In the area of process mining, efficient alignment-based conformance checking is a hot topic. Existing approaches for conformance checking are typically monolithic and compute exact fitness values. One limitation with monolithic approaches is that it may take a significant amount of computation time in large processes. Alternatively, decomposition approaches run much faster but do not always compute an exact fitness value. This paper presents the tool Replay using Recomposition which returns the exact fitness value and the resulting alignments using the decomposition approach in an iterative manner. Other than computing the exact fitness value, users can configure the balance between result accuracy and computation time to get a fitness interval within set constraints, e.g., “Give me the best fitness estimation you can find within 5 minutes”.
This paper presents PALIA-ER, a web-based tool for question-driven process mining in Emergency Room. PALIA-ER uses Palia discovery algorithm and includes model simplification and filtering features specially domain-specific for ER. Most PALIA-ER functionalities can be easily applied to other interdisciplinary contexts such as other healthcare units, education, or logistics.
Configurable process models are frequently used to represent business workflows and other discrete event systems among different branches of large organizations: they unify commonalities shared by all branches and describe their differences, at the same time. The configuration of such models is usually done manually, which is challenging. On the one hand, when the number of configurable nodes in the configurable process model grows, the size of the search space increases exponentially. On the other hand, the person performing the configuration may lack the holistic perspective to make the right choice for all configurable nodes at the same time, since choices influence each other. Nowadays, information systems that support the execution of business processes create event data reflecting how processes are performed. In this article, we propose three strategies (based on exhaustive search, genetic algorithms and a greedy heuristic) that use event data to automatically derive a process model from a configurable process model that better represents the characteristics of the process in a specific branch. These strategies have been implemented in our proposed framework and tested in both business-like event logs as recorded in a higher educational enterprise resource planning system and a real case scenario involving a set of Dutch municipalities.
Technical Report (ISCI17): In the area of process mining, efficient conformance checking is a hot topic. Several process mining vendors are in the process of implementing conformance checking in their tools to allow the user to check how well a model fits an event log. Current approaches for conformance checking are monolithic and compute exact fitness values but this may take excessive time. Alternatively, one can use a decomposition approach, which runs much faster but does not always compute an exact fitness value.
This paper introduces a recomposition approach that takes the best of both: it returns the exact fitness value by using the de- composition approach in an iterative manner. Results show that similar speedups can be obtained as by using the decomposition approach, but now the exact fitness value is guaranteed. Even better, this approach supports a configurable time bound: “Give me the best fitness estimation you can find within 10 minutes.” In such a case, the approach returns an interval that contains the exact fitness value. If such an interval is sufficiently narrow, there is no need to spend unnecessary time to compute the exact value.
Technical Report (Extended REMA 17): Allocating the most appropriate resource to execute the activities of a business process is a key aspect within the organizational perspective. An optimal selection of the resources that are in charge of executing the activities may contribute to improve the efficiency and the performance of the business processes. Despite the existence of resource metamodels that seek to provide a better representation of resources, a detailed classification of the allocation criteria that have been used to evaluate resources has been missing. In this paper, we provide an initial proposal for a resource allocation criteria taxonomy. This taxonomy is based on an extensive literature review that yielded 2,370 articles, from which 95 articles, regarding the existing resource allocation approaches within the business process management discipline, were considered for the analysis. The proposed taxonomy points out the most frequently criteria used for assessing the resources from January 2005 to July 2016.
Process Mining is an emerging discipline investigating tasks related with the automated identification of process models, given realworld data (Process Discovery). The analysis of such models can provide useful insights to domain experts. In addition, models of processes can be used to test if a given process complies (Conformance Checking) with specifications. For these capabilities, Process Mining is gaining importance and attention in healthcare. In this paper we introduce pMineR, an R library specifically designed for performing Process Mining in the medical domain, and supporting human experts by presenting processes in a human-readable way.
In order to improve the efficiency and effectiveness of Emergency Rooms (ER), it is important to provide answers to frequently-posed questions regarding all relevant processes executed therein. Process mining provides different techniques and tools that help to obtain insights into the analyzed processes and help to answer these questions. However, ER experts require certain guidelines in order to carry out process mining effectively. This article proposes a number of solutions, including a classification of the frequently-posed questions about ER processes, a data reference model to guide the extraction of data from the information systems that support these processes and a question-driven methodology specific for ER. The applicability of the latter is illustrated by means of a case study of an ER service in Chile, in which ER experts were able to obtain a better understanding of how they were dealing with episodes related to specific pathologies, triage severity and patient discharge destinations.
Project-based courses can provide valuable learning experiences for computing majors as well as for faculty and community partners. However, proper coordination between students, stakeholders and the academic team is very difficult to achieve. We present an integral study consisting of a twofold approach. First, we propose a proven capstone course framework implementation in conjunction with a software educational tool called SharedBoard, to support and ensure proper fulfillment of most academic and engineering needs. Second, we propose an approach for mining process data from the information generated by this tool as a way of understanding these courses and improving software engineering education. Moreover, we propose visualizations, metrics and algorithms using Process Mining to provide an insight into practices and procedures followed during various phases of a software development life cycle. We mine the event logs produced by SharedBoard and derive aspects such as cooperative behaviors in a team, component and student entropy, process compliance and verification. The proposed visualizations and metrics (learning
analytics) provide a multi-faceted view to the academic team serving as a tool for feedback on development process and quality by students.
Process mining techniques can be used to discover, analyze and improve real processes, by extracting models from observed behavior. The aim of this book is conformance checking, one of the main areas of process mining. In conformance checking, existing process models are compared with actual observations of the process in order to assess their quality. Conformance checking techniques are a way to visualize the differences between assumed process represented in the model and the real process in the event log, pinpointing possible problems to address, and the business process management results that rely on these models.
This book combines both application and research perspectives. It provides concrete use cases that illustrate the problems addressed by the techniques in the book, but at the same time, it contains complete conceptualization and formalization of the problem and the techniques, and through evaluations on the quality and the performance of the proposed techniques. Hence, this book brings the opportunity for business analysts willing to improve their organization processes, and also data scientists interested on the topic of process-oriented data science.
Process Mining focuses on extracting knowledge from data generated and stored in corporate information systems in order to analyze executed processes. In the healthcare domain, process mining has been used in different case studies, with promising results. Accordingly, we have conducted a literature review of the usage of process mining in healthcare. The scope of this review covers 74 papers with associated case studies, all of which were analyzed according to eleven main aspects, including: process and data types; frequently posed questions; process mining techniques, perspectives and tools; methodologies; implementation and analysis strategies; geographical analysis; and medical fields. The most commonly used categories and emerging topics have been identified, as well as future trends, such as enhancing Hospital Information Systems to become process-aware. This review can: (i) provide a useful overview of the current work being undertaken in this field; (ii) help researchers to choose process mining algorithms, techniques, tools, methodologies and approaches for their own applications; and (iii) highlight the use of process mining to improve healthcare processes.
Team recommendation is a key and little-explored aspect within the area of business process management. The efficiency with which the team is conformed may influence the success of the process execution. The formation of work teams is often done manually, without a comparative analysis based on multiple criteria between the individual performance of the resources and their collective performance in different teams. In this article, we present a multi-criteria framework to allocate work teams dynamically. The framework considers four elements: (i) a resource request characterization, (ii) historical information on the process execution and expertise information, (iii) different metrics which calculate the suitability of the work teams taking into account both individual performance as well as collective performance of the resources, and (iv) a recommender system based on BPA2 to obtain a ranking for the recommended work teams. A software development process was used to test the usefulness of our approach.
Dynamic resource allocation is considered a key aspect within business process management. Selecting the most suitable resources is a challenge for those in charge of making the allocation, because the efficiency with which this task is executed, can contribute to the quality of the results, and improve the process performance. Different mechanisms have been proposed to improve resource allocation. However, there is a need for more flexible allocation methods that integrate a set of conditions and requirements defined at run-time, and also, allow the combination of different criteria to evaluate resources.
In this paper, we present ResRec, a novel Multi-factor Criteria tool that can be used to recommend and allocate resources dynamically. The tool provides the feature of solving individual requests (On-demand), or requests made in blocks (Batch) through a recommender system developed in ProM.
Measurements in healthcare, and particularly in Emergency rooms (ER), are of great importance. Identifying metrics can give insight of how to improve the process and the quality of the provided services. This paper explores the capabilities of Process Mining (PM) to calculate ER metrics.
Study in a Massive Open and Online Courses (MOOCs) is challenging, since participants take the course without the support of a teacher. Taking a MOOC require the students to have the ability to self-regulate their learning. However, every person has its own learning style and the way each one interact and self-regulate in a MOOC varies. In this work we present an exploratory study from a process-oriented perspective to study whether students with different learning styles and SRL profiles show differences in navigating through a MOOC. Specifically, we investigate using Process Mining Techniques to analyze log files recording the course behavior of 99 learners across an Open edX MOOC combined with data from self-reported surveys. Our findings show that learners with different SRL profiles follow similar navigation paths, but there are differences when differentiating students by their learning styles.
Las organizaciones en la actualidad generan una gran cantidad de datos, de las cuales pocas realmente están haciendo un buen uso de éstos para optimizar el funcionamiento de su negocio. La minería de procesos aparece como una rama de la ciencia de los datos que intenta comprender el funcionamiento real que tienen los procesos en organizaciones a través de diferentes algoritmos, los que permiten el descubrimiento de estos procedimientos hasta entender cómo pueden ser mejorados. En este trabajo se aplicaron diferentes técnicas de minería de procesos a una empresa dedicada a la publicidad, la cual tiene problemas con uno de sus principales procesos de negocio, el de ventas y anulaciones de contratos. Además, se ha utilizado la metodología Process Mining Project Methodology ya propuesta para la aplicación de minería de procesos, añadiendo nuevos elementos para generar mejores resultados y comprender el problema. Éste último se intentó comprender en base de tres diferentes hipótesis, las que gracias a todo el análisis realizado
pudieron ser validadas.
Dynamically allocating the most appropriate resource to execute the different activities of a business process is an important challenge in business process management. An ineffective allocation may lead to an inadequate resources usage, higher costs, or a poor process performance. Different approaches have been used to solve this challenge: data mining techniques, probabilistic allocation, or even manual allocation. However, there is a need for methods that support resource allocation based on multi-factor criteria. We propose a framework for recommending resource allocation based on Process Mining that does the recommendation at sub-process level, instead of activity-level. We introduce a resource process cube that provides a flexible, extensible and fine-grained mechanism to abstract historical information about past process executions. Then, several metrics are computed considering different criteria to obtain a final recommendation ranking based on the BPA algorithm. The approach is applied to a help desk scenario to demonstrate its usefulness.
Global healthcare services have evolved over time, and nowadays they are expected to follow high-quality optimized standards. Analyzing healthcare processes has become a relevant field of study, and different techniques and tools have been developed to promote improvements in the efficiency and effectiveness of these processes. There is a research field called process mining that can be used to extract knowledge from the event data stored in the hospital information systems. With the help of this, it is possible to discover the real executed process, examine its performance and analyze the resource interaction during its execution. The goal of this article is to provide a bibliographic survey about the use of process mining algorithms, techniques, and tools in the analysis of healthcare processes, providing a general overview about the main approaches previously used and the information required to apply them in the medical field. We provide important insights about data, algorithms, techniques and methodologies that are required to help answer medical expert questions about their processes, motivating and inspiring a broader usage. So, if we have the information and it is possible to analyze and understand the healthcare processes, why are we not doing it?
Nowadays, the information systems are an indispensable resource for the organizations. The processes that are managed through these systems most of the time are hard to understand, maintain and improve. The data associated to the process becomes the main source and input to do all types of analysis. Process Mining allows the extraction of useful knowledge from the generated information of the corporate systems. This work suggests a methodology based on process mining to execute process support analysis of a corporate intranet implemented in SharePoint Server. With the extracted information it is possible to do analysis from several perspectives. The obtained results allow administrators of this type of technology platforms to evaluate the techniques used and generate benefits. The methodology was applied in a case study for a Retail client, besides doing an exploratory analysis of the data for two additional clients in the Industrial Safety industry in Chile.