Conference PaperPDF Available

Automated Process (Re-)Design

Automated Process (Re-)Design
Maximilian R¨oglinger1,2[0000000347434511] , Christopher van
Dun1,2[0000000243178592] , Tobias Fehrer1[0000000287985724] , Dominik A.
Fischer1,2[0000000252186463], Linda Moder1,2[0000000323624839] , and
Wolfgang Kratsch1,2[0000000198150653]
1FIM Research Center, Universities of Augsburg and Bayreuth, Germany
2Project Group Business & Information Systems Engineering of the Fraunhofer FIT,
Bayreuth, Germany
1 Introduction
Business process management (BPM) continuously attracts academia and prac-
tice, as it is known to drive organizational performance [7]. Especially process
(re-)design entails significant economic value by introducing innovation, reducing
costs, as well as improving quality, productivity, and customer experience [22].
Thus, it is considered an essential phase in the BPM lifecycle [15].
Today, organizations must overthink their business processes at an increas-
ingly fast pace, consider continuously rising customer needs, create novel process-
based value propositions, and engage in innovation to stay successful [7,13,15].
Technological developments are rapidly gaining momentum, processes are at
drift, and ever more players enter the global market, resulting in the organiza-
tional environment becoming more volatile, uncertain, complex, and ambiguous
(VUCA) [5]. Even though this poses pressure on organizations, it also offers a
wide range of opportunities.
While automation is prevalent in other BPM lifecycle phases (e.g., in process
execution) [1], process (re-)design commonly requires manual activities such as
traditional creativity techniques [15,22], making it time-consuming and labor-
intensive. Thus, automated process (re-)design holds high yet unexploited poten-
tial for long-term corporate success since it could accelerate process (re-) design
and make it more efficient as well as less dependent on human creativity.
2 Research Problem
2.1 Problem Description
Along the BPM lifecycle, many data-driven methods have recently emerged.
Enabled by the increasing volume of data, process mining techniques have been
developed to identify and discover process models based on process logs [2].
Predictive and prescriptive process monitoring techniques nowadays allow for
Copyright © 2021 for this paper by its authors. Use permitted under
CreativeCommonsLicenseAttribution4.0International (CCBY4.0).
2 oglinger et al.
Automated Improvement
Incrementally improve an existing
process automatically
Automated Innovation
Radically create a new process
Manual Improvement
Incrementally improve an existing
process manually
Manual Innovation
Radically create a new process
Incremental Radical
process improvement process innovation
Table 1. Process (re-)design matrix
acquiring real-time insights into future behavior and results of running process
instances and provide recommendations for optimizing process control [21].
Driven by the recent “hyperautomation” trend [19] and the widespread adop-
tion of process-aware information systems, organizations increasingly aspire to
leverage automation potential in the context of process operations [7]. Whereas
process mining and monitoring primarily focus on (partially) automated process
control, robotic process automation (RPA) has become the new “technological
star” for the lightweight automation of process execution [20]. Although some
research obstacles still need to be overcome, ever more organizations adopt RPA
to reduce manual efforts when performing specific tasks in processes [20].
Despite all these automation efforts, it is remarkable that the BPM lifecycle
phase process (re-)design remains a manual task with a high level of cognitive
effort. To illustrate the level of automation in the context of process (re-)design,
we propose a 2x2 matrix along two continua (Table 1). The first continuum con-
cerns the degree of automation (manual to automated process (re-)design), the
second concerns the scope of process (re-)design (incremental process improve-
ment to radical process innovation). In the following, we describe the state-of-art
of process (re-)design within the introduced quadrants.
With a lens on incremental process improvement, various collections of pro-
cess redesign patterns and methods have been developed [8,12]. These collections
reduce the cognitive effort and guide process stakeholders in process improve-
ment. However, they neither replace manual effort nor do they leverage the
potential of tools in the redesign process. Initial approaches for semi-automated
process improvement have been developed (see [3]). Yet, these methods are at
the lower end of automation, as they generally guide improving processes in a
user-interactive way. Thus, there is still great potential to increase the level of
automation. Research is already striving to further automate process improve-
ment, enabling automatic exploration of beneficial process changes [9].
Focusing on radical process innovation, efforts also have been made to de-
velop guidance for creating new processes with new value propositions [13]. For
instance, Grisold et al. [14] created the “Five Diamond” method, which aims
to guide organizations in identifying opportunities from business and technol-
Automated Process (Re-)Design 3
ogy trends and integrating them into processes with novel value propositions.
Nonetheless, equivalent to manual improvement, the introduced method does
not support replacing manual efforts with automation. While automating pro-
cess improvement seems easier to realize, the automation of process innovation
proves to be an unsolvable problem to date. There is certainly still huge potential
in the area of automated innovation that has barely been exploited.
Overall, we conclude that process (re-)design is still predominantly a manual
task. The automation of process (re-)design, especially with a focus on process
innovation, undoubtedly remains a major hurdle to overcome.
2.2 Challenges to Overcome
Several characteristics of business processes and the complexity of the process
(re-)design task itself make the BPM lifecycle phase of process (re-)design stand
out and, therefore, prevent or at least complicate its automation. Such charac-
teristics are described here in broad strokes:
Process (re-)design requires creativity. Process (re-)design often requires
breaking with existing structures and routines within the process to create some-
thing new. Falling back on existing concepts might be a good idea for evolu-
tionary process improvements. Still, radical (re-)design relies on going beyond
what has already been there and exploring the whole solution space of (possibly
unknown) process (re-)design opportunities. In contrast to data-based improve-
ment, such explorative and innovative efforts mostly rely on “creativity”, i.e., the
use of imagination or original ideas to create something new. Creativity is often
described as an inherently human capability. Therefore, automating (re-)design
efforts requires advances in computational creativity.
Processes are multi-dimensional. (Re-)designing processes is not as straight-
forward as simply rearranging the sequence of activities within the investigated
process. Business processes are commonly conceptualized using five fundamen-
tal perspectives [23]. Besides the above-mentioned control-flow or behavioral
perspective, these perspectives relate to the functional elements of a process
(functional perspective), the assignment of tasks to human participants (organi-
zational view), the implementation of atomic activities (operational perspective),
and the information entities handled during individual tasks (informational per-
spective). All perspectives have to be considered in automated (re-)design efforts.
Processes are executed in context. Business processes are often part of an
organization’s process landscape and, therefore, situated within a complex net-
work of dependencies such as restricted resources, logical relationships [17], and
domain-specific characteristics [4]. This makes it very hard to consider process
(re-)design as a clearly delimited activity and, thus, complicates automation.
Processes are socio-technical. Processes are sets of activities in which humans
and technology co-create value [10]. Automated approaches in every phase of the
BPM lifecycle are constricted by what data is available on these activities. Pro-
cess mining can, e.g., only discover processes when their activities have left traces
in the involved information systems or have otherwise been recorded [16] and
when these traces are of high quality [4]. In return, this data represents only the
4 oglinger et al.
technical perspective on the process. Process and domain knowledge of human
agents participating in or being responsible for the process is essential in guiding
any (re-)design effort, making full automation impractical, if not unfeasible.
Processes are at drift. All organizational concepts are subject to unintentional
change, i.e., the deviation from their planned purpose over time. In a VUCA
world, processes are no exception, constantly suffering from gradual and incre-
mental changes over time called process or concept drift [6] or being radically
changed by disruptive shocks [18]. This impacts automated process (re-)design
activities twofold: First, due to such drift, processes are dynamic, constantly
changing, and event-driven artifacts that are difficult to fully capture, define, and
reinvent using high-level process models. Second, in a dynamic and changing en-
vironment, attempting to (re-)design business processes is “subject to resistance,
deals, side effects, and the properties of the IT landscape” [6, p. 193].
3 Directions Towards a Solution
Initial ideas towards a solution may involve approaches that leverage advances
in computational creativity, e.g., evolutionary computation [3] or generative ma-
chine learning [11]. Further, to accelerate process (re-)design, organizations could
automatically incorporate feedback into design suggestions to shorten reaction
cycles, e.g., via artificial intelligence-enabled process improvement tools and com-
plex predictive models that capture trends from data.
These initial ideas are beset with challenges themselves. For example, relying
on historical data could lead to new processes already being outdated at the time
of implementation. This demonstrates the need to address ancillary issues such
as real-time data deployment [7]. A fully automated workflow environment would
also be necessary to implement new process designs without delay. Additionally,
artificial intelligence often works as a black box and lacks explainability.
In conclusion, automated process (re-)design remains a relevant research gap
that should be explored further. However, even if fully automated process (re-
)design became feasible, new challenges would arise since organizations could
then easily develop new processes. The focus of competition could move from
talent to access to data or to the best forecasting models estimating the impact
of changes on the future or identifying the next relevant time for re-evaluation.
1. van der Aalst, W.M.P.: Business process management: A comprehensive survey.
ISRN Softw Eng 2013, 1–37 (2013).
2. van der Aalst, W.M.P.: Process mining: Data Science in Action. Springer, Berlin,
Heidelberg (2016).
3. Afflerbach, P., Hohendorf, M., Manderscheid, J.: Design it like darwin -
a value-based application of evolutionary algorithms for proper and unam-
biguous business process redesign. Inf Syst Front 19(5), 1101–1121 (2017).
Automated Process (Re-)Design 5
4. Andrews, R., van Dun, C.G.J., Wynn, M.T., Kratsch, W., R¨oglinger,
M.K.E., ter Hofstede, A.H.M.: Quality-informed semi-automated event
log generation for process mining. Decis Support Syst 132 (2020).
5. Bennett, N., Lemoine, G.J.: What a difference a word makes: Understand-
ing threats to performance in a vuca world. Bus Horiz 57(3), 311–317 (2014).
6. Beverungen, D.: Exploring the interplay of the design and emergence of busi-
ness processes as organizational routines. Bus Inf Syst Eng 6(4), 191–202 (2014).
7. Beverungen, D., Buijs, J.C., Becker, J., Di Ciccio, C., van der Aalst,
W.M.P., Bartelheimer, C., et al.: Seven paradoxes of business process manage-
ment in a hyper-connected world. Bus Inf Syst Eng 63(2), 145–156 (2021).
8. vom Brocke, J., Baier, M.S., Schmiedel, T., Stelzl, K., R¨oglinger, M., Wehking, C.:
Context-aware business process management: Method assessment and selection.
Bus Inf Syst Eng (2021).
9. Dumas, M.: Process mining in 2021 and beyond (2020), https://www.linkedin.
10. Dumas, M., La Rosa, M., Mendling, J., Reijers, H.A.: Fundamentals of Business
Process Management. Springer (2018).
11. Elgammal, A., Liu, B., Elhoseiny, M., Mazzone, M.: CAN: Creative adversarial
networks, generating art by learning about styles and deviating from style norms.
arXiv:1706.07068v1 [cs.AI] (2017)
12. Fellmann, M., Koschmider, A., Laue, R., Schoknecht, A., Vetter, A.: Business pro-
cess model patterns: state-of-the-art, research classification and taxonomy. Bus
Process Manag J 25(5), 972–994 (2019).
13. Grisold, T., Gross, S., R¨oglinger, M., Stelzl, K., vom Brocke, J.: Exploring explo-
rative bpm - setting the ground for future research. In: Business Process Man-
agement, pp. 23–31. Springer, Cham (2019).
26619-6 4
14. Grisold, T., Groß, S., Stelzl, K., vom Brocke, J., Mendling, J., R¨oglinger, M., Rose-
mann, M.: The five diamond method for explorative business process management.
Bus Inf Syst Eng (2021)
15. Gross, S., Stelzl, K., Grisold, T., Mendling, J., R¨oglinger, M., vom Brocke, J.:
The business process design space for exploring process redesign alternatives. Bus
Process Manag J (2021).
16. Kratsch, W., K¨onig, F., R¨oglinger, M.: Shedding light on blind spots: Developing a
reference architecture to leverage video data for process mining. arXiv:2010.11289
[cs.CV] (2020)
17. Kratsch, W., Manderscheid, J., Reißner, D., R¨oglinger, M.: Data-driven pro-
cess prioritization in process networks. Decis Support Syst 100, 27–40 (2017).
18. Mendling, J., Pentland, B.T., Recker, J.: Building a complementary agenda for
business process management and digital innovation. Eur J Inf Syst 29(3), 208–
219 (2020).
19. Panetta, K.: Gartner top strategic technology trends for
2021 (2020),
6 oglinger et al.
20. Reijers, H.A.: Business process management: The evolution of a discipline. Comput
Ind 126 (2021).
21. Teinemaa, I., Dumas, M., La Rosa, M., Maggi, F.M.: Outcome-oriented predictive
process monitoring: Review and benchmark. ACM Trans Knowl Discov Data 13(2),
1–57 (2019).
22. Vanwersch, R.J., Vanderfeesten, I., Rietzschel, E., Reijers, H.A.: Improving busi-
ness processes. In: Business Process Management. pp. 3–18. Springer, Cham (2016). 1
23. Zeising, M., Sch¨onig, S., Jablonski, S.: Towards a common plat-
form for the support of routine and agile business processes. In: 10th
IEEE International Conference on Collaborative Computing (2014).
... However, traditional approaches to process improvement are often subjective and biased as they rely heavily on human intuition and creativity. They also often involve significant cognitive effort and can be too rigid (Gross et al. 2020;Limam Mansar et al. 2009;Röglinger et al. 2021). Rarely is the full solution space of process improvement explored. ...
... Therefore, the issue of data-driven business process improvement remains a key gap in research Röglinger et al. 2021; van der Aalst 2013; Zuhaira and Ahmad 2020). ...
... Traditional process improvement methods are often associated with mostly manual, creative work, and high investments in terms of cost and time Gross et al. 2020;Huang et al. 2015;Limam Mansar et al. 2009). Thus, Röglinger et al. (2021) distinguish between manual and automated, data-driven process improvement methods and position the latter as a key challenge for future BPM research. In the meantime, some early concepts for data-driven process improvement have been developed: For example, Afflerbach et al. (2017) deploy evolutionary algorithms based on structured data on process activities and properties to generate improved processes. ...
Business processes are at the core of every organisation’s effort to deliver services and products to customers and, thus, achieve the organisation’s goals. The discipline that deals with the design, analysis, execution, and improvement of such business processes is called business process management (BPM). Over the years, the BPM research discipline has created a large number of methods and tools to support practitioners in managing and improving their business processes. In recent years, the increasing abundance of process data available in organisational information systems and simultaneous progress in computational performance have paved the way for a new class of so-called data-driven BPM methods and tools, the most prominent of them being process mining. This cumulative doctoral thesis concentrates on two challenges related to data-driven BPM methods and tools that impede faster and more widespread adoption. First, while data-driven methods and tools have found quick adoption in BPM lifecycle phases such as process discovery and process monitoring, the lifecycle phase of process improvement has so far been neglected. However, process improvement is considered to be the most value-adding BPM lifecycle phase since it is the necessary step to address existing issues in as-is processes or to adapt these processes to constantly changing environments and customer needs and expectations. Process improvement is often expensive, time-consuming, and labour-intensive, which is why there is a particular need to support process stakeholders in redesigning their processes. Second, there is a need for high-quality process data in all phases of the BPM lifecycle. In practice, process data, e.g., in the form of event logs for process mining, is often far from the desired quality and process analysts spend the majority of their time on identifying, assessing, and remedying data quality issues. Thus, in the BPM community, the interest in exploring the roots of data quality problems and the related assurance of high-quality process data is rising. Hence, it is essential to have a means for detecting and quantifying process data quality. Against this backdrop, this cumulative doctoral thesis comprises five research articles that present advances in process data quality management on the one hand and data-driven process improvement on the other hand. Taking on a design-oriented research paradigm and applying different qualitative and quantitative research methods, this thesis proposes several IT-enabled artifacts that support stakeholders in managing process data quality and improving business processes. The insights contained in this thesis are relevant for academia and practice as they provide both scientific perspectives and practical guidance. Concerning process data quality management, research article #1 presents an approach for (semi-) automated and quality-informed event log extraction from process-agnostic relational databases. It applies metrics for data quality dimensions that are relevant to process mining in order to quantify the data quality of the source data in selected database tables and simultaneously allows users to extract event logs in XES format from the database tables. Research article #2 presents an approach for detecting and quantifying timestamp data quality issues in events logs already present in XES format. The approach applies metrics for identifying timestamp imperfection patterns and allows users to interactively filter, repair, and annotate the event log. Furthermore, this thesis provides several concrete approaches to data-driven business process improvement. First, it focuses on process improvement in itself and aims to create artifacts for supporting process improvement initiatives. Therefore, research article #3 provides a model based on generative adversarial networks to create new process designs. Specifically, it uses event logs and annotated information on process variants and process deviance to generate a new process model which provides suggestions for process improvement to the user. Second, this thesis targets data-driven decision support in business processes. In particular, research article #4 uses multi-criteria decision analysis to extend traditional vehicle routing problems in last-mile delivery with a customer-centric perspective. The customer-centric vehicle routing uses process and customer data and the concept of customer lifetime values to predict customer satisfaction and, thus, optimise delivery routes. Finally, research article #5 presents a modelling approach for IT availability risks in smart factory networks based on Petri nets. The modelling approach uses modular components of information systems and production machines to model, simulate, and analyse production processes. The thesis concludes by pointing to limitations of the presented research articles as well as directions for future research. Overall, this thesis contributes to several important research streams in BPM while applying a broad range of qualitative and quantitative research methods such as simulation, normative analytical modelling, multi-criteria decision analysis, and interview studies within an overarching design science research paradigm. It builds upon and extends existing research on process data quality management and business process improvement.
... While this research gap has been recognized in the liter-ature, no interactive and assistive approach combines both worlds in a guided process [13,11]: tool-based automation and guidance of BPR tasks on the one hand and the incorporation of domain expertise on the other hand. Thus, we formulate our research question as follows: How can assistive tools improve BPR? ...
For many organizations, the continuous optimization of their business processes has become a critical success factor. Several related methods exist that enable the step-by-step redesign of business processes. However, these methods are mainly performed manually and require both creativity and business process expertise, which is often hard to combine in practice. To enhance the quality and effectiveness of business process redesign, this paper presents a conceptualization of assisted business processre design (aBPR). The aBPR concept guides users in improving business processes based on redesign patterns. Depending on the data at hand, the aBPR concept classifies four types of recommendations that differ in their level of automation. Further, this paper proposes a reference architecture that provides operational support for implementing aBPR tools. The ra has been instantiated as a prototype and evaluated regarding its applicability and usefulness in artificial and naturalistic settings by performing an extensive real-world case study at KUKA and interviewing experts from research and practice.
Full-text available
Explorative business process management (BPM) is attracting increasing interest in the literature and professional practice. Organizations have recognized that a focus on operational efficiency is no longer sufficient when disruptive forces can make the value proposition of entire processes obsolete. So far, however, research on how to create entirely new processes has remained largely conceptual, leaving it open how explorative BPM can be put into practice. Following the design science research paradigm and situational method engineering, we address this research gap by proposing a method called the Five Diamond Method. This method guides explorative BPM activities by supporting organizations in identifying opportunities from business and technology trends and integrating them into business processes with novel value propositions. The method is evaluated against literature-backed design objectives and competing artefacts, qualitative data gathered from BPM practitioners, as well as a pilot study and two real-world applications. This research provides two contributions. First, the Five Diamond Method broadens the scope of BPM by integrating prescriptive knowledge from innovation management. Second, the method supports capturing emerging opportunities arising from changing customer needs and digital technologies.
Full-text available
Context awareness is essential for successful business process management (BPM). So far, research has covered relevant BPM context factors and context-aware process design, but little is known about how to assess and select BPM methods in a context-aware manner. As BPM methods are involved in all stages of the BPM lifecycle, it is key to apply appropriate methods to efficiently use organizational resources. Following the design science paradigm, the study at hand addresses this gap by developing and evaluating the Context-Aware BPM Method Assessment and Selection (CAMAS) Method. This method assists method engineers in assessing in which contexts their BPM methods can be applied and method users in selecting appropriate BPM methods for given contexts. The findings of this study call for more context awareness in BPM method design and for a stronger focus on explorative BPM. They also provide insights into the status quo of existing BPM methods.
Full-text available
Business Process Management (BPM) embodies a management philosophy, which is supported by a range of methods, techniques, and tools. Academics are continuously expanding this repertoire. In this overview article, the themes are sketched that characterize the development of the BPM discipline over the years: BPM Systems, process modeling, process design, coordination and interoperability, model management, process mining, and new technologies. Each of the themes is characterized in this overview on the basis of articles that appeared in Computers in Industry since its conception, now 40 years ago. Together, these themes provide a perspective on a thriving and evolving discipline.
Full-text available
Purpose – Process redesign refers to the intentional change of business processes. While process redesign methods provide structure to redesign projects, they provide limited support during the actual creation of to-be processes. More specifically, existing approaches hardly develop an ontological perspective on what can be changed from a process design point of view and they provide limited procedural guidance on how to derive possible process design alternatives. This paper aims to provide structured guidance during the to-be process creation. Design/methodology/approach – Using design space exploration as a theoretical lens, we develop a conceptual model of the design space for business processes, which facilitates the systematic exploration of design alternatives along different dimensions. We utilized an established method for taxonomy development for constructing our conceptual model. First, we derived design dimensions for business processes and underlying characteristics through a literature review. Second, we conducted semi-structured interviews with professional process experts. Third, we evaluated our artifact through three real-world applications. Findings – We identified 19 business process design dimensions that are grouped into different layers and specified by underlying characteristics. Guiding questions and illustrative real-world examples help to deploy these design dimensions in practice. Taken together, the design dimensions form the “Business Process Design Space” (BPD-Space). Research limitations/implications – Practitioners can use the BPD-Space to explore, question, and rethink business processes in various respects. Originality/value – The BPD-Space complements existing approaches by explicating process design dimensions. It abstracts from specific process flows and representations of processes and supports an unconstrained exploration of various alternative process designs.
Full-text available
Business Process Management is a boundary-spanning discipline that aligns operational capabilities and technology to design and manage business processes. The Digital Transformation has enabled human actors, information systems, and smart products to interact with each other via multiple digital channels. The emergence of this hyper-connected world greatly leverages the prospects of business processes – but also boosts their complexity to a new level. We need to discuss how the BPM discipline can find new ways for identifying, analyzing, designing, implementing, executing, and monitoring business processes. In this research note, selected transformative trends are explored and their impact on current theories and IT artifacts in the BPM discipline is discussed to stimulate transformative thinking and prospective research in this field.
Full-text available
The world is blazing with change and digital innovation is fueling 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.
Full-text available
Recent claims in the literature highlight that BPM should become more explorative and opportunity-driven. The underlying argument is that BPM has been mainly concerned with exploitation activities – i.e., analysis and improvement of existing business processes – but it has neglected the role of innovation. In this conceptual article, we aim to establish a systematic understanding of what explorative BPM is and how it can be brought about. We pursue three goals. First, we derive an overarching definition of explorative BPM. Second, we propose the “triple diamond model” as a means to integrate explorative BPM activities in business process work. Third, we point to future research opportunities in the context of explorative BPM.
Process mining is one of the most active research streams in business process management. In recent years, numerous methods have been proposed for analyzing structured process data. In many cases, however, only the digitized parts of processes are directly captured by process-aware information systems, whereas manual activities often leave blind spots in the process analysis. While video data can contain valuable process-related information that is not captured in information systems, a standardized approach to extracting event logs from unstructured video data remains lacking. To solve this problem and facilitate the systematic usage of video data in process mining, we have designed the ViProMiRA, a reference architecture that bridges the gap between computer vision and process mining. The various evaluation activities in our design science research process ensure that the proposed ViProMiRA allows flexible, use case-driven, and context-specific instantiations. Our results also show that a prototypical implementation of the ViProMiRA is capable of automatically extracting more than 70% of the process-relevant events from a real-world video dataset in a supervised learning scenario.
Process mining, as with any form of data analysis, relies heavily on the quality of input data to generate accurate and reliable results. A fit-for-purpose event log nearly always requires time-consuming, manual pre-processing to extract events from source data, with data quality dependent on the analyst's domain knowledge and skills. Despite much being written about data quality in general, a generalisable framework for analysing event data quality issues when extracting logs for process mining remains unrealised. Following the DSR paradigm, we present RDB2Log, a quality-aware, semi-automated approach for extracting event logs from relational data. We validated RDB2Log's design against design objectives extracted from literature and competing artifacts, evaluated its design and performance with process mining experts, implemented a prototype with a defined set of quality metrics, and applied it in laboratory settings and in a real-world case study. The evaluation shows that RDB2Log is understandable, of relevance in current research, and supports process mining in practice.
Purpose Patterns have proven to be useful for documenting general reusable solutions to a commonly occurring problem. In recent years, several different business process management (BPM)-related patterns have been published. Despite the large number of publications on this subject, there is no work that provides a comprehensive overview and categorization of the published business process model patterns. The purpose of this paper is to close this gap by providing a taxonomy of patterns as well as a classification of 89 research works. Design/methodology/approach The authors analyzed 280 research articles following a structured iterative procedure inspired by the method for taxonomy development from Nickerson et al. (2013). Using deductive and inductive reasoning processes embedded in concurrent as well as joint research activities, the authors created a taxonomy of patterns as well as a classification of 89 research works. Findings In general, the findings extend the current understanding of BPM patterns. The authors identify pattern categories that are highly populated with research works as well as categories that have received far less attention such as risk and security, the ecological perspective and process architecture. Further, the analysis shows that there is not yet an overarching pattern language for business process model patterns. The insights can be used as starting point for developing such a pattern language. Originality/value Up to now, no comprehensive pattern taxonomy and research classification exists. The taxonomy and classification are useful for searching pattern works which is also supported by an accompanying website complementing the work. In regard to future research and publications on patterns, the authors derive recommendations regarding the content and structure of pattern publications.