Discover Process Models Research Articles

Process mining framework with time perspective for understanding acute care: a case study of AIS in hospitals

BackgroundAcute care for critical illness requires very strict treatment timeliness. However, healthcare providers usually cannot accurately figure out the causes of low efficiency in acute care process due to the lack of effective tools. Besides, it is difficult to compare or conformance processes from different patient groups.MethodsTo solve these problems, we proposed a novel process mining framework with time perspective, which integrates four steps: standard activity construction, data extraction and filtering, iterative model discovery, and performance analysis.ResultsIt can visualize the execution of actual clinical activities hierarchically, evaluate the timeliness and identify bottlenecks in the treatment process. We take the acute ischemic stroke as a case study, and retrospectively reviewed 420 patients’ data from a large hospital. Then we discovered process models with timelines, and identified the main reasons for in-hospital delay.ConclusionsExperiment results demonstrate that the framework proposed could be a new way of drawing insights about hospitals’ clinical process, to help clinical institutions increase work efficiency and improve medical service.

Trace Clustering: A Preprocessing Method to Improve the Performance of Process Discovery

The information system collects a large number of business process event logs, and process discovery aims to discover process models from the event logs. Many process discovery methods have been proposed, but most of them still have problems when processing event logs, such as low mining efficiency and poor process model quality. The trace clustering method allows to decompose original log to effectively solve these problems. There are many existing trace clustering methods, such as clustering based on vector space approaches, context-aware trace clustering, model-based sequence clustering, etc. The clustering effects obtained by different trace clustering methods are often different. Therefore, this paper proposes a preprocessing method to improve the performance of process discovery, called as trace clustering. Firstly, the event log is decomposed into a set of sub-logs by trace clustering method, Secondly, the sub-logs generate process models respectively by the process mining method. The experimental analysis on the datasets shows that the method proposed not only effectively improves the time performance of process discovery, but also improves the quality of the process model.

An Integrated Approach for Discovering Process Models According to Business Process Types

Process discovery technique aims at automatically generating a process model that accurately describes a Business Process (BP) based on event data. Related discovery algorithms consider recorded events are only resulting from an operational BP type. While the management community defines three BP types, which are: Management, Support and Operational. They distinguish each BP type by different proprieties like the main business process objective as domain knowledge. This puts forward the lack of process discovery technique in obtaining process models according to business process types (Management and Support). In this paper, we demonstrate that business process types can guide the process discovery technique in generating process models. A special interest is given to the use of process mining to deal with this challenge.

Conformance checking over uncertain event data

The strong impulse to digitize processes and operations in companies and enterprises have resulted in the creation and automatic recording of an increasingly large amount of process data in information systems. These are made available in the form of event logs. Process mining techniques enable the process-centric analysis of data, including automatically discovering process models and checking if event data conform to a given model. In this paper, we analyze the previously unexplored setting of uncertain event logs. In such event logs uncertainty is recorded explicitly, i.e., the time, activity and case of an event may be unclear or imprecise. In this work, we define a taxonomy of uncertain event logs and models, and we examine the challenges that uncertainty poses on process discovery and conformance checking. Finally, we show how upper and lower bounds for conformance can be obtained by aligning an uncertain trace onto a regular process model.

The impact of biased sampling of event logs on the performance of process discovery

With Process discovery algorithms, we discover process models based on event data, captured during the execution of business processes. The process discovery algorithms tend to use the whole event data. When dealing with large event data, it is no longer feasible to use standard hardware in a limited time. A straightforward approach to overcome this problem is to down-size the data utilizing a random sampling method. However, little research has been conducted on selecting the right sample, given the available time and characteristics of event data. This paper systematically evaluates various biased sampling methods and evaluates their performance on different datasets using four different discovery techniques. Our experiments show that it is possible to considerably speed up discovery techniques using biased sampling without losing the resulting process model quality. Furthermore, due to the implicit filtering (removing outliers) obtained by applying the sampling technique, the model quality may even be improved.

Stochastic process mining: Earth movers’ stochastic conformance

Initially, process mining focused on discovering process models from event data, but in recent years the use and importance of conformance checking has increased. Conformance checking aims to uncover differences between a process model and an event log. Many conformance checking techniques and measures have been proposed. Typically, these take into account the frequencies of traces in the event log, but do not consider the probabilities of these traces in the model. This asymmetry leads to various complications. Therefore, we define conformance for stochastic process models taking into account frequencies and routing probabilities. We use the earth movers’ distance between stochastic languages representing models and logs as an intuitive conformance notion. In this paper, we show that this form of stochastic conformance checking enables detailed diagnostics projected on both model and log. To pinpoint differences and relate these to specific model elements, we extend the so-called ‘reallocation matrix’ to consider paths. The approach has been implemented in ProM and our evaluations show that stochastic conformance checking is possible in real-life settings.

Time-oriented interactive process miner: a new approach for time prediction

Everyday information systems collect a different kind of process instances of a business flow. As time goes on, the size of the collected data builds up speedily and constitutes a huge amount of data. It is a very challenging task to obtain valuable information and features of processes from such big data. Considering in advance, the trend and different features of the ongoing process are essential. Especially, time management is crucial in designing and conducting business processes. In this article, a novel process miner algorithm is proposed for time prediction, named time-oriented İnteractive process miner (T-IPM), which predicts the remaining and completion time of each process in a business workflow. The goal is to develop a process miner algorithm for time prediction that is able to work on a huge amount of event logs, and to incorporate the execution records of ongoing processes into the discovered process model instantly. In this study, three datasets from different domains that include event logs of repair, hospital, and traffic process flows were used to carry out the experimental studies in order to show the effectiveness of the proposed time prediction algorithm. The experimental studies show that the proposed algorithm is capable of performing process analysis on a huge amount of event logs with low memory consumption and high prediction accuracy.

Using Process Mining for Learning Resource Recommendation: A Moodle Case Study

Nowadays, Learning Management Systems (LMS) play an intrinsic role in education. They gather traces about the learner (course view, wiki view, quiz attempt, etc.) in event logs. These logs offer the opportunity to provide dashboards and analysis on learners. There are several techniques that analyze event logs for different purposes (adaptation, recommendation, performance detection, etc.). Within this framework, our central focus is upon Educational Process Mining technique which generates process models for improving learning resource recommendation.We set forward an architecture leading to discover process models and recommend to the learner not only learning resource but also process models, each of which is relative to a specific learning resource. These models exert a certain influence on the result of learning resource recommendation. One of the reason that endows our work with an original aspect is that it automatically analyses event logs based on multi-features extracted from the learner’s profiles. However, the state of the art works require a manual analysis step based on learning results uniquely. We evaluated the discovered process models grounded on the event logs of Moodle LMS. These event logs contain 42,438 traces of 100 students who learned a course over one semester. Results corroborate the good performance of our work.

Process-Aware Enterprise Social Network Prediction and Experiment Using LSTM Neural Network Models

Process mining that exploits system event logs provides significant information regarding operating events in an organization. By discovering process models and analyzing social network metrics created throughout the operation of the information system, we can better understand the roles of performers and characteristics of activities, and more easily predict what will occur in the next operation of a system. By using accurate and valuable predicted information, we can create effective environments, provide suitable materials to perform activities better, and facilitate more efficient operations. In this study, we apply the long short-term memory, a variant of the recurrent neural network, to predict the enterprise social networks that are formed through information regarding a business system’s operation. More precisely, we apply the multivariate multi-step long short-term memory model to predict not only the next activity and next performer, but also all the variants of a process-aware enterprise social network based on the next performer predictions using a probability threshold. Furthermore, we conduct an experimental evaluation on the real-life event logs and compare our results with some related researches. The results indicate that our approach creates a useful model to predict an enterprise social network and provides metrics to improve the operation of an information system based on the predicted information.

Process Model Enhancement Through Capturing Important Behaviors and Rating Trace Variants

In the field of process discovery, it is worth noting that most process discovery algorithms assume that event logs are clean, i.e., event logs should not contain infrequent behaviors. However, real-life event logs often contain infrequent behaviors (i.e., outliers) and lead to quality issues of the discovered process model. On the other hand, driven by recent trends such as big data and process automation, the volume of event data is rapidly increasing: an event log may contain billions of event data. Unfortunately, some process mining algorithms and platforms may have difficulties handling such event logs. The ever-increasing size of event data and infrequent behaviors in the event log are two main challenges in the field of process discovery nowadays. However, little research has been conducted on simultaneously filtering infrequent behaviors and decreasing the size of the event log: Various filtering methods can filter infrequent behaviors, whereas the volume of the filtered log is still considerable. On the other hand, sampling methods can reduce the size of the event log, but the processed event log may still contain infrequent behaviors. Therefore, this paper proposes a technique to simultaneously filter infrequent behaviors and control the volume of input logs by capturing important behaviors and rating trace variants. Our experiments show that our approach can significantly improve the quality of the discovered process models. Furthermore, our approach can obtain a better process model from 0.001% trace variants than the complete event log and significantly improves the runtime of discovery algorithms.

Agent System Mining: Vision, Benefits, and Challenges

In the Agent-Based Modeling (ABM) paradigm, an organization is a Multi-Agent System (MAS) composed of autonomous agents inducing business processes. Process Mining automates the creation, update, and analysis of explicit business process models based on event data. Process Mining techniques make simplifying assumptions about the processes discovered from data. However, actual business processes are often more complex than those restricted by Process Mining assumptions. Several Process Mining approaches relax these standard assumptions by discovering more realistic process models. These approaches can discover more realistic process models. However, these models are often difficult to visualize and, consequently, to understand. Many MASs induce processes whose behaviors become more complex with each next embraced time step, while the complexities of these MASs remain constant. Thus, the ABM paradigm can cope naturally with the increasing complexity of the discovered process models. This paper proposes Agent System Mining (ASM) and ASM Framework. ASM combines Process Mining and ABM in the Business Process Management (BPM) context to infer MAS models of operational business processes from real-world event data, while ASM Framework maps ASM activities to different phases of the MAS modeling lifecycle. The paper also discusses the benefits of using ASM and outlines challenges associated with the implementation of the ASM Framework.

Extraction of User Daily Behavior From Home Sensors Through Process Discovery

In the last years, the wide availability on the market of low-cost smart devices paved the way for the development of smart environments, which offer an unprecedented opportunity to recognize the patterns of activities from a large amount of collected data, with the ultimate aim of monitoring the user behavior. In this article, we propose a methodology which relies on process discovery techniques to analyze sensor data in terms of activation sequences and to discover process models representing user's behavioral patterns. The extraction of such models is valuable not only in the perspective of gaining a better insight on how a certain task is performed but also in supporting novel smart services. In order to evaluate the effectiveness of the approach, in this article, we also consider a real-world case study set in an ambient-assisted living environment.

Filtering Infrequent Behavior in Business Process Discovery by Using the Minimum Expectation

The aim of process discovery is to discover process models from the process execution data stored in event logs. In the era of “Big Data,” one of the key challenges is to analyze the large amounts of collected data in meaningful and scalable ways. Most process discovery algorithms assume that all the data in an event log fully comply with the process execution specification, and the process event logs are no exception. However, real event logs contain large amounts of noise and data from irrelevant infrequent behavior. The infrequent behavior or noise has a negative influence on the process discovery procedure. This article presents a technique to remove infrequent behavior from event logs by calculating the minimum expectation of the process event log. The method was evaluated in detail, and the results showed that its application in existing process discovery algorithms significantly improves the quality of the discovered process models and that it scales well to large datasets.

Automated simulation and verification of process models discovered by process mining

ABSTRACTThis paper presents a novel approach for automated analysis of process models discovered using process mining techniques. Process mining explores underlying processes hidden in the event data generated by various devices. Our proposed Inductive machine learning method was used to build business process models based on actual event log data obtained from a hotel's Property Management System (PMS). The PMS can be considered as a Multi Agent System (MAS) because it is integrated with a variety of external systems and IoT devices. Collected event log combines data on guests stay recorded by hotel staff, as well as data streams captured from telephone exchange and other external IoT devices. Next, we performed automated analysis of the discovered process models using formal methods. Spin model checker was used to simulate process model executions and automatically verify the process model. We proposed an algorithm for the automatic transformation of the discovered process model into a verification model. Additionally, we developed a generator of positive and negative examples. In the verification stage, we have also used Linear temporal logic (LTL) to define requested system specifications. We find that the analysis results will be well suited for process model repair.

Functional Integration with Process Mining and Process Analyzing for Structural and Behavioral Properness Validation of Processes Discovered from Event Log Datasets

In this paper, we propose an integrated approach for seamlessly and effectively providing the mining and the analyzing functionalities to redesigning work for very large-scale and massively parallel process models that are discovered from their enactment event logs. The integrated approach especially aims at analyzing not only their structural complexity and correctness but also their animation-based behavioral properness, and becomes concretized to a sophisticated analyzer. The core function of the analyzer is to discover a very large-scale and massively parallel process model from a process log dataset and to validate the structural complexity and the syntactical and behavioral properness of the discovered process model. Finally, this paper writes up the detailed description of the system architecture with its functional integration of process mining and process analyzing. More precisely, we excogitate a series of functional algorithms for extracting the structural constructs and for visualizing the behavioral properness of those discovered very large-scale and massively parallel process models. As experimental validation, we apply the proposed approach and analyzer to a couple of process enactment event log datasets available on the website of the 4TU.Centre for Research Data.

A novel completeness definition of event logs and corresponding generation algorithm

AbstractAs the promotion of technologies and applications of Big Data, the research of business process management (BPM) has gradually deepened to consider the impacts and challenges of big business data on existing BPM technologies. Recently, parallel business process mining (e.g. discovering business models from business visual data, integrating runtime business data with interactive business process monitoring visualisation systems and summarising and visualising historical business data for further analysis, etc.) and multi‐perspective business data analytics (e.g. pattern detecting, decision‐making and process behaviour predicting, etc.) have been intensively studied considering the steep increase in business data size and type. However, comprehensive and in‐depth testing is needed to ensure their quality. Testing based solely on existing business processes and their system logs is far from sufficient. Large‐scale randomly generated models and corresponding complete logs should be used in testing. To test parallel algorithms for discovering process models, different log completeness and generation algorithms were proposed. However, they suffer from either state space explosion or non‐full‐covering task dependencies problem. Besides, most existing generation algorithms rely on random executing strategy, which leads to low and unstable efficiency. In this paper, we propose a novel log completeness type, that is, #TAR completeness, as well as its generation algorithm. The experimental results based on a series of randomly generated process models show that the #TAR complete logs outperform the state‐of‐the‐art ones with lower capacity, fuller dependencies covering and higher generating efficiency.

Improving the performance of process discovery algorithms by instance selection

Process discovery algorithms automatically discover process models based on event data that is captured during the execution of business processes. These algorithms tend to use all of the event data to discover a process model. When dealing with large event logs, it is no longer feasible using standard hardware in limited time. A straightforward approach to overcome this problem is to down-size the event data by means of sampling. However, little research has been conducted on selecting the right sample, given the available time and characteristics of event data. This paper evaluates various subset selection methods and evaluates their performance on real event data. The proposed methods have been implemented in both the ProM and the RapidProM platforms. Our experiments show that it is possible to considerably speed up discovery using instance selection strategies. Furthermore, results show that applying biased selection of the process instances compared to random sampling will result in simpler process models with higher quality.

Measuring Fitness and Precision of Automatically Discovered Process Models: A Principled and Scalable Approach

Automated process discovery techniques allow us to generate a process model from an event log. The quality of automatically discovered process models can be assessed with respect to several criteria, including fitness, which captures the degree to which the process model is able to recognize the traces in the event log, and precision, which captures the extent to which the behavior allowed by the process model is observed in the event log. Many fitness and precision measures have been proposed in the literature. However, recent studies have shown that none of the existing measures fulfil a set of intuitive properties. In addition, existing fitness and precision measures suffer from scalability issues when applied to models discovered from real-life event logs. This article presents a family of fitness and precision measures based on the idea of comparing the k-th order Markovian abstractions of a process model and of an event log. We show that our family of measures fulfils the aforementioned properties for suitable values of k. An empirical evaluation shows that our proposed measures yield intuitive results on a synthetic dataset of model-log pairs, while outperforming existing measures in terms of execution times in real-life context.

Adversarial System Variant Approximation to Quantify Process Model Generalization

In process mining, process models are extracted from event logs using process discovery algorithms and are commonly assessed using multiple quality dimensions. While the metrics that measure the relationship of an extracted process model to its event log are well-studied, quantifying the level by which a process model can describe the unobserved behavior of its underlying system falls short in the literature. In this paper, a novel deep learning-based methodology called Adversarial System Variant Approximation (AVATAR) is proposed to overcome this issue. Sequence Generative Adversarial Networks are trained on the variants contained in an event log with the intention to approximate the underlying variant distribution of the system behavior. Unobserved realistic variants are sampled either directly from the Sequence Generative Adversarial Network or by leveraging the Metropolis-Hastings algorithm. The degree by which a process model relates to its underlying unknown system behavior is then quantified based on the realistic observed and estimated unobserved variants using established process model quality metrics. Significant performance improvements in revealing realistic unobserved variants are demonstrated in a controlled experiment on 15 ground truth systems. Additionally, the proposed methodology is experimentally tested and evaluated to quantify the generalization of 60 discovered process models with respect to their systems.

Top-Down Process Mining From Multi-Source Running Logs Based on Refinement of Petri Nets

Today's information systems of enterprises are incredibly complex and typically composed of a large number of participants. Running logs are a valuable source of information about the actual execution of the distributed information systems. In this paper, a top-down process mining approach is proposed to construct the structural model for a complex workflow from its multi-source and heterogeneous logs collected from its distributed environment. The discovered top-level process model is represented by an extended Petri net with abstract transitions while the obtained bottom-level process models are represented using classical Petri nets. The Petri net refinement operation is used to integrate these models (both top-level and bottom-level ones) to an integrated one for the whole complex workflow. A multi-modal transportation business process is used as a typical case to display the proposed approach. By evaluating the discovered process model in terms of different quality metrics, we argue that the proposed approach is readily applicable for real-life business scenario.