Process mining has been intensively used for business processes that are extensively supported by information systems. The tight integration of information processing and process execution, as leveraged in the service sector, is however often absent in land-centric processes such as farming. Land-centric processes exhibit some challenging characteristics that make it difficult to monitor them in real-time: they unfold continuously over time, yet with clearly identifiable states. In this paper, we address the challenge of monitoring land-centric processes. We introduce a framework to generate event logs of land-centric processes by utilizing remote sensing systems such as satellites. We demonstrate the feasibility of our approach using publicly available data on agricultural processes in the United States. • We introduce the class of land-centric processes and outline several examples from multiple domains. • We present a framework to retrieve process mining data from remote sensing data such as satellite images. • Our approach enables us to infer the timing of state changes of land-centric processes. • We evaluate our approach through a case study in agriculture using available data from the United States. • We show how process mining can be used to derive insights into land-centric processes.

Abstract Resource analysis in process mining focuses on understanding the behavior and performance of resources involved in business processes. While previous research has provided various insights into resource-related aspects, the visualization of these insights remains insufficiently developed. This paper addresses this gap by proposing a novel resource analytics technique that integrates metrics from four critical resource-related areas: resource allocation, resource performance, workload distribution, and capacity utilization. The technique incorporates interactive visualizations and process model views to support process analysts in performing resource analysis tasks such as identifying bottlenecks and inefficiencies. Results from a user evaluation demonstrate that the proposed technique enhances the accuracy of resource analysis tasks and is highly regarded for its ease of use and perceived usefulness.

Process Mining has been widely adopted by businesses and has been shown to help organizations analyze and optimize their processes. However, so far, little attention has gone into the cross-organizational comparison of processes, since many companies are hesitant to share their data. In this paper, we explore the processes of German state parliaments that are often legally required to share their data and run the same type of processes for different geographical regions. This paper is the first attempt to apply process mining to parliamentary processes and, therefore, contributes toward a novel interdisciplinary research area that combines political science and process mining. In our case study, we analyze legislative processes of three German state parliaments and generate insights into their differences and best practices. We provide a discussion of the relevance of our results that are based on knowledge exchange with a political scientist and a domain expert from the German federal parliament.

Process mining visualizations provide analysts with relevant cues for identifying bottlenecks, waiting times, or other operational inefficiencies. Such visualizations show instances of the process and the corresponding model separately. This spatial separation requires users to mentally integrate detailed and generalized data representations, hindering effective traceability between the two perspectives. This study introduces an interactive visual analytics technique for process mining that seamlessly links the process model view with the corresponding case view through a zoom-based visualization. We first define a two-dimensional coordinate system for plotting individual event sequences. Then, we define semantic layers using contour diagrams to incrementally aggregate nodes and edges into a directly-follows graph. Finally, the resulting Time-Order Map enables non-geometric semantic zooming, allowing for a seamless transition between multiple abstraction levels of a process graph, including its instance representation. We evaluate our technique using realworld datasets to demonstrate its effectiveness in exploring the connection between the process model and the underlying cases. In this way, our approach offers a solution for semantic zooming in process mining, facilitating navigation between process view levels.

Decision models play an important role in various areas of information systems research, including system analysis and design, compliance management, and various application domains. Decision models must be effectively presented so that analysts can assure their correctness and completeness. So far, empirical research on the cognitive effectiveness of decision models has provided partially inconclusive results. Our paper provides novel insights into the drivers of decision model comprehension by moving from a classification based on representation archetypes to granular representation features. Using an experimental research design, we discover that the decision model type must be assessed in conjunction with other representational factors, such as representation structure (expanded vs. frugal) and representation design (monochromatic vs. colours). In this way, we extend prior arguments of cognitive fit theory by demonstrating that colour can be used to compensate for a misfit of decision model and task, and structural features can further increase model comprehension. We further studied the root causes of the observed effects by using eye-tracking. Our findings have implications for both cognitive information systems research and practice, as they can be used to guide decision model users and tool vendors.

A key concern of automatic process discovery is providing insights into business process performance. Process analysts are specifically interested in waiting times and delays for identifying opportunities to speed up processes. Against this backdrop, it is surprising that current techniques for automatic process discovery generate directly-follows graphs and comparable process models without representing the time axis explicitly. This paper presents four layout strategies for automatically constructing process models that explicitly align with a time axis. We exemplify our approaches for directly-follows graphs. We evaluate their effectiveness by applying them to real-world event logs with varying complexities. Our specific focus is on their ability to handle the trade-off between high control-flow abstraction and high consistency of temporal activity order. Our results show that timeline-based layouts provide benefits in terms of an explicit representation of temporal distances. They face challenges for logs with many repeating and concurrent activities.

Process mining research has made tremendous progress in analyzing, visualizing, and predicting the performance of business processes through computational techniques. However, little attention has been brought to understanding why and how business processes behave as they do. Process mining results alone are not sufficient to arrive at meaningful interpretations about the dynamics and changes of a given business process. Rather, we need to account for contextual factors that underlie and explain the behavior of processes. In this paper, we make two central contributions. First, we develop a framework that depicts relevant factors to make sense of process mining results. The framework is intended to help researchers and practitioners explain why and how processes change across a variety of contexts. Second, we demonstrate the application of our framework within a real-world case: a customer onboarding process in a European financial institution.

Recent technologies like ChatGPT inspire ideas of increasing automation for professional tasks in various domains. This includes among others requirements engineering. So far, however, the divide between useful and harmful automation has been blurry and in flux. In this keynote, I will discuss foundational concepts with which automation has been analyzed. Using these concepts, I will discuss the opportunities arising for requirements engineering from the advent of natural language processing, image processing, and event sequence analysis techniques. Furthermore, I will discuss important pitfalls that have been well documented for other automation technologies in the past.

Conceptual models are used to help professionals understand complex information systems and solve problems during systems analysis and design. Because single model often do not represent all relevant information, typically multiple models are used in combination. To design effective combinations of models, we propose a systematic approach that uses color highlighting to foreground overlapping concepts between multiple models to help readers identify corresponding information between models. We conducted two empirical studies – an online experiment and an eye-tracking experiment – to evaluate the cognitive efficacy of this approach. Our findings suggest that color highlighting can somewhat improve participants’ domain understanding but not the efficiency of problem-solving. Findings from the eye-tracking study suggest that the use of color can have both beneficial and harmful effects, depending on the extent of overlap. • Color highlighting aids the perceptual integration and synchronization of overlap between multiple conceptual models. • Color highlighting can improve information integration processes, which in some cases aids problem-solving. • Color highlighting has both positive and negative effects when it comes to the information search processes.

Process analytics is a collection of data-driven techniques for, among others, making predictions for individual process instances or overall process models. At the instance level, various novel techniques have been recently devised, tackling analytical tasks such as next activity, remaining time, or outcome prediction. However, there is a notable void regarding predictions at the process model level. It is the ambition of this article to fill this gap. More specifically, we develop a technique to forecast the entire process model from historical event data. A forecasted model is a will-be process model representing a probable description of the overall process for a given period in the future. Such a forecast helps, for instance, to anticipate and prepare for the consequences of upcoming process drifts and emerging bottlenecks. Our technique builds on a representation of event data as multiple time series, each capturing the evolution of a behavioural aspect of the process model, such that corresponding time series forecasting techniques can be applied. Our implementation demonstrates the feasibility of process model forecasting using real-world event data. A user study using our Process Change Exploration tool confirms the usefulness and ease of use of the produced process model forecasts.

Process mining supports the analysis of the actual behavior and performance of business processes using event logs. An essential requirement is that every event in the log must be associated with a unique case identifier (e.g., the order ID of an order-to-cash process). In reality, however, this case identifier may not always be present, especially when logs are acquired from different systems or extracted from non-process-aware information systems. In such settings, the event log needs to be pre-processed by grouping events into cases — an operation known as event correlation. Existing techniques for correlating events have worked with assumptions to make the problem tractable: some assume the generative processes to be acyclic, while others require heuristic information or user input. Moreover, they abstract the log to activities and timestamps, and miss the opportunity to use data attributes. In this paper, we lift these assumptions and propose a new technique called EC-SA-Data based on probabilistic optimization. The technique takes as inputs a sequence of timestamped events (the log without case IDs), a process model describing the underlying business process, and constraints over the event attributes. Our approach returns an event log in which every event is associated with a case identifier. The technique allows users to flexibly incorporate rules on process knowledge and data constraints. The approach minimizes the misalignment between the generated log and the input process model, maximizes the support of the given data constraints over the correlated log, and the variance between activity durations across cases. Our experiments with various real-life datasets show the advantages of our approach over the state of the art.

Process mining analyzes business processes’ behavior and performance using event logs. An essential requirement is that events are grouped in cases representing the execution of process instances. However, logs extracted from different systems or non-process-aware information systems do not map events with unique case identifiers (case IDs). In such settings, the event log needs to be pre-processed to group events into cases – an operation known as event correlation. Existing techniques for correlating events work with different assumptions: some assume the generating processes are acyclic, others require extra domain knowledge such as the relation between the events and event attributes, or heuristic information about the activities’ execution time behavior. However, the domain knowledge is not always available or easy to acquire, compromising the quality of the correlated event log. In this paper, we propose a new technique called EC-SA-RM, which correlates the events using a simulated annealing technique and iteratively learns the domain knowledge as a set of association rules. The technique requires a sequence of timestamped events (i.e., the log without case IDs) and a process model describing the underlying business process. At each iteration of the simulated annealing, a possible correlated log is generated. Then, EC-SA-RM uses this correlated log to learn a set of association rules that represent the relationship between the events and the changing behavior over the events’ attributes in an understandable way. These rules enrich the input and improve the event correlation process for the next iteration. EC-SA-RM returns an event log in which events are grouped in cases and a set of association rules that explain the correlation over the events. We evaluate our approach using four real-life datasets.

The assessment of behavioral rules with respect to a given dataset is key in several research areas, including declarative process mining, association rule mining, and specification mining. An assessment is required to check how well a set of discovered rules describes the input data, and to determine to what extent data complies with predefined rules. Particularly in declarative process mining, Support and Confidence are used most often, yet they are reportedly unable to provide a sufficiently rich feedback to users and cause rules representing coincidental behavior to be deemed as representative for the event logs. In addition, these measures are designed to work on a predefined set of rules, thus lacking generality and extensibility. In this paper, we address this research gap by developing a measurement framework for temporal rules based on (LTLpf). The framework is suitable for any temporal rules expressed in a reactive form and for custom measures based on the probabilistic interpretation of such rules. We show that our framework can seamlessly adapt well-known measures of the association rule mining field to declarative process mining. Also, we test our software prototype implementing the framework on synthetic and real-world data, and investigate the properties characterizing those measures in the context of process analysis.

The design and improvement of business processes is of central importance for realizing benefits of information systems. A broad spectrum of methods has been proposed since the 1990s, which ranges into several dozen. It is unclear whether this large number trivially stems from copying and relabeling or whether there are substantial differences in these methods that can be tied to their applicability in different contexts or to the pursuit of different goals. Accordingly, we ask: Which activities do process improvement methods have in common, how do they differ, and why? In this paper, we approach these research questions using a multi-method design integrating techniques from systematic literature review, process mining, and statistical analysis. Our contributions are as follows. First, we provide a framework with 264 activities clustered in six stages that could be used for incrementally and radically improving processes. Second, we find that methods map to different configurations of the three dimensions described by the redesign orbit. Third, we uncover similarities and differences of the different methods contingent to the factors industry, objectives and whether a method is proposed or applied. Fourth, we observe three distinct clusters of method activities, which show that different strategies play a role when choosing a method for improvement. Our findings have important implications for the application of improvement methods in various improvement scenarios.

Process mining is a research area focusing on the design of algorithms that can automatically provide insights into business processes. Among the most popular algorithms are those for automated process discovery, which have the ultimate goal to generate a process model that summarizes the behavior recorded in an event log. Past research had the aim to improve process discovery algorithms irrespective of the characteristics of the input log. In this paper, we take a step back and investigate the connection between measures capturing characteristics of the input event log and the quality of the discovered process models. To this end, we review the state-of-the-art process complexity measures, propose a new process complexity measure based on graph entropy, and analyze this set of complexity measures on an extensive collection of event logs and corresponding automatically discovered process models. Our analysis shows that many process complexity measures correlate with the quality of the discovered process models, demonstrating the potential of using complexity measures as predictors of process model quality. This finding is important for process mining research, as it highlights that not only algorithms, but also connections between input data and output quality should be studied.

Abstract Companies often claim to follow specific software development methodologies (SDM) when performing their software development process. These methodologies are often supported by dedicated tools that keep track of work activities carried out by developers. The purpose of this paper is to provide a novel approach that integrates analytical insights from both the perceptions of SDM stakeholders and software development tools logs to provide SDM improvement recommendations. This paper develops a new process improvement approach that combines two significantly different sources of data on the same phenomenon. First, it uses a questionnaire to gather software development stakeholder SDM perceptions (managers and developers). Second, it leverages process mining to analyze software development tools logs to obtain additional information on software development activities. Finally, it develops recommendations based on concurrent analysis of both sources. Our novel process improvement approach is evaluated in three directions: Does the presented approach (RQ1) enable managers to gain additional insights into employees' performance, (RQ2) deliver additional insights into project performance, and (RQ3) enable development of additional SDM improvement recommendations? We find that integrated analysis of software development perception data and software development tools logs opens new possibilities to more precisely identify and improve specific SDM elements. The evaluation of our novel process improvement approach follows a single case study design. Our approach can only be used in enterprises in which software development tools logs are available. The study should be repeated in different cultural settings. We practically show how concurrently analyzing data about developer SDM perceptions and event log data from software development tools enables management to gain additional insights in the software development process regarding the performance of individual developers. The main theoretical contribution of our paper is a novel process improvement approach that effectively integrates data from management and developer perspectives and software development tools logs.

Abstract Process mining is a family of analytical techniques that extract insights from an event log and present them to an analyst. A key analysis task is to understand the distinctive features of different variants of the process and their impact on process performance. Techniques for log-delta analysis (or variant analysis) put a strong emphasis on automatically extracting explanations for differences between variants. A weakness of them is, however, their limited support for interactively exploring the dividing line between typical and atypical behavior. In this paper, we address this research gap by developing and evaluating an interactive technique for log-delta analysis, which we call InterLog . This technique is developed based on the idea that the analyst can interactively define filter ranges and that these filters are used to partition the log L into sub-logs $$L_1$$ L 1 for the selected cases and $$L_2$$ L 2 for the deselected cases. In this way, the analyst can step-by-step explore the log and manually separate the typical behavior from the atypical. We prototypically implement InterLog and demonstrate its application for a real-world event log. Furthermore, we evaluate it in a preliminary design study with process mining experts for usefulness and ease of use.

Event sequence data is increasingly available in various application domains, such as business process management, software engineering, or medical pathways. Processes in these domains are typically represented as process diagrams or flow charts. So far, various techniques have been developed for automatically generating such diagrams from event sequence data. An open challenge is the visual analysis of drift phenomena when processes change over time. In this article, we address this research gap. Our contribution is a system for fine-granular process drift detection and corresponding visualizations for event logs of executed business processes. We evaluated our system both on synthetic and real-world data. On synthetic logs, we achieved an average F-score of 0.96 and outperformed all the state-of-the-art methods. On real-world logs, we identified all types of process drifts in a comprehensive manner. Finally, we conducted a user study highlighting that our visualizations are easy to use and useful as perceived by process mining experts. In this way, our work contributes to research on process mining, event sequence analysis, and visualization of temporal data.

Abstract It has been argued that reporting software engineering experiments in a standardized way helps researchers find relevant information, understand how experiments were conducted and assess the validity of their results. Various guidelines have been proposed specifically for software engineering experiments. The benefits of such guidelines have often been emphasized, but the actual uptake and practice of reporting have not yet been investigated since the introduction of many of the more recent guidelines. In this research, we utilize a mixed-method study design including sequence analysis techniques for evaluating to which extent papers follow such guidelines. Our study focuses on the four most prominent software engineering journals and the time period from 2000 to 2020. Our results show that many experimental papers miss information suggested by guidelines, that no de facto standard sequence for reporting exists, and that many papers do not cite any guidelines. We discuss these findings and implications for the discipline of experimental software engineering focusing on the review process and the potential to refine and extend guidelines, among others, to account for theory explicitly.

Blockchain provides an immutable distributed ledger for storing transactions. One of the challenges of blockchain is the particular processing of dynamic queries due to accumulating costs. Complex Event Processing (CEP) provides efficient and effective support for this in a way, however, that is difficult to integrate with blockchain. This paper addresses the research challenges of integrating blockchain with CEP. More specifically, we envision an effective development environment in which (i) event-driven smart contracts are modeled in a graphical way, which are, in turn, (ii) automatically transformed into complementary code that is deployed in both a CEP engine and a blockchain network, and then (iii) executed on off-chain CEP applications which, connected to different data sources and sinks, automatically invoke smart contracts when event pattern conditions are met. We follow a classic systems engineering approach for defining the concepts of our system, called CEPchain, which addresses the described requirements. CEPchain was evaluated using a real-world case study for vaccine delivery, which requires an unbroken cold chain. The results demonstrate that our approach can be applied without requiring experts on event processing and smart contract languages. Our contribution simplifies the design of integrated CEP and blockchain functionality by hiding implementation details and supporting efficient deployment.

Business Process Management Systems (BPMS) need to be able to take into account the fluctuating demand for computational resources during the execution of business process activities. Today, BPMS rely on the leasing and releasing of virtual machines (VMs) on cloud resources, which leads to a rather coarse-grained allocation of computational resources. This may result in an increase in the execution cost, flexibility restrictions, and a negative impact on the Quality of Service. In order to overcome these drawbacks, we introduce the Vienna Platform for Elastic Processes on Containers (ViePEP-C). ViePEP-C is an elastic BPMS that uses containers instead of VMs for the execution of business process activities on cloud resources, leading to a more fine-grained execution environment. To achieve this, ViePEP-C offers cloud controller, monitoring and business process execution functionalities and provides a platform for different resource and task scheduling algorithms. To evaluate the benefits of ViePEP-C, we further present a resource and task scheduling algorithm and show that, by using containers as execution environment, the execution cost can be decreased by over 20 percent (compared to a state-of-the-art VM-based scheduling algorithm) while considering a high service level.

Batch processing refers to an organization of work in which cases are synchronized such that they can be processed as a group. Prior research has studied batch processing mainly from a deductive angle, trying to identify optimal rules for composing batches. As a consequence, we lack methodological support to investigate according to which rules human resources build batches in work settings where batching rules are not strictly enforced. In this paper, we address this research gap by developing a technique to inductively mine batch activation rules from process execution data. The obtained batch activation rules can be used for various purposes, including to explicate the real-life batching behavior of human resources; to determine the compliance between the prescribed and actual batching behavior; or to investigate the influence of alternative batching behavior on service levels. The evaluation of our technique using both synthetic and real-world data demonstrates its effectiveness. With this work we complement prescriptive research on batch processing with a descriptive technique that is empirically grounded in process execution data.

The assessment of behavioral rules with respect to a given dataset is key in several research areas, including declarative process mining, association rule mining, and specification mining. The assessment is required to check how well a set of discovered rules describes the input data, as well as to determine to what extent data complies with predefined rules. In declarative process mining, in particular, some measures have been taken from association rule mining and adapted to support the assessment of temporal rules on event logs. Among them, support and confidence are used most often, yet they are reportedly unable to provide a sufficiently rich feedback to users and often cause spurious rules to be discovered from logs. In addition, these measures are designed to work on a predefined set of rules, thus lacking generality and extensibility. In this paper, we address this research gap by developing a general measurement framework for temporal rules based on Linear-time Temporal Logic with Past on Finite Traces (LTLpf). The framework is independent from the rule-specification language of choice and allows users to define new measures. We show that our framework can seamlessly adapt well-known measures of the association rule mining field to declarative process mining. Also, we test our software prototype implementing the framework on synthetic and real-world data, and investigate the properties characterizing those measures in the context of process analysis.

The world is blazing with change and digital innovation is fuelling the fire. Process management can help channel the heat into useful work. Unfortunately, research on digital innovation and process management has been conducted by separate communities operating under orthogonal assumptions. We argue that a synthesis of assumptions is required to bring these streams of research together. We offer suggestions for how these assumptions can be updated to facilitate a convergent conversation between the two research streams. We also suggest ways that methodologies from each stream could benefit the other. Together with the three exemplar empirical studies included in the special issue on business process management and digital innovation, we develop a broader foundation for reinventing research on business process management in a world ablaze with digital innovation.

Abstract Automated process discovery is a technique that extracts models of executed processes from event logs. Logs typically include information about the activities performed, their timestamps and the resources that were involved in their execution. Recent approaches to process discovery put a special emphasis on (human) resources, aiming at constructing resource-aware process models that contain the inferred resource assignment constraints. Such constraints can be complex and process discovery approaches so far have missed the opportunity to represent expressive resource assignments graphically together with process models. A subsequent verification of the extracted resource-aware process models is required in order to check the proper utilisation of resources according to the resource assignments. So far, research on discovering resource-aware process models has assumed that models can be put into operation without modification and checking. Integrating resource mining and resource-aware process model verification faces the challenge that different types of resource assignment languages are used for each task. In this paper, we present an integrated solution that comprises (i) a resource mining technique that builds upon a highly expressive graphical notation for defining resource assignments; and (ii) automated model-checking support to validate the discovered resource-aware process models. All the concepts reported in this paper have been implemented and evaluated in terms of feasibility and performance.

The behavioural comparison of systems is an important concern of software engineering research. For example, the areas of specification discovery and specification mining are concerned with measuring the consistency between a collection of execution traces and a program specification. This problem is also tackled in process mining with the help of measures that describe the quality of a process specification automatically discovered from execution logs. Though various measures have been proposed, it was recently demonstrated that they neither fulfil essential properties, such as monotonicity , nor can they handle infinite behaviour. In this article, we address this research problem by introducing a new framework for the definition of behavioural quotients. We prove that corresponding quotients guarantee desired properties that existing measures have failed to support. We demonstrate the application of the quotients for capturing precision and recall measures between a collection of recorded executions and a system specification. We use a prototypical implementation of these measures to contrast their monotonic assessment with measures that have been defined in prior research.

Business process descriptions are useful documents that are becoming increasingly important for identifying and documenting business processes. They are particularly beneficial during discovery when information about the process is gathered in interviews or by observation. Such business process descriptions are written as natural language text, which makes them intrinsically ambiguous. For this reason, it is the major challenge to formulate them in a precise and correct way right from the start. Therefore, this paper presents a service oriented architecture that analyzes a process description written in natural language to generate a sound process description. Being sound means that a description is structured, unambiguous, reveals possible quality and soundness problems related to BPMN 2.0, and contains clear identifiers for all known process elements in the original text. More specifically, we develop specific analysis and transformation techniques that are integrated by our proposed architecture. For validation purposes, we have implemented a prototype of this architecture. Our evaluation demonstrates that our techniques to generate sound process descriptions cover an average of 95% of the information extracted from its original process description while maintaining quality properties. Finally, our architecture can be enhanced with additional services that contribute to the creation and management of processes descriptions in organizations.

Documenting business processes using process models is common practice in many organizations. However, not all process information is best captured in process models. Hence, many organizations complement these models with textual descriptions that specify additional details. The problem with this supplementary use of textual descriptions is that existing techniques for automatically searching process repositories are limited to process models. They are not capable of taking the information from textual descriptions into account and, therefore, provide incomplete search results. In this paper, we address this problem and propose a technique that is capable of searching textual as well as model-based process descriptions. It automatically extracts activity-related and behavioral information from both descriptions types and stores it in a unified data format. An evaluation with a large Austrian bank demonstrates that the additional consideration of textual descriptions allows us to identify more relevant processes from a repository.

The Business Process Management (BPM) field focuses in the coordination of labor so that organizational processes are smoothly executed in a way that products and services are properly delivered. At the same time, NLP has reached a maturity level that enables its widespread application in many contexts, thanks to publicly available frameworks. In this position paper, we show how NLP has potential in raising the benefits of BPM practices at different levels. Instead of being exhaustive, we show selected key challenges were a successful application of NLP techniques would facilitate the automation of particular tasks that nowadays require a significant effort to accomplish. Finally, we report on applications that consider both the process perspective and its enhancement through NLP.

Cloud manufacturing supports companies to create cross-organizational elastic process landscapes with flexible and scalable processes. In recent years, the Business Process Model and Notation language gained interest in the cloud manufacturing domain not only as a modeling language but also as a base for process enactment. During this enactment of manufacturing processes, accurate knowledge about when a machine is needed is of great importance. While such planning is simple in a static production environment, it becomes a challenging task in an elastic cloud manufacturing environment with a variety of flexible and scalable processes. Hence, we propose an approach that performs enactment simulations before an actual manufacturing process is carried out. The goal is to reserve manufacturing machines at the time at which they will be needed in a machine reservation timetable. By discussing different use case scenarios, we further elaborate on the benefits of our approach.

Nowadays, business processes are increasingly supported by IT services that produce massive amounts of event data during the execution of a process. These event data can be used to analyze the process using process mining techniques to discover the real process, measure conformance to a given process model, or to enhance existing models with performance information. Mapping the produced events to activities of a given process model is essential for conformance checking, annotation and understanding of process mining results. In order to accomplish this mapping with low manual effort, we developed a semi-automatic approach that maps events to activities using insights from behavioral analysis and label analysis. The approach extracts Declare constraints from both the log and the model to build matching constraints to efficiently reduce the number of possible mappings. These mappings are further reduced using techniques from natural language processing, which allow for a matching based on labels and external knowledge sources. The evaluation with synthetic and real-life data demonstrates the effectiveness of the approach and its robustness toward non-conforming execution logs.

Business process modeling has received a lot of attention from practitioners and researchers alike. Organizations make significant investments into process modeling in terms of training, tools, and resources. Yet, having invested into creating large process model collections, process models often fall into disuse, provoking the impression that the initial investment has been lost. While previous work has aimed at exploring model re-use as a design principle, our work examines repeated use of a model as a behavior and thus aims at identifying factors that facilitate or hinder the repeated use of process models by individual users . We develop a conceptual model of factors that can influence an individual's intention to repeatedly use process models. We evaluate this model through a cross-sectional survey of process model users from a large European financial institution. Our results indicate the importance of quality and ease of understanding of process models to repeated use, alongside individual factors, such as motivation and individual expertise. We identify means that help organizations to promote the repeated use of process models, which can assist them to increase the benefits of process modeling.

Enterprise Architecture Management (EAM) is discussed in academia and industry as a vehicle to guide IT implementations, alignment, compliance assessment, or technology management. Still, a lack of knowledge prevails about how EAM can be successfully used, and how positive impact can be realized from EAM. To determine these factors, we identify EAM success factors and measures through literature reviews and exploratory interviews and propose a theoretical model that explains key factors and measures of EAM success. We test our model with data collected from a cross-sectional survey of 133 EAM practitioners. The results confirm the existence of an impact of four distinct EAM success factors, ‘EAM product quality’, ‘EAM infrastructure quality’, ‘EAM service delivery quality’, and ‘EAM organizational anchoring’, and two important EAM success measures, ‘intentions to use EAM’ and ‘Organizational and Project Benefits’ in a confirmatory analysis of the model. We found the construct ‘EAM organizational anchoring’ to be a core focal concept that mediated the effect of success factors such as ‘EAM infrastructure quality’ and ‘EAM service quality’ on the success measures. We also found that ‘EAM satisfaction’ was irrelevant to determining or measuring success. We discuss implications for theory and EAM practice.

Cloud computing is becoming increasingly important for executing business processes. This development contributes to a novel class of Business Process Management Systems, called eBPMS, that inherit elasticity from cloud computing. The aim of eBPMS is to improve the efficiency of process enactment, in particular regarding scalability and cost-efficiency. However, there is hardly any research that investigates scheduling for eBPMS so far. Against this background, we design an elastic scheduling approach for eBPMS and a corresponding formal problem definition in order to evaluate its data transfer capabilities -- this is especially important for hybrid cloud environments. Through extensive evaluations, we are able to show that our approach reduces the total cost by a considerable share.

Plenty of activities in many business contexts must be performed collaboratively, e.g., in a hospital or when organising a conference. Tasks such as team composition and allocation are usually performed manually and on the ground of limited criteria such as individual skills, a.o. because adequate automatic support is missing. This paper addresses this shortcoming. We present an approach for team selection and compliance checking in business processes, which includes (i) a language for describing teams; (ii) a way to define team selection conditions and policies related to team composition; and (iii) a mechanism for the automatic resolution of the team selection conditions and for team-related compliance checking based on formal ontologies.

Business process models are an important means to design, analyze, implement, and control business processes. As with every type of conceptual model, a business process model has to meet certain syntactic, semantic, and pragmatic quality requirements to be of value. For many years, such quality aspects were investigated by centering on the properties of the model artifact itself. Only recently, the process of model creation is considered as a factor that influences the resulting model’s quality. Our work contributes to this stream of research and presents an explorative analysis of the process of process modeling (PPM). We report on two large-scale modeling sessions involving 115 students. In these sessions, the act of model creation, i.e., the PPM, was automatically recorded. We conducted a cluster analysis on this data and identified three distinct styles of modeling. Further, we investigated how both task- and modeler-specific factors influence particular aspects of those modeling styles. Based thereupon, we propose a model that captures our insights. It lays the foundations for future research that may unveil how high-quality process models can be established through better modeling support and modeling instruction.

System-related engineering tasks are often conducted using process models. In this context, it is essential that these models do not contain structural or terminological inconsistencies. To this end, several automatic analysis techniques have been proposed to support quality assurance. While formal properties of control flow can be checked in an automated fashion, there is a lack of techniques addressing textual quality. More specifically, there is currently no technique available for handling the issue of lexical ambiguity caused by homonyms and synonyms. In this paper, we address this research gap and propose a technique that detects and resolves lexical ambiguities in process models. We evaluate the technique using three process model collections from practice varying in size, domain, and degree of standardization. The evaluation demonstrates that the technique significantly reduces the level of lexical ambiguity and that meaningful candidates are proposed for resolving ambiguity.

A growing number of enterprises use complex event processing for monitoring and controlling their operations, while business process models are used to document working procedures. In this work, we propose a comprehensive method for complex event processing optimization using business process models. Our proposed method is based on the extraction of behaviorial constraints that are used, in turn, to rewrite patterns for event detection, and select and transform execution plans. We offer a set of rewriting rules that is shown to be complete with respect to the$all$,$seq$, and$any$patterns. The effectiveness of our method is demonstrated in an experimental evaluation with a large number of processes from an insurance company. We illustrate that the proposed optimization leads to significant savings in query processing. By integrating the optimization in state-of-the-art systems for event pattern matching, we demonstrate that these savings materialize in different technical infrastructures and can be combined with existing optimization techniques.

The design and development of process-aware information systems is often supported by specifying requirements as business process models. Although this approach is generally accepted as an effective strategy, it remains a fundamental challenge to adequately validate these models given the diverging skill set of domain experts and system analysts. As domain experts often do not feel confident in judging the correctness and completeness of process models that system analysts create, the validation often has to regress to a discourse using natural language. In order to support such a discourse appropriately, so-called verbalization techniques have been defined for different types of conceptual models. However, there is currently no sophisticated technique available that is capable of generating natural-looking text from process models. In this paper, we address this research gap and propose a technique for generating natural language texts from business process models. A comparison with manually created process descriptions demonstrates that the generated texts are superior in terms of completeness, structure, and linguistic complexity. An evaluation with users further demonstrates that the texts are very understandable and effectively allow the reader to infer the process model semantics. Hence, the generated texts represent a useful input for process model validation.

Background: Measurement is a technique that is widely-used to quantify quality of process models. Evaluation of measurement results implies comparison against limit values, called thresholds. Determining thresholds is no trivial task and it requires the application of complex techniques. There are several techniques that have been published to date, proposing different approaches for threshold extraction. Two of the most prominent techniques are ROC curves and the Bender method. Although they come from different fields, both use logistic regression analysis as a discriminator function. Aim: For this reason, the main hypothesis is that thresholds obtained by both of those techniques are equally efficient in classifying the measurement results. Method: To check the hypothesis, we obtained thresholds for a group of empirically-validated measures for business process models, by applying both techniques. Then we checked the accuracy of the results. Results: The results indicate that the hypothesis should be rejected. Conclusions: ROC curves obtained more accurate thresholds for measurement evaluation.

There are several motives for creating process models ranging from technical scenarios in workflow automation to business scenarios in which management decisions are taken. As a consequence, companies typically have different process models for the same process, which differ in terms of granularity. In this context, business process model abstraction serves as a technique that takes a process model as an input and derives a high-level model with coarse-grained activities and the corresponding control flow between them. In this way, business process model abstraction reduces the number of models capturing the same business process on different abstraction levels. In this article, we provide a solution to the problem of deriving the control flow of an abstract process model for the case that an arbitrary grouping of activities is permitted. To this end, we use behavioral profiles and prove that the soundness of the synthesized process model requires a notion of well-structuredness of the abstract model behavioral profile. Furthermore, we demonstrate that the activities can be grouped according to the data flow of the model in a meaningful way, and that this grouping does not directly coincides with a structural decomposition of the process, which is generally assumed by other abstraction approaches. This finding emphasizes the need for handling arbitrary activity groupings in business process model abstraction.

Analysis of behavioural consistency is an important aspect of software engineering. In process and service management, consistency verification of behavioural models has manifold applications. For instance, a business process model used as system spe

While Business Process Management (BPM) is an established discipline, the increased adoption of BPM technology in recent years has introduced new challenges. One challenge concerns dealing with the ever-growing complexity of business process models. Mechanisms for dealing with this complexity can be classified into two categories: 1) those that are solely concerned with the visual representation of the model and 2) those that change its inner structure. While significant attention is paid to the latter category in the BPM literature, this paper focuses on the former category. It presents a collection of patterns that generalize and conceptualize various existing mechanisms to change the visual representation of a process model. Next, it provides a detailed analysis of the degree of support for these patterns in a number of state-of-the-art languages and tools. This paper concludes with the results of a usability evaluation of the patterns conducted with BPM practitioners.

As a result of the growing adoption of Business Process Management (BPM) technology, different stakeholders need to understand and agree upon the process models that are used to configure BPM systems. However, BPM users have problems dealing with the complexity of such models. Therefore, the challenge is to improve the comprehension of process models. While a substantial amount of literature is devoted to this topic, there is no overview of the various mechanisms that exist to deal with managing complexity in (large) process models. As a result, it is hard to obtain an insight into the degree of support offered for complexity reducing mechanisms by state-of-the-art languages and tools. This paper focuses on complexity reduction mechanisms that affect the abstract syntax of a process model, i.e., the formal structure of process model elements and their interrelationships. These mechanisms are captured as patterns so that they can be described in their most general form, in a language- and tool-independent manner. The paper concludes with a comparative overview of the degree of support for these patterns offered by state-of-the-art languages and tools, and with an evaluation of the patterns from a usability perspective, as perceived by BPM practitioners.

Engineering of process-driven business applications can be supported by process modeling efforts in order to bridge the gap between business requirements and system specifications. However, diverging purposes of business process modeling initiatives have led to significant problems in aligning related models at different abstract levels and different perspectives. Checking the consistency of such corresponding models is a major challenge for process modeling theory and practice. In this paper, we take the inappropriateness of existing strict notions of behavioral equivalence as a starting point. Our contribution is a concept called behavioral profile that captures the essential behavioral constraints of a process model. We show that these profiles can be computed efficiently, i.e., in cubic time for sound free-choice Petri nets w.r.t. their number of places and transitions. We use behavioral profiles for the definition of a formal notion of consistency which is less sensitive to model projections than common criteria of behavioral equivalence and allows for quantifying deviation in a metric way. The derivation of behavioral profiles and the calculation of a degree of consistency have been implemented to demonstrate the applicability of our approach. We also report the findings from checking consistency between partially overlapping models of the SAP reference model.

Business process models are key artifacts in the development of information systems. While one of their main purposes is to facilitate communication among stakeholders, little is known about the factors that influence their comprehension by human agents. On the basis of a sound theoretical foundation, this paper presents a study into these factors. Specifically, the effects of both personal and model factors are investigated. Using a questionnaire, students from three different universities evaluated a set of realistic process models. Our findings are that both types of investigated factors affect model understanding, while personal factors seem to be the more important of the two. The results have been validated in a replication that involves professional modelers.

Abstract A configurable process model captures a family of related process models in a single artifact. Such models are intended to be configured to fit the requirements of specific organizations or projects, leading to individualized process models that are subsequently used for domain analysis or solution design. This article proposes a formal foundation for individualizing configurable process models incrementally, while preserving correctness, both with respect to syntax and behavioral semantics. Specifically, assuming the configurable process model is behaviorally sound, the individualized process models are guaranteed to be sound. The theory is first developed in the context of Petri nets and then extended to a process modeling notation widely used in practice, namely Event-driven Process Chains.

Several methods for enterprise systems analysis rely on flow-oriented representations of business operations, otherwise known as business process models. The Business Process Modeling Notation (BPMN) is a standard for capturing such models. BPMN models facilitate communication between domain experts and analysts and provide input to software development projects. Meanwhile, there is an emergence of methods for enterprise software development that rely on detailed process definitions that are executed by process engines. These process definitions refine their counterpart BPMN models by introducing data manipulation, application binding, and other implementation details. The de facto standard for defining executable processes is the Business Process Execution Language (BPEL). Accordingly, a standards-based method for developing process-oriented systems is to start with BPMN models and to translate these models into BPEL definitions for subsequent refinement. However, instrumenting this method is challenging because BPMN models and BPEL definitions are structurally very different. Existing techniques for translating BPMN to BPEL only work for limited classes of BPMN models. This article proposes a translation technique that does not impose structural restrictions on the source BPMN model. At the same time, the technique emphasizes the generation of readable (block-structured) BPEL code. An empirical evaluation conducted over a large collection of process models shows that the resulting BPEL definitions are largely block-structured. Beyond its direct relevance in the context of BPMN and BPEL, the technique presented in this article addresses issues that arise when translating from graph-oriented to block-structure flow definition languages.

In practice, the development of process-aware information systems suffers from a gap between conceptual business process models and executable workflow specifications. Because of this gap, conceptual models are hardly reused as execution templates. In this paper, we address the notorious "OR-join problem" that is partly responsible for this gap. At the conceptual level people frequently use OR-joins. However, given their non-local semantics, OR-joins cannot be mapped easily onto executable languages. In particular, we present a new approach to map a conceptual process model with OR-joins (expressed in terms of an EPC) onto an executable model without OR-joins (expressed in terms of a Petri net). Although we used an EPC process model as a running example, the approach is equally applicable to other process modeling languages that offer OR-joins as (e.g. BPMN). Moreover, the resulting Petri net can be mapped onto other execution languages such as BPEL. All of this has been implemented in the context of the ProM framework.

The correctness of business process models is of paramount importance for the application on an enterprise level. A severe problem is that several languages for business process modelling do not have formal execution semantics which is a prerequisite to check correctness criteria. In this context, soundness defines a minimum correctness criterion that a process model should fulfil. In this paper we present a novel approach to reason about soundness based on so-called causal footprints. A causal footprint represents a set of conditions on the order of activities that holds for every case of a process model. We identify three kinds of error patterns that affect the soundness of a process model, namely the deadlock pattern, the multiple termination pattern, and the trap pattern. We use event-driven process chains (EPCs) and Petri nets to demonstrate the applicability of our approach for both conceptual as for formal process modelling languages. Furthermore, it can easily be applied to other languages, such as UML activity diagrams or BPEL. Based on the trap pattern, we prove that the "vicious circle", that is heavily discussed in EPC literature, is unsound