Mining Timing Constraints from Event Logs for Process Model

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 completeness of event logs and long-distance dependencies are two major challenges for process mining. Until now, most process mining methods have not been able to discover long-distance dependency and assume that the directly-follows relationship in the log is complete. However, due to the existence of high concurrency and the cycle, it is difficult to guarantee that the real-life log is complete regarding the directly-follows relationship. Therefore, process mining needs to be able to deal with incompleteness. In this paper, we propose a method for discovering process models including sequential, exclusive, concurrent, and cyclic structures from incomplete event logs. The method analyzes the co-occurrence class of the log and the model and then uses the technology of combining the behavior profile and co-occurrence class to obtain the communication behavior profile of the co-occurrence class. Furthermore, a method of constructing a substructure from the event log using the co-occurrence class is presented. Finally, the whole process model is built by combining those substructures. The experimental results show that the proposed method can discover process models with complex structures involving cycles from incomplete event logs and also can deal with long-distance dependency in the event log. Meanwhile, the discovered process model has a good degree of consistency with the original model.

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.

Event logs are the primary source of information in process mining. An event log consists of events, each having three mandatory attributes: a case identifier, an activity, and a timestamp. Translucent event logs add a fourth mandatory attribute: enabled activities. These are activities for which execution was possible besides the executed activity. Information on enabled activities can be accessed in task management software, in tasks executed within a desktop environment, or by utilizing domain knowledge. Information on enabled activities is valuable for process discovery and other process mining tasks. For example, utilizing the information when discovering process models from translucent event logs results in more robust and generalizable process models. In recent work, translucent activity relationships were defined, and their application was shown by extending the inductive mining approach. This work extends the defined relationships by also introducing frequencies. As a result, the Inductive Miner—infrequent can be extended. Hence, it is possible to use these relationships in the presence of noise in the recorded event logs. We introduce three translucent variants for the Inductive Miner—infrequent, each using translucent activity relationships at different points. Moreover, we allow for different graphs when fall-throughs within the Inductive Miner occur. Using artificial and enriched real-life event logs, we show that considering these relationships, more data-specific process models that still have desired generalization capabilities can be discovered.

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, one of the key challenges in process mining, aims at discovering process models from process execution data stored in event logs. Most discovery algorithms assume that all data in an event log conform to correct execution of the process, and hence, incorporate all behaviour in their resulting process model. However, in real event logs, noise and irrelevant infrequent behaviour are often present. Incorporating such behaviour results in complex, incomprehensible process models concealing the correct and/or relevant behaviour of the underlying process. In this paper, we propose a novel general purpose filtering method that exploits observed conditional probabilities between sequences of activities. The method has been implemented in both the ProM toolkit and the RapidProM framework. We evaluate our approach using real and synthetic event data. The results show that the proposed method accurately removes irrelevant behaviour and, indeed, improves process discovery results.

Workarounds are frequently observed in business processes, where employees intentionally bypass or deviate from procedures and business rules embedded in process models. One of the common workaround behaviors is the “split cases” workaround. This workaround typically takes place in processes where threshold conditions over data items are used by decision rules, determining the process path to be followed. Being familiar with the process rules, employees may decide to split a case whose relevant data value exceeds the threshold into two or more cases whose data value is below the threshold. Doing this they avoid the path which should be followed otherwise – this path may seem undesirable to the employee (e.g., including lengthy approvals), but might be needed for avoiding risks. Detecting such workarounds is hence of importance.Although the split case workaround is quite common, existing process mining and conformance checking techniques are not geared to detect it in an event log, since each of the cases is conformant by itself, and it is the relation among cases which might indicate this behavior. In this paper we propose an approach for detecting the split cases workaround in event log and report experimentation that has been performed using simulated as well as real life logs.KeywordsProcess miningWorkaroundInter-case behavior

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.

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.

Time prediction based on process mining

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

Discovering process models for the analysis of application failures under uncertainty of event logs

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.

VINST is a system used to record all activities related to the incident and problem management in Volvo IT Belgium. In this VINST system, activities recorded in several attributes, such as serial number, change date and time, status, and any other attributes needed, which is stored in the event log. In these sequenced activities, there are some specific knowledge that is not explicitly described in the event log. To find some of the necessary knowledge, this study extracts knowledge from the event log, by using process mining. Heuristic miner algorithm was implemented in this study, because this algorithm has advantages compared to other methods, such as the ability to calculate the frequency of the process, to handle noise, and to combine the frequency between events and trace in the event log to build a process model. The steps being taken in this study began with the discovery process, followed by conformance checking, and completed with process model extension in enhancement step, which was finding knowledge based on analysing process model and event log. This study was using a specifically designed program to achieve the goals of this study. This study used VINST incident records with 149 cases consisting of 2.771 events. By applying heuristics miner, a process model discovered with precision, recall, and f-measure more than 96%. Knowledge gained from this process model extension were the analysis of time and organizational perspectives in forms of timestamp-impact attribute relation and originator-impact attribute relation.

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.