Time prediction based on process mining

Real-life business process event logs may suffer from significant data quality problems negatively influencing process mining analysis. Over time, a range of approaches has been developed to detect and repair these quality problems. Validation of these approaches tends to be challenging due to the lack of a ground truth. Moreover, the identification and definition of event log quality problems have been tackled mainly through a pattern-based approach, with systematic and extensible methods currently lacking. In this article, we present FLAWD , a formal language for describing event log data quality issues that enables solutions addressing the shortcomings of process mining data quality research identified above. FLAWD can be used to formally describe and possibly reason over event log data quality errors, as well as to guide the development of tools for controlled and sophisticated “polluting” of event logs through which benchmark datasets may be systematically created. We present the abstract syntax grammar of FLAWD and an open-source software tool based on it that allows for the insertion of all so-called event log imperfection patterns in a stochastic manner. We show how FLAWD has been used in our research to generate benchmark datasets and how it can be used to formally describe and replicate a range of errors found in real-life event logs.

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.

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.

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 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 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 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.

Process mining, which aims to mine a high-quality process model from event log, provides a powerful tool to support the design, enactment, management, and analysis of operational business processes. However, the task is not easy because the algorithm needs to discover various complex process structures, handle noisy and incomplete event logs and balance multiple performance indicators. In this paper, a novel algorithm (called PSOMiner) for process mining is proposed, which consists of a discrete particle swarm optimization algorithm and guided local mutation. The former is in charge of searching the solution space of causal matrix and the latter is used to help the algorithm skip out the local optimum when it suffers from premature. A fine-grained scoring strategy which used to assign a score to each position of a particle (i.e. causal matrix) is presented to direct the mutation. The experiments were performed on 28 synthetic event logs with/without noise and 4 real-life event logs, and three classical algorithms of process mining (ETM, Hybrid ILP Miner, HM) were chosen for comparison. The results show that (1) PSOMiner achieved the best f-score on 25 synthetic event logs; (2) The average f-score of PSOMiner is 0.825 on 4 real-life event logs, which is superior to ETM whose average f-score is 0.703.

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.

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.

Process mining is the area of research that embraces the automated discovery, conformance checking and enhancement of process models. Declarative process mining approaches offer capabilities to automatically discover models of flexible processes from event logs. However, they often suffer from performance issues with real-life event logs, especially when constraints to be discovered go beyond a standard repertoire of templates. By leveraging relational database performance technology, a new approach based on SQL querying has been recently introduced, to improve performance though still keeping the nature of discovered constraints customisable. In this paper, we provide an in-depth analysis of configuration parameters that allow for a speed-up of the answering time and a decrease of storage space needed for query processing. Thereupon, we provide configuration recommendations for process mining with SQL on relational databases.

Everything at the proper time: Repairing identical timestamp errors in event logs with Generative Adversarial Networks

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

Flexible business processes can often be modelled more easily using a declarative rather than a procedural modelling approach. Process mining aims at automating the discovery of business process models. Existing declarative process mining approaches either suffer from performance issues with real-life event logs or limit their expressiveness to a specific set of constaint types. Lately, RelationalXES, a relational database architecture for storing event log data, has been introduced. In this paper, we introduce a mining approach that directly works on relational event data by querying the log with conventional SQL. By leveraging database performance technology, the mining procedure is fast without limiting itself to detecting certain control-flow constraints. Queries can be customised and cover process perspectives beyond control flow, e.g., organisational aspects. We evaluated the performance and the capabilities of our approach with regard to several real-life event logs.

Process Mining is a set of techniques that aim at discovering, monitoring and improving real processes by using logs of events created and stored by corporate information systems. The growing use of information and communication technologies and the imminent wide deployment of the Internet of Things enable the massive collection of events, which are going to be studied so as to improve all kinds of systems efficiency. Despite its enormous benefits, analyzing event logs might endanger individuals privacy, especially when those logs contain personal and confidential information, such as healthcare data. This article contributes to an emerging research direction within the process mining field, known as Privacy-Preserving Process Mining (PPPM), which embraces the privacy-by-design principle when conducting process mining analyses. We show that current solutions based on pseudonyms and encryption are vulnerable to attacks based on the analysis of the distribution of events combined with well-known location-oriented attacks such as the restricted space identification and the object identification attacks. With the aim to counteract these attacks, we present u-PPPM, a novel privacy-preserving process mining technique based on the uniformization of events distributions. This approach protects the privacy of the individuals appearing in event logs while minimizing the information loss during process discovery analyses. Experimental results, conducted using six real-life event logs, demonstrate the feasibility of our approach in real settings.