The Proposition of a Framework for Semantic Process Mining

This chapter introduces semantic business process mining of SAP transaction logs. SAP systems are promising domains for semantic process mining as they contain transaction logs that are linked to large amounts of structured data. A challenge with process mining these transaction logs is that the core of SAP systems was not originally designed from the business process management perspective. The business process layer was added later without full rearrangement of the system. As a result, system logs produced by SAP are not process-based, but transaction-based. This means that the system does not produce traces of process instances that are needed for process mining. In this chapter, we show how data available in SAP systems can enrich process instance logs with ontologically structured concepts, and evaluate techniques for mapping executed transaction sequences with predefined process hierarchies.

This chapter introduces semantic business process mining of SAP transaction logs. SAP systems are promising domains for semantic process mining as they contain transaction logs that are linked to large amounts of structured data. A challenge with process mining these transaction logs is that the core of SAP systems was not originally designed from the business process management perspective. The business process layer was added later without full rearrangement of the system. As a result, system logs produced by SAP are not process-based, but transaction-based. This means that the system does not produce traces of process instances that are needed for process mining. In this chapter, we show how data available in SAP systems can enrich process instance logs with ontologically structured concepts, and evaluate techniques for mapping executed transaction sequences with predefined process hierarchies.

Semantics has been a major challenge when applying the process mining (PM) technique to real-time business processes. The several theoretical and practical efforts to bridge the semantic gap has spanned the advanced notion of the semantic-based process mining (SPM). Fundamentally, the SPM devotes its methods to the idea of making use of existing (semantic) technologies to support the analysis of PM techniques. In principle, the semantic-based process mining method is applied through the acquisition and representation of abstract knowledge about the domain processes in question. To this effect, this paper demonstrates how the semantic concepts and process modelling (reasoning) methods are used to improve the outcomes of PM techniques from the syntactic to a more conceptual level. To do this, the study proposes an SPM-based framework that shows to be intelligent with a high level of semantic reasoning aptitudes. Technically, this paper introduces a process mining approach that uses information (semantics) about different activities that can be found in any given process to make inferences and generate rules or patterns through the method for annotation, semantic reasoning, and conceptual assertions. In turn, the method is theoretically applied to enrich the informative values of the resultant models. Also, the study conducts and systematically reviews the current tools and methods that are used to support the outcomes of the process mining as well as evaluates the results of the different methods to determine the levels of impact and its implications for 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 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.

Modern realities dictate the requirements for effective optimization of production. Modernization of business, economics, production, healthcare and medicine in general is an urgent task of our time. One of the technologies for effective process analysis is process mining. Process mining technology allows you to extract knowledge from an event log, forming a process model based on it. Such tools make it possible to identify ineffective management structures and business processes, malfunctions in software, and incompetence of employees, solving many problems of organizing effective management in various industries. Today there are many software tools that implement process analysis methods: Disco, Apromore, ProM. Celonis Process Mining, UiPath Process Mining, etc. Selecting software for certain tasks is a complex process, which subsequently affects the implementation of the project as a whole. Therefore, it is extremely important to comprehensively analyze all the capabilities and limitations of existing process mining tools. The following criteria were selected: cost of the framework; availability of source code; programming language; the ability to build a decision tree based on the process; technical support; technical documentation; developed community of developers; the ability to create and add plugins; experience with the framework. Healthcare was chosen as the target application area. According to the results of the analysis, it was concluded that the use of the ProM framework is effective for solving problems in the field of healthcare.

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.

Healthcare processes are inherently complex as each patient is unique and medical staff deviate from protocols, often for valid reasons. Event logs collected by modern process-aware (healthcare) information systems provide a wealth of data and can be used to analyze the adherence to these protocols. Process mining is a young research area combining data science (machine learning, data mining, etc.) and business process management. Its main contributions have been techniques for process discovery (the automatic learning of process models from event data) and conformance checking (aligning observed and modeled behavior). However, existing techniques face challenging issues discovering high-quality process models in a healthcare setting. In this chapter we introduce the key concepts of process mining such as event logs, process models, and process discovery. We then show the application of two recent process mining techniques on a public event data set from the healthcare domain to demonstrate how some of the common pitfalls can be overcome. A first presented technique projects data statistics on a process model, allowing the analysis of correlations between patient characteristics and the executed activities (e.g., type of treatment). A second technique analyzes process performance without a specific need for a process model by considering contextual information. It correlates process characteristics with process performance.

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 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

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

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.