Practical Aspects of Event Logs Creation for Industrial Process Modelling

Process mining techniques aim at extracting knowledge from event logs. One of the most important tasks in process mining is process model discovery. In discovering process models, an algorithm is designed to build a process model from a given event log. In this paper, a new model to discover process models has been proposed. A combination of Genetic Algorithm and Simulated Annealing has been used in this model. Genetic Algorithms has previously been used in this context. Previous approaches had drawbacks in fitness evaluation that misguided the algorithm. Another problem was that the quality of the candidates, in the population, was low such that it reduced the chance of finding a perfect answer. In this paper, a new fitness measure has been proposed to evaluate process models based on event logs. Moreover SA has been used to improve the quality of candidates in the population. It has been demonstrated that the proposed model outperformed in terms of rediscovering process models, compared to other approaches which are proposed in the literature, which was the result of better fitness evaluation and increased quality of individuals,. It came to conclusion that using GA and SA in combination with each other can be effective in this context.

Nowadays, organizations use process-aware information systems to understand and apply rapid changes to their processes. Process mining techniques automatically extract true dimensions of organizational processes including process models from data sets like event logs stored in these information systems. In most studies performed in the area of process model discovery, only information of the event logs is used. However, in this research, a novel method of process discovery is proposed, which uses event logs as well as the information on the data exchange among organizational roles, which is derived from physical generalized flow diagram model. This information formed the basis of a two-layered network that represents handover flow and data exchange flow among organizational roles. Then, by extracting and analyzing motifs existing in this network, five rules are set that map motifs with certain features to logical structures constructing process models. Finally, by integrating those structures, the process model will be discovered. The advantage of the proposed method over the previous ones is that from the business process management viewpoint, it is more efficient in detecting sophisticated structures in the process model. It is also highly resistant to noise. These benefits are derived from the fact that it exerts data exchange information along with event log information. Doing various experiments and evaluation of their results using the F-measure confirmed the superiority of this method to previous ones from the viewpoint of the business process management.

Determining right model of business process from event log is the purpose of process discovery. However some problems i.e the inability to discover OR, noise and event log incompleteness are emmerged while determining right model of business process. First, OR relation is often discovered as AND relation. Second, noise problem is occured when there are truncated and low frequency traces in event log. Thus control-flow pattern is used to solve issues of same noise relation frequency hence it discovers relation based on transaction function of activity. Consequently, it can refine non noise relation in business process model. Third, incompleteness leads to incorrect discovery of parallel process model; therefore we used Timed-based Process Mining which utilized non-linear dependence to solve the incompleteness. Finally this paper proposed combination of Timed-based Process Mining and control-flow pattern to discover OR and handle same frequency noise and incompleteness. From the experiment in section 3, this proposed method manages to get right process model from event log.

Process mining is a field of computer science that deals with the discovery and analysis of process models based on automatically generated event logs. Currently, many companies are using this technology to optimize and improve their business processes. However, a discovered process model may be too detailed, sophisticated, and difficult for experts to understand. In this paper, we consider a problem of discovering the hierarchical business process model from a low-level event log, i. e., the problem of the automatic synthesis of more readable and understandable process models based on the data stored in the event logs of information systems. The discovery of better-structured and more readable process models is extensively studied in the framework of process mining research from different perspectives. In this paper, we present an algorithm for discovering hierarchical process models represented as two-level workflow Petri nets. The algorithm is based on predefined event partitioning so that this partitioning defines a sub-process corresponding to a high-level transition at the top level of a two-level net. In contrast to existing solutions, our algorithm does not impose restrictions on the process control flow and allows for concurrency and iterations.

Process discovery aims to discover process models from event logs to describe actual business processes. The quality of event logs has an impact on the quality of process models, so preprocessing methods can be used to improve the quality of event logs. Chaotic activities may exist in real business scenarios, and the occurrence of chaotic activities is independent of other activities in the process and can occur at any location in the event log at any frequency. Therefore, chaotic activities seriously affect the model quality of process discovery. Filtering chaotic activities in event logs can effectively improve the quality of event logs and thus improve the quality of process models. The traditional chaotic activity filtering algorithm makes it difficult to balance accuracy and time performance. Therefore, a direct method for filtering chaotic activities is proposed in this paper. By analyzing the relationship between activities, chaotic activities are identified in the log according to the characteristics of chaotic activities and the direct following relationship of activities as the judgment condition, and the filtering of chaotic activities in the event log is realized. In addition, this paper proposes an indirect chaotic activity filtering method, which identifies and filters chaotic activities in the log by analyzing the influence of the existence of different activities on the overall chaos degree of the log. The proposed method is compared with the traditional chaotic activity filtering method on several simulation/real data sets, and the accuracy and running time between the multi-group event logs and the process models generated before and after chaotic activity filtering are analyzed, further verifying the effectiveness and feasibility of the proposed method. By summarizing the experimental results, it is found that the accuracy of the proposed chaotic activity filtering methods is greater than that of the frequency-based filtering method and is close to that of the entropy-based chaotic activity filtering methods. Moreover, compared with other filtering methods used in the experiment, the chaotic activity filtering method proposed in this paper can improve the efficiency by 23.4% on average for simulation logs, and by 84.25% on average for real event logs. It is concluded that compared with other filtering methods, the proposed chaotic activity filtering methods have higher accuracy and can effectively improve the time performance of chaotic activity filtering. Therefore, the chaotic activity filtering method proposed in this paper can balance the accuracy and time performance, and can ensure the integrity of the filtered event log to a certain extent.

Process mining refers to the extraction process models from event logs. Traditional process mining algorithms have problems dealing with event logs that are produced from unstructured real-life processes and generate spaghetti-like and incomprehensible process models. One means making traces more structural is to extract commonly used process model constructs (common patterns) in the event log and transform traces basing on such constructs. Another way of pre-processing traces is to categorize traces in event log into clusters such that process traces in each cluster can be adequately represented by a process model. Nevertheless, current approaches for trace clustering have many problems such as ignoring context process and huge computational overhead. In this paper, suffix-tree is firstly utilized for discovering common patterns. The traces in event log are transformed with common patterns. Thereafter suffix-trees are applied to categorize transformed traces. The trace clustering algorithm has a linear-time computational complexity. The process models mined from the clustered traces show a high degree of fitness and comprehensibility.

This paper is dedicated to a tool whose aim is to facilitate process mining experiments and evaluation of the repair algorithms. Process mining is a set of approaches which provides solutions and algorithms for discovery and analysis of business process models based on event logs. Process mining has three main areas of interest: model discovery, conformance checking and enhancement. The paper focuses on the latter. The goal of enhancement process is to refine an existing process model in order to make it adhere event logs. The particular approach of enhancement considered in the paper is called decomposed model repair. Although the paper is devoted to the implementation part of the approach, theoretical preliminaries essential for domain understanding are provided. Moreover, a typical use case of the tool is shown as well as guides to extending the tool and enriching it with extra algorithms and functionality. Finally, other solutions which can be used for implementation of repair schemes are considered, pros and cons of using them are mentioned.

The aim of process discovery is to build a process model from an event log without prior information about the process. The discovery of declarative process models is useful when a process works in an unpredictable and unstable environment since several allowed paths can be represented as a compact set of rules. One of the tools available in the literature for discovering declarative models from logs is the Declare Miner, a plug-in of the process mining tool ProM. Using this plug-in, the discovered models are represented using Declare, a declarative process modelling language based on LTL for finite traces. In this paper, we use a combination of an Apriori algorithm and a group of algorithms for Sequence Analysis to improve the performances of the Declare Miner. Using synthetic and real life event logs, we show that the new implemented core of the plug-in allows for a significant performance improvement.

The discovery of declarative process models by mining event logs aims to represent flexible or unstructured processes, making them visible to business and improving their manageability. Although promising, the declarative perspective may still produce models that are hard to understand, both due to their size and to the high number of restrictions of the process activities. This work presents an approach to reduce declarative model complexity by aggregating activities according to inclusion and hierarchy semantic relations. The approach was evaluated through a case study with an artificial event log and its results showed complexity reduction on the resulting hierarchical model.

Process mining is a technique for extracting process models from event logs. Event logs contain abundant information related to an event such as the timestamp of the event, the actions that triggers the event, etc. Much of existing process mining research has been focused on discoveries of process models behind event logs. How to uncover the timing constraints from event logs that are associated with the discovered process models is not well-studied. In this paper, we present an approach that extends existing process mining techniques to not only mine but also integrate timing constraints with process models discovered and constructed by existing process mining algorithms. The approach contains three major steps, i.e., first, for a given process model constructed by an existing process mining algorithm and represented as a workflow net, extract a time dependent set for each transition in the workflow net model. Second, based on the time dependent sets, develop an algorithm to extract timing constraints from event logs for each transition in the model. Third, extend the original workflow net into a time Petri net where the discovered timing constraints are associated with their corresponding transitions. A real-life road traffic fine management process scenario is used as a case study to show how timing constraints in the fine management process can be discovered from event logs with our approach.

Process mining is an emerging discipline incorporating methods and tools for the analysis of system/process executions captured in the form of event logs [1]. Traditionally process mining can be divided into three research areas: discovery (construction of process models from event logs), conformance checking (finding log and model deviations), and enhancement of existing process models with additional event log data.BPMN (Business Process Model and Notation) 2.0 [2] is a widely used process modeling notation, supported by various process modeling and analysis tools, and is a de-facto process modeling standard. Using BPMN within process mining opens perspectives for applicability of the existing process mining techniques: for instance discovered process models can be analyzed or enacted using existing BPMN-based software, and vice versa, manually created models can be imported to a process mining tool, verified against event logs, and enhanced with additional data.In this work we bridge the gap between conventional process modeling formalisms used in the context of process mining (e.g., Petri nets, causal nets, process trees) and BPMN. For that purpose we developed a suite of conversion algorithms and provide formal guarantees relating the behavior of Petri nets (including non-free-choice nets) to the corresponding BPMN models (and vice versa). The derived relations are used to enhance the BPMN models with information learned from the event logs.The developed conversion techniques are described in detail in [3] and have been implemented [4] as a part of ProM (Process Mining Framework) [5] -- an open source tool for process mining and verified on real event log data. Moreover, cases for which conversion algorithms give more compact process models in comparison with the initial models are identified. Although the developed algorithms deal with basic control flow constructs only, they can be applied in the discovery of advanced BPMN modeling elements [2], including subprocesses [6-7], cancellations [8], conditional branching and data objects, swimlanes, message flows, and others.

The automatic detection of divergences from a desired process behavior is a common research topic in the business process management community. An established technique to analyze processes is called conformance checking. Given a definition of a process in form of a process model, conformance checking can be used to test whether the executions of a process contained in a so-called event log data structure are conforming with the process as it was defined. The result is a comparison of the execution traces and their respective correct execution, according to the process model. This technique provides insights into where the divergence has occurred and how the execution must be altered to conform to the process model. However, a problem is that it requires a process model to be available. Process models in the correct format are not always available. Contrary to conformance checking, process anomaly detection aims to find anomalous executions without relying on a predefined process model. A process anomaly detection algorithm derives the process logic from the event log itself and exploits the patterns found within the event log to distinguish normal from anomalous process executions. Though process anomaly detection provides the benefit of not relying on a process model, it typically does not provide the level of detail that conformance checking does. A process execution can either be normal or it can be anomalous. This dissertation proposes process anomaly correction, a novel approach that combines the benefits of conformance checking and process anomaly detection. Given only an event log, process anomaly correction detects anomalous executions, clearly indicates where the anomaly has occurred during the execution and suggests possible corrective measures. The solution presented in this work is based on a new concept to the field of process anomaly detection: Process learning. In process learning, the task of understanding the process based on the example data is transformed into a learning problem in which a neural network is trained to predict the very next activity in a running process execution. The resulting machine learning model thus represents an approximation of the real process that created the data. This cumulative dissertation consists of five contributions to the field of business process management that demonstrate how, starting from a process anomaly detection, process anomaly correction is achieved in a series of four steps. (1) Process learning is employed to generate an approximated model of the process logic. (2) The limitation of only distinguishing between normal and anomalous process executions is overcome by employing the process learning model which processes the process executions on a finer level of detail than existing approaches. (3) The necessity of providing manual threshold settings, as it is typical for process anomaly detection algorithms, is replaced by an automatic parameterization utilizing the process learning model. (4) The predictive capabilities of the process learning model are exploited to generate possible corrections of detected anomalies. The resulting process anomaly correction approach can be employed in scenarios where classic conformance checking would be infeasible, due to the restriction of relying on a process model. Furthermore, it can be employed alongside classical conformance checking, for it incorporates more information coming from the event log than classical conformance checking (such as employees executing a process step, in which country the process is executed, etc.), and thus provides a new perspective for the process analyst.

Process mining (PM) is a technique to extract a process model from an event log to represent the process behaviour recorded in that event log. A mined process model with high fitness means that it can reflect most of the process behaviour recorded in the event log. Previous studies have shown that the mined model with high fitness can be used in process improvement, such as fraud detection, continuous process improvement and benchmarking. Genetic process mining (GPM) is a famous PM approach, which can simultaneously identify several process structures from event logs. However, GPM cannot effectively discover parallel structures from event logs. This study proposes a PM approach based on integration of GPM, particle swarm optimisation and differential evolution to find process models with high fitness for event logs involving multiple parallel structures. The results show that the proposed approach does indeed lead to improvement in gaining process models with high fitness for event logs involving multiple parallel structures.

A learning management system is a system that supports student learning with several components. Telkom University is an institution that has an LMS called CeLOE LMS (Center for e-Learning and Open Education Learning Management System). Using LMS supports students' learning process, and lecturers can monitor what activities are carried out by their students. The pattern of learning by students can affect the success of learning in certain situations. The influence of these learning patterns makes lecturers need to pay attention to differences in student learning patterns. The data that will be used in this research are Website Application Development and Enterprise Systems courses. This study uses a process mining method with fuzzy miner algorithms to overcome the "spaghetti-like" process model into a more straightforward and easier-to-understand form. The resulting process model will then be analyzed using the graph simplification process method to obtain an accurate process model that describes the event log. In the Website Application Development course, conformance for all students Event logs is 99.93%, while conformance for students with index A event log is 99.94%. In the Enterprise System course, both event logs show the best conformance of 100%. The modeling produced by the two courses shows that the resulting model has a close alignment between the fuzzy model and the event log to reflect the behavior observed in the event log.

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.