An efficient stochastic process discovery framework based on optimization

Process mining uses event logs to improve business processes, but such logs may contain privacy information. One popular research problem is the privacy protection of event logs. Publishing logs with differential privacy is one of major research directions. Existing research achieves privacy protection primarily by injecting random noise into event logs, or merging similar information. The former ignores the fact that injecting random noise will produce apparently unreasonable activity traces, and the latter will cause a loss of process information in the process mining perspective. To solve the above problems, this article proposes a differential algorithm based on randomized response to model Petri nets for the original event logs, select the important labels in the logs by the weak sequential relationship of control flow between activities, inject noise into the Petri net model based on the important labels using the randomized response approach, and establish a differential Petri net model. Experiments on public datasets show that the event logs produced by the approach proposed do not contain unreasonable traces. Compared with the baseline approach, the proposed approach performs better on Fitness metrics with consistent privacy requirements and retains more process variants, reducing the loss of original event log process information.

Process mining techniques enable the analysis of a wide variety of processes using event data. For example, event logs can be used to automatically learn a process model (e.g., a Petri net or BPMN model). Next to the automated discovery of the real underlying process, there are process mining techniques to analyze bottlenecks, to uncover hidden inefficiencies, to check compliance, to explain deviations, to predict performance, and to guide users towards “better” processes. Dozens (if not hundreds) of process mining techniques are available and their value has been proven in many case studies. However, existing techniques focus on the analysis of a single process rather than the comparison of different processes. In this paper, we propose comparative process mining using process cubes. An event has attributes referring to the dimensions of the process cube. Through slicing, dicing, rolling-up, and drilling-down we can view event data from different angles and produce process mining results that can be compared. To illustrate the process cube concept, we focus on educational data. In particular, we analyze data of students watching video lectures given by the first author. The dimensions of the process cube allow us to compare the process of students that passed the course versus the process of students that failed. We can also analyze differences between male and female students, between different parts of the course, and between Dutch students and international students. The initial analysis provided in this paper is used to elicit requirements for better tool support facilitating comparative process mining.

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 topic of process mining has attracted the attention of both researchers and tool vendors in the Business Process Management (BPM) space. The goal of process mining is to discover process models from event logs, i.e., events logged by some information system are used to extract information about activities and their causal relations. Several algorithms have been proposed for process mining. Many of these algorithms cannot deal with concurrency. Other typical problems are the presence of duplicate activities, hidden activities, non-free-choice constructs, etc. In addition, real-life logs contain noise (e.g., exceptions or incorrectly logged events) and are typically incomplete (i.e., the event logs contain only a fragment of all possible behaviors). To tackle these problems we propose a completely new approach based on genetic algorithms. As can be expected, a genetic approach is able to deal with noise and incompleteness. However, it is not easy to represent processes properly in a genetic setting. In this paper, we show a genetic process mining approach using the so-called causal matrix as a representation for individuals. We elaborate on the relation between Petri nets and this representation and show that genetic algorithms can be used to discover Petri net models from event logs.KeywordsProcess MiningPetri NetsGenetic AlgorithmsProcess DiscoveryBusiness Process IntelligenceBusiness Activity Monitoring

Event logs recorded during the execution of business processes constitute a valuable source of information. Applying process mining techniques to them, event logs may reveal the actual process execution and enable reasoning on quantitative or qualitative process properties. However, event logs often contain sensitive information that could be related to individual process stakeholders through background information and cross-correlation. We therefore argue that, when publishing event logs, the risk of such re-identification attacks must be considered. In this paper, we show how to quantify the re-identification risk with measures for the individual uniqueness in event logs. We also report on a large-scale study that explored the individual uniqueness in a collection of publicly available event logs. Our results suggest that potentially up to all of the cases in an event log may be re-identified, which highlights the importance of privacy-preserving techniques in process mining.

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.

Since IoT devices supporting business processes (BPs) in sectors like manufacturing, logistics or healthcare can collect data on the execution of the processes, there is a growing awareness of the opportunity to use IoT data for process mining (PM). However, several challenges need to be addressed to enable IoT-enhanced PM, among which the need for a standard IoT-enhanced event log model. In this paper, we identify four main challenges for the integration of IoT data in event logs, from which 10 requirements for an IoT-enhanced event log model are derived. We then analyse the two main current event log models, the extensible event stream (XES) and the object-centric event log (OCEL), and confront them with the requirements, showing that both present important limitations to storing IoT-enhanced event logs. Based on this analysis, we conclude that a comprehensive data model is required to develop more advanced IoT-enhanced PM techniques .KeywordsProcess miningIoTIoT-enhanced event log

Process Mining helps to analyze and interpret business processes using event logs of information systems as ground truth. It has been successfully applied to process-oriented enterprise systems but is less suited for communication- and document-oriented Enterprise Collaboration Systems (ECS). This is due to two core characteristics of ECS processes and process logging. On the one hand, ECS processes are highly unstructured as their usage is not related to predefined business processes. The usage of ECS is ad-hoc and flexible. On the other hand, ECS commonly provide rather low-level process logs, i. e., logged events are very fine-granular, and, thus hard to interpret. Both characteristics lead to so-called spaghetti process models, which are barely interpretable when applying “traditional” process mining to ECS. This work focuses on the second aspect, i. e., to solve the problem of too fine-granular process logs of ECS. A common solution and preprocessing step for this granularity challenge is event abstraction, i.e., converting low-level logs into more abstract high-level logs before applying further process mining analysis techniques. ECS logs come with special characteristics that have so far not been fully addressed by existing event abstraction approaches. We aim to close this gap with a tailored ECS event abstraction (ECSEA) approach that trains a model by comparing observed actual user activities (also named high-level traces) with the system-generated low-level traces. The model allows us to automatically convert historic and future low-level traces into abstracted high-level traces that can be used for Process Mining. This article describes the ECS event log characteristics, introduces the proposed ECSEA approach with the underlying algorithms, and shows a corresponding evaluation. Our two-fold evaluation shows that the algorithm produces accurate results. In the evaluation, we use both synthetic and real-world event data taken from a productive ECS with more than 4000 users currently running at a university. • Derivation of relevant low-level event log characteristics: Based on a comparison of low-level and related high-level event logs, we derive low-level event log characteristics. They serve as requirements for a novel event abstraction approach and are further used for a comparison of related approaches. • Conceptualization of a novel supervised event abstraction approach: The main contribution is a novel event abstraction algorithm, which is described and evaluated in this article.

AITIA-PM: Discovering the true causes of events in a process mining context

The lecture preparation process marks the start of lectures in the new semester. Telkom University has utilized online-based information technology, Integrated Academic Information System (i-Gracias). One of the applications available on i-Gracias is Outcome-Based Education (OBE). The significant increase in I-Gracias users indeed produces many data records in event logs. This data has become an opportunity to evaluate and improve the lecture preparation process already running. Data processing is needed to gain knowledge from the event log, one of which is process mining. This study starts from the discovery stage to conformance checking. Before the process mining is carried out, pre-processing of the data will be carried out to produce good quality logs and define the case id, activity, and timestamp. Existing event logs will be processed using ProM tools using the Heuristic Miner Algorithm to model the process and achieve the best fitness, precision, generalization, and simplicity values. The Heuristic Miner algorithm was adopted in this study because of its capability to manage event logs with noise and display the primary behavior of existing business processes. The conformance checking result shows a fitness value of 0.98, precision's value of 0.29, generalization's value of 0.51, and simplicity's value of 0.45 for the lecture preparation process. It means the process model has a good representation of the data event log. This study hopes to add new insight to improve the lecture preparation process at Telkom University.

Towards interactive event log forensics: Detecting and quantifying timestamp imperfections

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.

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 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 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.