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.

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.

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.

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.

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.

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.

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.