applying it to the security domain.

Advancing verification of process mining models with quantitative model checking in stochastic environment

The study of business process analysis and optimization has attracted significant scholarly interest in the recent past, due to its integral role in boosting organizational 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 information systems, thereby discovering process models and identify process-related issues. The goal of the current study is to examine how Quantitative Model Checking (QMC) approaches might be applied in the context of PM. Model checking is a well-known verification approach that provides thorough analysis and validation of a system’s properties in comparison to a predetermined model. The adoption of QMC is aimed at improving the accuracy, reliability, and comprehensiveness of PM models in stochastic environment. We propose a novel methodology in this research direction, which integrates QMC with PM by formally modelling discovered and replayed process models and applying QMC methods to verify PM models. The potential of QMC to overcome significant drawbacks of the existing methodologies is the main driver for its use in PM. By including probabilistic model verification, it is possible to take into account the uncertainties and stochastic behaviour that are frequently present in systems that are used in real world; while statistical model checking methods utilized where probabilistic methods fails/not suitable, such as, to handle complex models and/or models with large state-spaces.

Enhancement in Process Mining Model by Repairing Noisy Behavior in Event Log

Enhancement in Process Mining Model by Repairing Noisy Behavior in Event Log

A multi-objective medical process mining model using event log and causal matrix

Medical analytics in genetic process mining have developed models with multiple and often conflicting criteria. Some studies have reduced the complexities inherent in multiple and conflicting criteria by eliminating some criteria and solving the problem with a single or linear objective function. This study proposes a multi-objective model for obtaining Pareto without removing any conflicting criteria. Two multi-objective Pareto-based models of Non-dominated Ranked Genetic Algorithm (NRGA) and Non-dominated Sorting Genetic Algorithm (NSGAII) are developed. A comprehensive examination and statistical analysis are performed to analyze the performance of the two algorithms with five metrics of Mean Ideal Distance (MID), Cover Surface (CS), diversity, spacing, and time. In addition, sensitivity analysis examines the problem size and the coefficients of the different criteria on the objective functions. Finally, for the performance evaluation, a Data Envelopment Analysis (DEA) is developed based on some instances and benchmarks to assess the efficiency of the algorithms. We show that NSGAII outperforms the NRGA according to the performance metrics and statistical hypothesis testing.

Real-time resource allocation in the emergency department: A case study

Overcrowding is a phenomenon that affects Emergency Departments (EDs) worldwide determining a harmful impact on the healthcare provided. Because of the wide variety of different patient paths, in the literature the ED processes are usually modeled making significant assumptions, and neglecting fundamental aspects. Such assumptions could make sense for strategic or tactical decisions but nowadays the objective most frequently required by practitioners is the optimization of already available resources. To deal with this problem, we need to act at the operational level, with a particular attention to resource allocation in real time since arrivals and activities to be performed are known only over time. In this paper we present the case study of an Italian ED to investigate if an online allocation algorithm based on prioritization combined with a prediction tool can improve the ED performance, alleviating the overcrowding. Then, we propose several policies for the online allocation of the ED resources, which take into account the real-time state of the ED and the prediction of the next activities provided by an ad hoc process mining model. The proposed approach is validated and analyzed on the case study through a fine-grained simulation model. Results suggest that if the decisions of allocating resources for the execution of activities take into consideration the probably subsequent activities, then there is a room for improvement for the average door-to-doctor time, ED Length-of-Stay, and resource utilization.

Multi-Level Decision Support System in Production and Safety Management

The proposed paper introduces an innovative approach based on the implementation of a multi-level Decision Support System (DSS) modelling processes in the industry. Specifically, the work discusses a theoretical Process Mining (PM) DSS model gaining digital knowledge by means of logics that are able to select the best decisions. The PM model is applied to an open dataset simulating a working scenario and defining a possible safety control method based on the risk assessment. The application of the PM model provides automatic alerting conditions based on a threshold of values detected by sensors. Specifically, the PM model is applied to worker security systems characterized by the environment with a risk of emission of smoke and gases. The PM model is improved by Artificial Intelligence (AI) algorithms by strengthening information through prediction results and improving the risk analysis. An Artificial Neural Network (ANN) MultilaLayer Perceptron (MLP) algorithm is adopted for the risk prediction by achieving the good computational performance of Mean Absolute Error (MAE) of 0.001. The PM model is first sketched by the Business Process Modelling and Notation (BPMN) method, and successively executed by means of the Konstanz Information Miner (KNIME) open source tool, implementing the process-controlling risks for different working locations. The goal of the paper is to apply the theoretical PM model by means of open source tools by enhancing how the multi-level approach is useful for defining a security procedure to control indoor worker environments. Furthermore, the article describes the key variables able to control production and worker safety for different industry sectors. The presented DSS PM model also can be applied to industry processes focused on production quality.

Advanced Control Systems in Industry 5.0 Enabling Process Mining.

This paper merges new research topics in Industry 5.0 using the Business Process Modeling and Notation (BPMN) approach able to integrate Artificial Intelligence (AI) in production processes. The goal is to provide an innovative approach to model production management in industry, adopting a new "proof of concept" of advanced Process Mining (PM) automatizing decisions and optimizing machine setting and maintenance interventions. Advanced electronic sensing and actuation systems, integrating supervised and unsupervised AI algorithms, are embedded in the PM model as theoretical process workflows suggested by a Decision Support System (DSS) engine enabling an intelligent decision-making procedure. The paper discusses, as examples, two theoretical models applied to specific industry sectors, such as food processing and energy production. The proposed work provides important elements of engineering management related to the digitalization of production process matching with automated control systems setting production parameters, thus enabling the self-adapting of product quality supervision and production efficiency in modern industrial systems.

The process approach using for the optimization and risk decreasing of the business marketing activity

The aim of the article. The aim of the article is an investigating process approach inoculation advantages for the risk decreasing and sales policy enterprise optimization. Analyses results. It was considered the main organization management approaches and the application features in the article. The strategy choice is determined by management goals, the period of application, making management decisions time, the tasks’ difficulty, the senior management qualification, the organization status during the time of decision-making, and the internal and external environment characteristics. In conditions of the variability increasing and environment complexity, the process way allows to ensure the organization flexibility and adaptability, eliminate interfunctional barriers, and increase interest in client satisfaction. It involves to the management through the business implementation processes as a sequential logical actions for processing resources into a product or service that satisfies the needs of internal or external consumers. The main, auxiliary, management business processes and development business processes are distinguished by their influence on value formation. The sequence of process management implementation consists in carrying out a strategic analysis, developing an organization strategy, setting priorities, compiling a register of business processes, establishing their goals and metrics. Also, a necessary component is the owners’ determination and limits of business processes, the creation of a planning and controlling system, a detailed description and documentation of business processes. The management of the organization makes possible to identify and timely eliminate such mistakes as the duplication presence, redundant functions, lack of information, violations in its transfer or information creation that is not used, inconsistency of interfaces, low productivity or business process isolation. The need to model sales activity is determined by the creating the organization's value chain role. Providing value to the consumer is realized through the sale of the company's products or services, which is one of the main business processes. The article examines the external and internal factors of influence on the formation of the company's marketing sales policy, the factors that determine the level of the quality, complications that may arise in the absence of business processes automation. Conclusions and directions for further research. The further development of company's marketing sales policy involves the progressive techniques using and sales process organizing methods with the help of automatic data recording and the creation of a customer contact center that has an interactive virtual customer reply and includes: the creation and development of the distribution ways structure and the system management; the sales methods option for different product groups and geographic markets; the security of the funds influx for the overturn products and services. All these activities influence to the predictability of the organization's working results increasing and business risk decreasing. The most difficult automating business processes creating problem is the processes decomposition of the upper conceptual level into the comprehensive work operation with the logical mistakes avoidance. Further theoretical investigation thereupon consists in the idea of the management procedure model agreement and the implementation model reasoning course, which is achieved through the process mining modeling strategy implementation and description of the enterprise's sales activity business processes

Repairing Event Logs to Enhance the Performance of a Process Mining Model

Organizations and companies are starving to improve their business processes to stay in competition. As we know that process mining is a young and emerging study that lasts among data mining and machine learning. The main goal of process mining is to obtain accurate information from the data; therefore, in recent years, it attracts the attention of many researchers, practitioners, and vendors. However, the purpose of enhancement is to extend or develop an existing process model by taking information from the actual process recorded in an event log. One type of enhancement of a process mining model is repair. It is common practice that due to logging errors in information systems or the presence of a special behavior process, they have the actual event logs with the noise. Hence, the event logs are traditionally thought to be defined as situation. Actually, when the logging is based on manual logging i.e., entering data in hospitals when patients are admitted for treatment while recording manually, events and timestamps are missing or recorded incorrectly. Our paper is based on theoretical and practical research work. The main purpose of our study is to use the knowledge gather from the process model, and give a technique to repair the missing events in a log. However, this technique gives us the analysis of incomplete logs. Our work is based on time and data perspectives. As our proposed approach allows us to repair the event log by using stochastic Petri net, alignment, and converting them into Bayesian analysis, which improves the performance of the process mining model. In the end, we evaluate our results by using the algorithms described in the alignment and generate synthetic/artificial data that are applied as a plug-in in a process mining framework ProM.

Business process mining from e-commerce event web logs: Conformance checking and bottleneck identification

A range of advanced methods have been formulated and utilized in the efforts of improving the business processes in many enterprises. One impacting enhancement technique is to employ process mining algorithms as modeling and analysis tools in order to provide the actual business performance by digging the event log data and finding the useful information. This paper focuses on the applications of process mining in e-commerce industry. Event log data with timestamps were retrieved and analyzed from the web databases of an e-commerce company and process mining algorithms, like inductive miner and fuzzy miner were executed for generating the actual e-commerce business processes automatically and checking their conformance with the standardized processes as well as to early detecting any bottlenecks and issues in the e-commerce processes. Several e-commerce process issues were considered, such as item procurement, product order and delivery item tracking. The process mining modeling and its statistical results indicate that process mining can provide an efficient and effective tool for modeling and analyzing the e-commerce business processes allowing for real-time process auditing and reengineering.

Process mining for self-regulated learning assessment in e-learning

Content assessment has broadly improved in e-learning scenarios in recent decades. However, the e-Learning process can give rise to a spatial and temporal gap that poses interesting challenges for assessment of not only content, but also students’ acquisition of core skills such as self-regulated learning. Our objective was to discover students’ self-regulated learning processes during an e-Learning course by using Process Mining Techniques. We applied a new algorithm in the educational domain called Inductive Miner over the interaction traces from 101 university students in a course given over one semester on the Moodle 2.0 platform. Data was extracted from the platform’s event logs with 21,629 traces in order to discover students’ self-regulation models that contribute to improving the instructional process. The Inductive Miner algorithm discovered optimal models in terms of fitness for both Pass and Fail students in this dataset, as well as models at a certain level of granularity that can be interpreted in educational terms, which are the most important achievement in model discovery. We can conclude that although students who passed did not follow the instructors’ suggestions exactly, they did follow the logic of a successful self-regulated learning process as opposed to their failing classmates. The Process Mining models also allow us to examine which specific actions the students performed, and it was particularly interesting to see a high presence of actions related to forum-supported collaborative learning in the Pass group and an absence of those in the Fail group.

Analysis of Process Mining Model for Software Reliability Dataset using HMM

Process mining analyzes business processes constructed on event logs. To derive a process model from a log of recorded events, HMM and reliability metrics are used to derive the required petri net. Two software reliability datasets are used to derive petri nets. This dataset describes particular software's failure cycle. Using this failure cycle, its corresponding event log is recorded. HMM and software reliability are used to track recorded event logs.

VTMine Framework as Applied to Process Mining Modeling

VTMine Framework as Applied to Process Mining Modeling

An efficient process mining model for Petri Nets in process discovery

Process mining is a process management system used to analyze business processes based on event logs. The knowledge is extracted from event logs by using knowledge retrieval techniques. The process mining algorithms are accomplished of inevitably discover models to give details of all the events registered in some log traces provided as input. The theory of regions is a valuable tool in process discovery: it aims at learning a formal model (Petri nets) from a set of traces. The main objective of this paper is to propose new concepts for noise filtering and label splitting problem. The experiment is done based on standard bench mark dataset HELIX and RALIC datasets. The performance of the proposed system is better than other existing methods.