АНАЛИЗ ВОЗМОЖНОСТЕЙ ИНСТРУМЕНТОВ РЕАЛИЗАЦИИ ТЕХНОЛОГИИ PROCESS MINING

Process mining technology selection with spherical fuzzy AHP and sensitivity analysis

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.

As one of the hot topics in Business Process Management (BPM), process mining aims at constructing models to explain what is actually happening from different perspectives based on the process-related information that automatically extracted from event logs. Because the semantics of the data that recorded in event logs are not usually explicit, current mining approaches are somewhat limited. A number of studies have been carried out in the combination use of formalized semantic models and process mining technologies to obtain the semantic mining capability. However, among these researches, there is lack of a guideline that can clearly illustrate different stages during the semantic process mining. The objective of this study is to present a general framework, which unambiguously expresses the main stages of the semantic process mining. Based on this framework, an example about carbon footprint analysis is used to show the possibility of obtaining advantages from semantic process mining.

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

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.

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.

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 analyzes event logs from business processes to enhance understanding, verification, and improvement of these processes. Event logs typically originate from multiple sources and require extensive preprocessing before applying process mining techniques. This preprocessing includes filtering, composing, joining events into traces, and fixing or calculating attribute values. Existing process mining tools offer ad-hoc manual actions through graphical interfaces and simple domain-specific languages, but these methods have limitations, and there is no standardized approach for processing event logs. The primary contribution of this work is the Process Query Language (PQL), a domain-specific language (DSL) for processing event logs that surpasses current techniques. We begin by analyzing several existing DSLs concerning key success factors, followed by an evaluation of current business process DSLs to identify their limitations. Based on this analysis, we propose the PQL specification as a draft technical standard. PQL’s design decisions are validated through an interpreter implementation and its application to numerous use cases.

Business process management (BPM) is a well-established discipline comprising a set of principles, methods, techniques, and tools to continuously improve the performance of business processes. Traditionally, most BPM decisions and activities are undertaken by business stakeholders based on manual data collection and analysis techniques. This is time-consuming and potentially leads to suboptimal decisions, as only a restricted subset of data and options are considered. Over the past decades, a rich set of data-driven techniques has emerged to support and automate various activities and decisions across the BPM lifecycle, particularly within the process mining field. More recently, the uptake of artificial intelligence (AI) methods for BPM has led to a range of approaches for proactive business process monitoring. Given their common data requirements and overlapping goals, process mining and AI-driven approaches to business process optimization are converging. This convergence is leading to a promising emerging concept, which we call (AI-)augmented process execution: a collection of data analytics and artificial intelligence methods for continuous and automated improvement and adaptation of business processes. This article gives an outline of research at the intersection between process mining and AI-driven process optimization, classifies the researched techniques based on their scope and objectives, and positions augmented process execution as an additional layer on top of this stack.

Process mining-driven analysis of COVID-19’s impact on vaccination patterns

Process mining analyzes business processes using event logs. Existing tools generate models to facilitate this task and improve the original business process, but the results are often unsatisfactory due to the complexity of the obtained models. Among the challenges faced in this context, we identify the misalignment with specific business requirements, preventing managers from accessing key data and making effective decisions. In this paper, we propose a requirement-driven approach centered on meta-modeling, which can help the development of process mining tools specially tailored to organizational needs. Thus, we introduce a requirement-driven method to address the critical challenge of model misalignment with required information. The method employs Model-Driven Engineering to simplify how process mining results are formulated, analyzed, and interpreted. The proposed method is iterative and involves several steps. First, a service manager defines a specific business question. Second, service managers and developers collaboratively establish a meta-model representing the target data. Third, developers extract relevant data using appropriate analysis techniques and visualize it. Finally, service managers and developers jointly interpret these visualizations to inform strategic decisions. This requirement-driven methodology empowers developers to concentrate on relevant information. Unlike general-purpose frameworks (e.g., ProM, Disco), this method emphasizes specificity, iterative refinement, and close stakeholder collaboration. By reducing cognitive overload through focused modeling and filtering of irrelevant data, organizations adopting this approach can achieve faster response times to business questions and develop specialized in-house analytical tools. This requirement-driven methodology, therefore, improves decision-making capabilities within process mining and across related analytical domains. We illustrate our methodology through a real business process taken from the literature owned by the VOLVO group. We use several examples of process mining to illustrate the benefits of the proposed methodology compared to existing tools which are unable to provide the required information. • A requirement-driven meta-model method for standardized process-mining assessment. • An iterative approach for low-cost development of tailored in-house tools. • Improves decisions through focused data extraction and need-aligned analysis.

Process mining is a prominent discipline in business process management. It collects a variety of techniques for gathering information from event logs, each fulfilling a different mining purpose. Event logs are always necessary for assessing and validating mining techniques in relation to specific purposes. Unfortunately, event logs are hard to find and usually contain noise that can influence the validity of the results of a mining technique. In this paper, we propose a framework, named PURPLE, for generating, through business model simulation, event logs tailored for different mining purposes, i.e., discovery, what-if analysis, and conformance checking. It supports the simulation of models specified in different languages, by projecting their execution onto a common behavioral model, i.e., a labeled transition system. We present eleven instantiations of the framework implemented in a software tool by-product of this paper. The framework is validated against reference log generators through experiments on the purposes presented in the paper.

Business processes change over time in response to changing circumstances, it is crucial for process managers to discover and understand such changes from event logs. While existing techniques in process mining cannot efficiently detect and locate concept drifts in uncompleted event logs. Hence, an appropriate method, which could prompt to unravel the process evolution, may greatly assist organizations to manage the flexibility and change of business processes in the context of business process management (BMP). In this paper, we first proposed an extensible feature and employed it with the sliding window technique and heuristic miner for detecting and locating concept drifts in uncompleted event logs. Then, Genetic Process Mining (GPM) was improved by the Weight Heuristic Miner (WHM) and Differential Evolution (DE) for mining the new process model of an evolving process. Finally, experimental results on four event logs which are created based on a real-world business process have validated the proposed method.

Towards interactive event log forensics: Detecting and quantifying timestamp imperfections

A high level of competitiveness of a company is one of the key factors that guarantees stable long-term economic development and a stable position of the enterprise in the market. Competition is a trigger for activating innovative processes and introducing modern information technologies into the enterprise infrastructure. The lack of control and regulation system, the low level of automation of processes, their inconsistency with strategic goals and duplication significantly reduce the overall level of competitiveness of the enterprise and negatively affects economic profit. As a tool that can increase a company’s competitive position in the market, the work explores the digital technology of in-depth analysis, that is Process Mining. This article aims to explore how businesses gain a competitive edge by using business process mining technology. It also examines the current trends in implementing process analytics across different industries. Process Mining technology is the focus of the study. The subject is the impact of business process mining technology on the competitiveness of a company. The study used theoretical and empirical scientific approaches, methods of statistical analysis, and systems analysis techniques. By analyzing competitive advantages, a correlation between the implementation of process analytics and the company’s increased efficiency was established. The most popular areas of application of Process Mining are procurement, customer service and information technology. The leaders in the number of successful implementations of business process analysis technology are the financial services, telecommunications, and oil and gas industries.