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Implementing approaches based on process mining in inter-organizational collaboration environments presents challenges related to the granularity of event logs, the privacy and autonomy of business processes, and the alignment of event data generated in inter-organizational business process (IOBP) execution. Therefore, this paper proposes a complete and modular data-driven approach that implements natural language processing techniques, text similarity, and process mining techniques (discovery and conformance checking) through a set of methods and formal rules that enable analysis of the data contained in the event logs and the intra-organizational process models of the participants in the collaboration, to identify patterns that allow the discovery of the process choreography. The approach enables merging the event logs of the inter-organizational collaboration participants from the identified message interactions, enabling the automatic construction of an IOBP model. The proposed approach was evaluated using four real-life and two artificial event logs. In discovering the choreography process, average values of 0.86, 0.89, and 0.86 were obtained for relationship precision, relation recall, and relationship F-score metrics. In evaluating the quality of the built IOBP models, values of 0.95 and 1.00 were achieved for the precision and recall metrics, respectively. The performance obtained in the different scenarios is encouraging, demonstrating the ability of the approach to discover the process choreography and the construction of business process models in inter-organizational environments. [ABSTRACT FROM AUTHOR]

In this paper, we propose a deep learning-based approach to predict the next event in hospital organizational process models following the guidance of predictive process mining. This method provides value for the planning and allocating of resources since each trace linked to a case shows the consecutive execution of events in a healthcare process. The predictive model is based on a long short-term memory (LSTM) neural network that achieves high accuracy in the training and testing stages. In addition, a framework to implement the LSTM neural network is proposed, comprising stages from the preprocessing of the raw data to selecting the best LSTM model. The effectiveness of the prediction method is evaluated through four real-life event logs that contain historical information on the execution of the processes of patient transfer orders between hospitals, sepsis care cases, billing of medical services, and patient care management. In the test stage, the LSTM model reached values of 0.98, 0.91, 0.85, and 0.81 in the accuracy metric, and in the evaluation of the prediction of the next event using the 10-fold cross-validation technique, values of 0.94, 0.88, 0.84, and 0.81 were obtained for the four previously mentioned event logs. In addition, the performance of the LSTM prediction model was evaluated with the precision, recall, F1-score, and area under the receiver operating characteristic (ROC) curve (AUC) metrics, obtaining high scores very close to 1. The experimental results suggest that the proposed method achieves acceptable measures in predicting the next event regardless of whether an input event or a set of input events is used. [ABSTRACT FROM AUTHOR]

Process mining is a novel alternative that uses event logs to discover, monitor, and improve real business processes through knowledge extraction. Event logs are a prerequisite for any process mining technique. The extraction of event data and event log building is a complex and time-intensive process, with human participation at several stages of the procedure. In this paper, we propose a framework to semi-automatically build an event log based on the XES standard from relational databases. The framework comprises the stages of requirements identification, event log construction, and event log evaluation. In the first stage, the data is interpreted to identify the relationship between the columns and business process activities, then the business process entities are defined. In the second stage, the hierarchical structure of the event log is specified. Likewise, a formal rule set is defined to allow mapping the database columns with the attributes specified in the event log structure, enabling the extraction of attributes. This task is implemented through a correlation method at the case, event, and activity levels, to automatic event log generation. We validate the event log through quality metrics, statistical analysis, and business process discovery. The former allows for determining the quality of the event log built using the metrics of accuracy, completeness, consistency, and uniqueness. The latter evaluates the business process models discovered through precision, coverage, and generalization metrics. The proposed approach was evaluated using the autonomous Internet of Things (IoT) air quality monitoring system's database and the patient admission and healthcare service delivery database, reaching acceptable values both in the event log quality and in the quality of the business process models discovered. [ABSTRACT FROM AUTHOR]

Process Mining allows organizations to obtain actual business process models from event logs (discovery), to compare the event log or the resulting process model in the discovery task with the existing reference model of the same process (conformance), and to detect issues in the executed process to improve (enhancement). An essential element in the three tasks of process mining (discovery, conformance, and enhancement) is data cleaning, used to reduce the complexity inherent to real-world event data, to be easily interpreted, manipulated, and processed in process mining tasks. Thus, new techniques and algorithms for event data preprocessing have been of interest in the research community in business process. In this paper, we conduct a systematic literature review and provide, for the first time, a survey of relevant approaches of event data preprocessing for business process mining tasks. The aim of this work is to construct a categorization of techniques or methods related to event data preprocessing and to identify relevant challenges around these techniques. We present a quantitative and qualitative analysis of the most popular techniques for event log preprocessing. We also study and present findings about how a preprocessing technique can improve a process mining task. We also discuss the emerging future challenges in the domain of data preprocessing, in the context of process mining. The results of this study reveal that the preprocessing techniques in process mining have demonstrated a high impact on the performance of the process mining tasks. The data cleaning requirements are dependent on the characteristics of the event logs (voluminous, a high variability in the set of traces size, changes in the duration of the activities. In this scenario, most of the surveyed works use more than a single preprocessing technique to improve the quality of the event log. Trace-clustering and trace/event level filtering resulted in being the most commonly used preprocessing techniques due to easy of implementation, and they adequately manage noise and incompleteness in the event logs. [ABSTRACT FROM AUTHOR]

• We propose an end-to-end approach for BPMN process modeling from event logs. • We propose a filtering algorithm for removing outliers in the event log. • We present an algorithm for the identification of join gateways in the BPMN models. • We implement a tool called P-Miner that realizes our proposed approach. Information systems daily register a large amount of data in event logs. All these data can be used for the organizations to automatically discover their process models. However, the automatic construction of simple process models with consistently high and balanced fitness and precision remains a challenging task, which has attracted the attention in the scientific and organizational communities to develop suitable methods for creating high-quality business process models. In this paper, we present an approach to discover the model of a business process based on the Business Process Model and Notation (BPMN) standard from its behavior observed in event logs. Particularly, we propose: (1) a method to detect outlier behavior in a given event log; (2) a set of heuristic rules to discover join gateways associated with the closing of each of the split gateways in conformance with the rules of BPMN models; and (3) P-Miner, a tool that realizes our proposed approach to automatically discover and visualize process models. We use a state of the art process discovery technique as a basis for discovering XOR/AND gateways in the models. A set of experiments was carried out on real and artificial event logs for evaluating our proposal, considering the fitness and precision metrics to determine the quality of the built models. The amount of gateways in the resulting BPMN model and the time required to build the process models were estimated. The results reveal that the process models derived by our proposed approach exhibited competitive and more balanced results in fitness and precision than those derived with other discovery techniques. [ABSTRACT FROM AUTHOR]