On the Suitability of Process Mining for Enhancing Transparency of Blockchain Applications

Klinkmüller, Christopher; Bandara, H. M. N. Dilum; van der Aalst, Wil; Hobeck, Richard; Weber, Ingo Article — Published Version On the Suitability of Process Mining for Enhancing Transparency of Blockchain Applications Business & Information Systems Engineering Suggested Citation: Klinkmüller, Christopher; Bandara, H. M. N. Dilum; van der Aalst, Wil; Hobeck, Richard; Weber, Ingo (2024) : On the Suitability of Process Mining for Enhancing Transparency of Blockchain Applications, Business & Information Systems Engineering, ISSN 1867-0202, Springer Fachmedien Wiesbaden, Wiesbaden, Vol. 67, Iss. 6, pp. 777-796, https://doi.org/10.1007/s12599-024-00903-5 This Version is available at: https://hdl.handle.net/10419/333368 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ RESEARCH PAPER On the Suitability of Process Mining for Enhancing Transparency of Blockchain Applications Richard Hobeck •Christopher Klinkmu ¨ller •H. M. N. Dilum Bandara • Ingo Weber •Wil van der Aalst Received: 18 December 2022 / Accepted: 28 June 2024 / Published online: 25 October 2024 ÓThe Author(s) 2024 Abstract Blockchain technology is known for its transparency properties due to its publicly available, immutable data. Yet, as data availability does not inherently ensure transparency, further analytical methods may be required for human interpretation of data traces. Process mining has emerged as a popular toolbox for understanding processes and how they are executed in practice. The paper studies process mining as a method to enhance the transparency of blockchain data. To this end, two popular Ethereum applications were analyzed using process mining: the prediction and betting marketplace Augur and the network marketing platform Forsage. Observations from the process-mining analyses are used to discuss if process mining can serve as a method to establish transparency of a blockchain. For both applications, new insights are generated for usage scenarios such as application redesign, security analysis, user behavior analysis, and revealing blind spots in Augur’s and Forsage’s documentation. The paper concludes that there is evidence that process mining can serve as a method to enhance transparency in blockchains at the cost of technical setup and knowledge acquisition. Keywords Blockchain Process mining Transparency  Process discovery Conformance checking Process enhancement Ethereum 1 Introduction A blockchain can be characterized as a distributed, appendonly data store for transactions (Xu et al. 2019). Secondgeneration blockchains allow for deploying and executing user-defined programs called smart contracts. On this basis, blockchain has emerged as a technology that enables the automation of cross-organizational processes on a neutral platform (Mendling et al. 2018; Weber et al. 2016), and more generally the design, development, and operation of decentralized applications (DApps) (Xu et al. 2019). In theory, public blockchains offer transparency properties (Xu et al. 2019, p.19f). That is, public blockchains constitute a distributed computing environment in which users can host, execute, and store applications and data. A core argument for transparency properties of public blockchains is the accessibility of this environment, including access to the deployed programs, stored data, and, if made available, the source code of applications. Following Leonardi and Treem (2020), we argue that information availability alone is insufficient to establish transparency. Additional analytical steps are required to present the data in easily accessible formats and thereby achieve transparency. Hence, examining the behavior of blockchain applications Accepted after 3 revisions by Daniel Beverungen. R. Hobeck (&) Chair of Service-centric Networking, Technische Universitaet Berlin, Berlin, Germany e-mail: [email protected] C. Klinkmu ¨ller BPMotion, Sydney, Australia H. M. N. D. Bandara Data61, CSIRO, Sydney, Australia I. Weber (&) School of CIT & Fraunhofer-Gesellschaft, Technical University of Munich, Munich, Germany e-mail: [email protected] W. van der Aalst RWTH Aachen University, Aachen, Germany 123 Bus Inf Syst Eng 67(6):777–796 (2025) https://doi.org/10.1007/s12599-024-00903-5 and users requires effort to turn the information from the blockchain into clear insights. Process mining (van der Aalst 2016) provides a set of tools to extract knowledge from data, e.g., through the discovery of process models from data without prior information about the process (IEEE Task Force on Process Mining 2011). Process mining has become popular as a toolbox for understanding processes and how they are executed in practice. For example, many case studies ranging from healthcare (Andrews et al. 2018; Mans et al. 2009; Rovani et al. 2015; Suriadi et al. 2014), finance (De Weerdt et al. 2013; Jans et al. 2011), manufacturing (Rozinat et al. 2009), and public services (van der Aalst et al. 2007; Leemans et al. 2019) to software development (Lemos et al. 2011) have applied process mining to analyze processes from different perspectives such as control flow, conformance, drifts, and performance (Reinkemeyer 2020). Nevertheless, process mining on blockchain data has turned out to be a challenging task (Di Ciccio et al. 2018). Hence, recently researchers have created techniques to extract authoritative data from blockchains (Klinkmu ¨ller et al. 2019,2020). In this article, we complement those propositions by studying the utility of process mining on blockchain data in the context of real-world use cases. To this end, we view process mining from a methodological perspective (van Eck et al. 2015; Klinkmu ¨ller et al. 2019). While process mining provides valuable tools for data-driven analysis of all types of processes, we do not only want to understand the utility of these tools but also the feasibility of applying them. Here, we focus on DApp transparency and specifically on two prominent research goals from the blockchain domain (see Sect. 2.3): G1: Code Validation & Verification: Determine to what extent process mining can contribute to making DApps more transparent by supporting the validation and verification of their source code. G2: User Behavior Analysis: Determine to what extent process mining can contribute to making DApps more transparent by supporting the analysis of their users’ behavior. To this end, we conduct two in-depth DApp analyses with process mining on popular Ethereum applications: Augur 1 and Forsage. 2 Augur is a prediction and betting marketplace, where users (1) raise questions about future events, (2) predict and bet on answers, and (3) settle the bets after the event occurred and the answer is known. Forsage is a network marketing (aka multi-level marketing) platform that was very popular until being declared a Ponzi scheme by market regulators in several countries 3, . 4 Note that we presented the Augur analysis in a previous paper (Hobeck et al. 2021) in which we outlined our general experience and findings from using process mining to explore this application in an open-ended analysis. By contrast, in this paper, we examine the utility of process mining for specific application scenarios and synthesize our initial insights with those of the second DApp analysis. In this way, we provide evidence that process mining can enhance the transparency of blockchains and, as a result, generate valuable insights for code validation & verification and user behavior analysis. The remainder of the paper is structured as follows. First, we motivate our research goals by (1) summarizing blockchain research challenges, (2) outlining why process mining can help address some of them in principle, and (3) reviewing related work. In Sect. 3, we present our methodology, including justifications for choosing Augur and Forsage. Section 4outlines the data extraction and preprocessing procedures underlying both DApp analyses. The paper’s focal point are the DApp analyses for Augur (Sect. 5) and Forsage (Sect. 6). In each section, we describe the respective application and data analyses, covering insights from data exploration, process discovery, conformance checking, and performance analysis. In Sect. 7, we then discuss and synthesize the results to assess the contribution of process mining in enhancing the transparency in a blockchain environment in alignment with our research goals. Finally, we conclude in Sect. 8. 2 Motivation This section motivates our work. First, we introduce the paper’s understanding of transparency in Sect. 2.1 and summarize the process-mining discipline in Sect. 2.2.We then introduce basic blockchain concepts and discuss open research challenges in Sect. 2.3, highlighting code validation & verification and user behavior analysis as challenges that could benefit from process mining. Lastly, we review work related to process mining in the context of blockchain in Sect. 2.4. 2.1 Software Transparency According to Leite and Cappelli (2010), transparency in software concerns the disclosure of information, i.e., execution data and software functions that transform input to 1 https://augur.net/, accessed 12 June 2022. 2 https://forsage.io/, accessed 12 June 2022. 3 https://www.sec.gov.ph/cdo-2020/forsage-and-forsage-philippines/, accessed 28 June 2022. 4 https://csimt.gov/2021/04/07/us-state-issues-cease-and-desistorder-against-dapp-forsage/, accessed 28 Aug 2023. 123 778 R. Hobeck et al.: On the Suitability of Process Mining for Enhancing. . ., Bus Inf Syst Eng 67(6):777–796 (2025) output data. They note that although disclosed, software functions can remain obfuscated if the software code is presented in a way that is hard to read. Leonardi and Treem (2020) have a similar standpoint with respect to transparency and data availability. They argue that efforts to achieve greater transparency by disclosing more data can obfuscate the visibility of information in that data. They call that effect the transparency paradox and argue that it takes additional analytical steps to retrieve information from the data. In this paper, we use the transparency notion described by Leite and Cappelli (2010) and follow the argument of Leonardi and Treem (2020) that information availability requires appropriate analytical steps on the disclosed data. 2.2 Process Mining Process mining (van der Aalst 2016) is increasingly used to monitor and improve operational processes. It offers a rich tool set for analyzing event data, i.e., data that contain information about events occurring during the execution of processes (van der Aalst 2016). Each event must at least have three attributes: (1) a case identifier of the case that the event belongs to, (2) an activity name that represents the activity whose execution led to the event, and (3) a timestamp at which the event occurred. Additional attributes may refer to locations, resources, costs, transaction data, and on the Ethereum blockchain, the consumed gas (i.e., a measure for the computational effort of an operation). A trace is the sequence of all events belonging to the same case and sorted by timestamps. Focusing on the activity names only, traces can be transformed into variants, i.e., unique sequences of activities. Lastly, an event log is a collection of events stored in a format like XES (Extensible Event Stream) (Acampora et al. 2017). Figure 1illustrates an overview of process-mining categories (van der Aalst 2016). Note that the categories are shown as rectangles. First, process discovery algorithms infer process models, thus visualizing the control flow of the process that generated the data. Second, conformance checking analyzes the degree to which the behavior of individual traces and/or entire event logs adheres to a normative process model that describes the expected control flow. Third, performance analysis can be used to obtain insights into the key performance indicators like cycle time or staff productivity, and to detect drivers for those indicators. Fourth, comparative process mining and drift detection enable the investigation of process behavior under different conditions, allowing analysts to, e.g., inspect behavioral differences between user groups or changes over time. Fifth, exploration enables analysts to understand data characteristics using visual analytics and to detect outliers. Additionally, process-mining research addresses methodological aspects, see e.g., van Eck et al. (2015) and Klinkmu ¨ller et al. (2019). Such research aims to understand and provide guidelines for conducting process-mining projects, including aspects like planning, data extraction, data preparation, and insight validation. Sect. 3 presents the process-mining methodology applied in the context of this work. In essence, process mining provides means to analyze different process perspectives, including control flow, conformance, drifts, and performance (Reinkemeyer 2020). Therefore, we argue that those analyses can reveal meaningful insights from blockchain transaction execution data. That is, we expect insights into the blockchain application behavior to assist software developers with code validation & verification in general, and specifically with ensuring that applications correctly implement process requirements. Considering that blockchain events are the results of transactions initiated by users, we also expect that insights into the application behavior reveal insights into user behaviors. Note that blockchains record the user address that triggered a specific transaction, enabling us to enrich extracted event logs with this information. Next, we outline our rationale for investigating process mining for blockchain analysis, and in particular for code validation & verification and user behavior analysis. 2.3 Blockchain Research Challenges Ablockchain is an append-only store of transactions, distributed across a peer-to-peer network and structured as a linked list of blocks (Xu et al. 2019). Second-generation blockchains also provide a neutral execution infrastructure for running user-defined programs, called smart contracts. Applications that operate autonomously through smart contracts and run on top of a blockchain are called decentralized applications (DApps). Blockchain provides immutability, transparency, and data integrity to DApps. Similar to traditional enterprise information systems, many DApps support operational processes. For example, there are decentralized autonomous organizations (DAOs) for which blockchain applications define a set of transparent processes and rules that allow the organizations’ members to control the organizations without requiring centralized leadership (Prusty 2017). Similarly, cross-organizational processes, such as supply chains, can be facilitated by blockchain technology that enforces business rules and exchanges business information (Mendling et al. 2018; Weber et al. 2016). There are various reviews and research agendas that map out challenges and directions for blockchain research. Table 1categorizes challenges identified in those publications. Overall, there are six categories. First, application 123 R. Hobeck et al.: On the Suitability of Process Mining for Enhancing. . ., Bus Inf Syst Eng 67(6):777–796 (2025) 779 design concerns the development of DApps, given the limitations of a blockchain environment. While this category subsumes aspects like programming language features, application optimization, and immutability of smart contracts, a key area of concern is code validation & verification. Second, data privacy covers ethical and legal data privacy concerns, as well as data confidentiality challenges. Third, the socio-technical category focuses on interactions between users and blockchain technology. In this regard, a central challenge is to understand user behavior and how it is influenced by technological features. Fourth, scalability revolves around issues concerning latency, and limited transaction and storage volumes of blockchains. Fifth, blockchain interoperability includes integration with existing systems and off-chain components. Lastly, the development of consensus mechanisms focuses on protocols for blockchain nodes to agree on the blockchain state in a cryptographically secure and environmentally sustainable way. In this work, we study process mining as a means to enhance blockchain transparency. Considering the analytical nature of process mining, we believe that process mining can help users in filtering, analyzing, and visualizing blockchain data so that it is easier to understand (Leonardi and Treem 2020, p. 1611). Hence, our interest specifically lies in exploring process mining in the context of challenges that can benefit from the analysis of blockchain data. First, this includes smart contract code validation & verification challenges from the application design category. Here, testing and evaluating DApps after deployment is most relevant to our work, as it is of interest to users who want to monitor if the DApp and any updates comply with expected behavior (Casino et al. 2019; Rossi et al. 2019; Zheng et al. 2020), e.g., to detect fraudulent schemes (Risius and Spohrer 2017; Casino et al. 2019; Zheng et al. 2020). Note that while the deployed code of individual smart contracts is immutable, DApp behavior can be altered at run-time by dynamically changing parameters and the binding of smart contracts. In this regard, efforts by external users are frequently exacerbated by the unavailability or readability of smart contract code (Zheng et al. 2020; Sharma et al. 2023). We argue that analyzing event data generated during smart contract execution using process mining can enhance transparency and assist users in smart contract code validation & verification (G1). In particular, we believe that process-discovery and conformance-checking capabilities support the detection of deviations from the expected behavior. In practical terms, users can access the public blockchain data of a DApp but Fig. 1 Process-mining categories for event data analysis Table 1 Categories of challenges from related work (– generally mention category; CV – mention challenges related to code validation & verification; UB – mention user behavior analysis) Application design Data privacy Socio-technical Scalability Interoperability Consensus mechanisms Risius and Spohrer (2017)CV UB   Zheng et al. (2018) Casino et al. (2019)CV    Rossi et al. (2019)CV UB   Zheng et al. (2020)CV  Vacca et al. (2021)CV  Sharma et al. (2023)CV   123 780 R. Hobeck et al.: On the Suitability of Process Mining for Enhancing. . ., Bus Inf Syst Eng 67(6):777–796 (2025) require methods to check if the DApp implements services as advertised or not, and here process mining could be the method of choice. Second, there is consensus that from a socio-technical perspective, blockchain research must become multi-disciplinary and consider the perspectives of all involved stakeholders. While there is a general call for theories that explain how technological blockchain features influence user behavior (Rossi et al. 2019; Risius and Spohrer 2017), we focus on a particular challenge for individual stakeholders. That is, many DApps, such as those implemented for DAOs, can be viewed as process coordination or orchestration mechanisms that allow individuals to transact. This creates the need for DApp users to decide whether they want to trust other users. However, insights into the motivations and behaviors of other users are not readily available. Similar to code validation & verification, mechanisms that can help make user behavior more transparent are thus required (Rossi et al. 2019). Here, we consider process mining to examine user behavior as a potential means for overcoming this challenge (G2). For instance, while data might capture the users that are involved in a case, their general behavior and potentially their motivation only become apparent when analyzing their actions across cases using, e.g., comparative process mining. 2.4 Related Work Researchers have explored process mining on blockchain data before. First, research has focused on extracting event logs in the XES format (Mu ¨hlberger et al. 2019; Klinkmu ¨ller et al. 2019,2020; Koschmider and Duchmann 2021) or a specialized, object-centric log format (Moctar et al. 2023; Hobeck and Weber 2023). The corresponding publications focus on outlining technical implementation details and present illustrative applications of the approaches, in part relying on real-world applications. Second, building on data extraction capabilities, researchers have suggested technical concepts that use process mining for auditing and monitoring. Di Ciccio et al. (2020) propose an approach for DApp monitoring but do not apply their approach to blockchain data. Corradini et al. (2019) introduce a methodology for DApp auditing that is based on trace clustering and process discovery. While they evaluate their methodology on a small realworld application, they solely focus on measuring the quality of the discovered models and do not provide insights into how their methodology helps to contribute to transparency. Mu ¨ller and Ruppel (2019) demonstrate an approach that relies on process mining to monitor the blockchain network, but not a single process or DApp. Finally, only two publications report insights from applying process mining on real-world DApp data. Hobeck et al. (2021) generate value-adding findings by applying process mining to data extracted from a DApp deployed on Ethereum. Lamghari (2023) applies process mining to suggest ideas for extending an Ethereum-based game. Both publications interpret the ability to derive insights as a general indicator of the suitability of process mining. Considering these publications, the contribution of this paper is twofold. First, it studies process mining as a means to enhance the transparency for DApp users in the context of code validation & verification and user behavior analysis, aspects that have not been addressed by prior work. Second, we aim to understand the utility of process mining from a methodological perspective. That is, we do not only consider the application of a technique to data, but cover all steps required in a process-mining project including e.g., familiarization with a DApp, data extraction, and iterative investigation. 3 Research Methodology In this paper, we adopt the research methodology depicted in Fig. 2. As a first step, we defined our research goals. To this end, we identified current challenges commonly discussed in the blockchain literature and argued how process mining could in principle contribute to solving them (see Sect. 2). Consequently, our goal is to provide deeper insights into the actual suitability of process mining for code validation & verification and user behavior analysis in real-world settings. As such, this paper can be classified as process-mining research on the individual level (i.e., examining specific tasks like code verification & verification and user behavior analysis) and the ecosystem level (i.e., focused on inter-organizational settings such as decentralized blockchain applications) (vom Brocke et al. 2021). To investigate the research goals, we then defined our research approach. Considering that the utility of process mining in the context of blockchain transparency has not been studied yet (see Sect. 2.4), we chose an exploratory approach suited to examine such novel phenomena (Recker 2021). We decided to analyze two DApps using process mining, in particular focusing on software validation & verification and user behavior analysis. In both analyses, we followed best practices for process-mining projects (see below and Fig. 3) and conducted all steps required to infer insights from blockchain data. These steps include DApp familiarization, data extraction, and insight evaluation. The goal is to share and discuss observations from these analyses. The observations do not only refer to the insights that we obtained. They also cover methodological aspects that 123 R. Hobeck et al.: On the Suitability of Process Mining for Enhancing. . ., Bus Inf Syst Eng 67(6):777–796 (2025) 781 must be considered when applying process mining. By relying on such observations, we go beyond a demonstration of the potential value of applying process mining. Instead, we more broadly discuss contextual factors that impact, e.g., the reliability of insights or the effort to generate them, and thus the suitability of process mining for establishing blockchain transparency. Note that we decided to rely on our own observations due to lacking initiatives and experts that applied process mining on blockchain data. The implications of this decision for the validity of our findings are discussed in Sect. 7.3. After defining the research approach, we selected the DApps, choosing Augur and Forsage as suitable, independent DApps for the following reasons. First, both DApps operate on public Ethereum, the most popular platform for decentralized applications (Wu et al. 2021; Qasse et al. 2020). Hence, time-stamped event data is publicly accessible for both DApps. Second, both DApps were among the most popular Ethereum DApps at times 5,6 , resulting in the availability of substantial volume of data to analyze. Third, Augur and Forsage were designed so that log entries are tracked and stored by a central logging contract to reflect major events during contract execution, which enables meaningful analyses and simplifies data extraction. Fourth, information on both DApps is widely available, such as in Augur’s white paper (Peterson et al. 2018) and Forsage’s marketing material. Fifth, because we wanted to study DApps that were not developed with a process-centric focus, we reviewed the publicly available source code and documentation for both DApps. We did not find any indication that they were developed in a process-driven fashion or that their execution was administered by any business process management system, e.g., as proposed by Lo ´pez-Pintado et al. (2017). Hence, confirmatory evidence regarding our research goals implies that process mining can be a useful tool for blockchain applications, independent of whether the application is based on a process-centric design or not. Lastly, transparency is important for users of both DApps, as they invest cryptocurrency to receive some kind of return on investment. Hence, insights into source code validity and user behavior bear the potential to provide enhanced transparency into the DApps’ trustworthiness. We then analyzed both DApps following van Eck et al. (2015)’s widely adopted methodology for process-mining projects (see Fig. 3). We adopted this methodology to ensure that our analyses are aligned with common practices and hence are representative of how analysts generally conduct such projects. To that end, we collected data for both DApps. Foremost, we extracted log entries from public Ethereum to generate event data. Additionally, we collected supplementary data to reconstruct process models from, e.g., Augur’s white paper (Peterson et al. 2018) and Forsage marketing material. These preparation steps are described in Sect. 4. As part of the data preparation for the process-mining analyses, we filtered the event data and created views, in part by altering the case notion or sampling the data (see also Sect. 4). In the case of Forsage, we enriched the event log with data on Ether transfers. During the mining and analyses, we applied process-discovery and conformance-checking algorithms, as well as other forms of process analysis. We also documented our observations. The analyses are described in detail in Sect. 5and Sect. 6. We evaluated the results of our process-mining projects in Fig. 2 Overview of the research approach Fig. 3 The process-mining methodology used for the DApp analyses (adapted from van Eck et al. (2015)) 5 Augur: https://dappradar.com/dapp/augur?range-ha=all, accessed 04 Aug 2023. 6 Forsage: https://dappradar.com/dapp/forsage?range-ha=all, accessed 04 Aug 2023. 123 782 R. Hobeck et al.: On the Suitability of Process Mining for Enhancing. . ., Bus Inf Syst Eng 67(6):777–796 (2025) an interview with the lead architect of Augur and using literature on Forsage (Kell et al. 2021). While we followed the same methodology for both DApps, the plans for the two analyses differed slightly. By analyzing Augur (Hobeck et al. 2021), we initially aimed to understand the general utility of process mining for blockchain applications. We hence conducted an openended analysis and among others obtained insights relevant to code validation & verification and user behavior analysis. Considering the relevance of these two topics for blockchain research (see Sect. 2.3), in this paper, we aim to refine the respective observations by conducting the Forsage analysis. The resulting iterative nature of our research approach is reflected in the loop in Fig. 2. After that, we consolidated the observations from the DApp analyses and discuss the findings with respect to G1 and G2 in Sect. 7. Finally, we report the results of our research in this article. 4 Analysis Preparation As depicted in Fig. 3, before extracting the data we had to plan the analysis. Besides selecting suitable DApps, this included becoming familiar with the inner workings of the DApps by examining the smart contract code and studying other materials, such as the documentation, Augur’s white paper (Peterson et al. 2018), blog entries, posts on social media, and videos. For Augur, this step was exacerbated by the complexity of the source code which consisted of 95 Solidity source code files. 7 While there is a central logging contract that emits relevant execution information and serves as an entry point into understanding the source code, the large number of smart contracts and heavy use of dynamic binding impeded interpretation of the DApp’s inner workings. With Forsage being a Ponzi scheme, we faced a different challenge. As also noted by Kell et al. (2021), Forsage’s implementation is opaque and not all parts of the source code are available, making it hard to follow the program’s logic in code and interpret the log entries. This was further exacerbated by the unavailability of developer documentation, forcing us to rely on secondhand documentation such as reports and blog posts. Observation 1 Obtaining a foundational understanding of the two DApps and their inner workings was a critical step for data extraction and analysis, but was exacerbated by the unavailability and complexity of source code and documentation. On a second-generation blockchain, such as Ethereum, log entries are frequently used by developers to communicate information related to the results of smart contract execution to off-chain components. We extracted the log entry data using the open-source Ethereum Logging Framework (ELF) (Klinkmu ¨ller et al. 2020). It allowed us to define declarative queries to extract, transform, and format data from Ethereum-based applications. ELF abstracts many technical details, such as establishing a connection to an Ethereum node or orchestrating API calls. However, developing ELF queries that extract high-quality data required us to refine and test the queries in multiple iterations. In each iteration, we (1) inspected sample query results, (2) compared them to other data sources, e.g., data from etherscan.io, and (3) evaluated basic data characteristics through explorative analysis. Observation 2 While software such as ELF reduced technical implementation effort for data extraction, we still needed multiple iterations to diligently develop queries and ensure high data quality For each DApp, ELF query execution resulted in one event log in the XES format, containing one event for each log entry recorded by the DApp. 8 For Augur, we extracted information related to 2,897 markets from 9 July 2018 to 10 November 2020. The former date marks the first execution of Augur v1.0, and the latter refers to the last event we extracted when running the ELF manifest on 16 November 2020. However, as outlined in Sect. 5, the launch of Augur v2.0 in July 2020 rendered Augur v1.0 ‘‘economically insecure’’ and unsurprisingly caused a decline in user interest, which already started after the announcement of Augur v2.0 in April 2020. To account for this decrease, we removed 162 cases that were either created after v2.0 was announced on 2 April 2020 or were not finalized before its actual launch, resulting in 2,735 cases and 22,772 events. This log can be understood to cover the complete lifecycle of Augur v1.0. For Forsage, the log is 6 GB in size and contains 1,055,931 cases with 13,368,052 events between 31 January 2020 and 15 April 2021. The former date marks the first DApp execution, while the latter marks the day on which we extracted the log. During that time DApp activity was phasing out and at the time of writing Forsage does not permit new users to join its Ethereum version. Hence, our 7 The source code for Augur v1 is available on: https://github.com/ AugurProject/augur-core, accessed 25 June 2024. 8 In the spirit of open science, we made artifacts created for data extraction and analysis available online via our blockchain data collection (Bandara et al. 2021): https://ingo-weber.github.io/dappdata/index.html. For Augur and Forsage, this includes scripts for data extraction and high resolution figures. For Forsage, we also provide Jupyter notebooks for analysis. Note that we could not share scripts for analyses performed in UI-based tools such as ProM or Disco. 123 R. Hobeck et al.: On the Suitability of Process Mining for Enhancing. . ., Bus Inf Syst Eng 67(6):777–796 (2025) 783 log captures events from the main period of activity of Forsage. For Augur, we concluded that the extracted event log covers relevant process steps. In contrast, for an analysis of Forsage, we noticed a need for additional information. That is because, the event attributes did not provide a meaningful metric to observe payments to and from the smart contracts directly, which is central to understanding the economic workings and effects of Forsage activities. We hence used a second, complementing data source to deal with the challenges. We queried the Etherscan API 9 to retrieve regular and internal transactions to and from Forsage’s central contract address. We also extracted the corresponding transaction fees for all user addresses active in the log, for the time frame of the analysis. Based on that, we created a balance sheet, allowing us to sum up each user’s total income and spending through interactions with Forsage. The challenges of interpreting Forsage are best illustrated by a comparison of the total user income and spending which were approximately 728k ETH and 768k ETH, respectively. Of the remaining 40.1k ETH, 39.6k ETH can be attributed to transaction fees, i.e., user addresses paying the Ethereum network for including transactions and executing Forsage smart contract functions. At the time of writing, we cannot explain the whereabouts of the remaining 500 ETH (0.06% of the turnover) despite our efforts to that end. Observation 3 The data available in blockchain log entries of Forsage did not cover all aspects relevant for the analysis. We were able to enhance the log-entry-based data structure with on-chain data on token movement to establish a database for our analysis In general, the extraction and analysis of blockchain data impose specific demands on the computational infrastructure. First, to have access to historical data, we needed to set up an Ethereum archive node for which the current hardware requirements include up to 12TB SSD disk space 10 . Second, the size of the Forsage event log posed a problem when applying common process-mining tools and platforms (ProM, Disco, and Python) on an offthe-self notebook with a 1.80 GHz CPU and 16 GB RAM. In particular, the used software packages failed to load the event log or failed to allocate sufficient memory when applying process-discovery or conformance-checking algorithms (more details in Sect. 6). To cope with the event log’s size and contrast behavior of different groups, we created subsets of the original log based on user success, as follows: Group A – successful user addresses: the 1000 addresses with the highest profits, Group B – average user addresses: 1000 addresses randomly sampled from user addresses with a balance between median and the 75%- percentile of the total, and Group C – unsuccessful user addresses: the 1000 addresses with the highest losses. For the analysis, event names were extended with the Forsage matrix upgrade level when applicable. Observation 4 Data extraction and analysis required access to sufficient computing infrastructure. For data analysis, we had to create subsets of the Forsage event log due to limitations of available hardware and software. Note that most data preparation, a crucial step in any process-mining project (see Fig. 3), was implemented as part of the ELF queries. However, depending on the specific analysis, we filtered the event data before applying process mining. Where relevant, details are outlined in Sect. 5and 6. 5 Process-Mining Analysis of Augur Augur’s white paper (Peterson et al. 2018) characterizes the mechanics of a prediction and betting market: ‘‘individuals can speculate on the outcomes of future events; those who forecast the outcome correctly win money, and those who forecast incorrectly lose money.’’ As a betting market organized on Ethereum, the developers claim that Augur bypasses the disadvantages of traditional betting markets, such as trusted market operators and limited participation (Peterson et al. 2018). Currently, two versions of Augur are available in parallel: Augur v1.0 (launched 9 July 2018) and Augur v2.0 (details announced April 2020 11 , launched 28 July 2020 12 ). To gain user trust, the Augur developers open-sourced the smart contracts and deployed both versions without any option to update or stop them – as giving themselves the privilege to do either might result in the loss of users’ cryptocurrency, so omitting that possibility strengthens trustworthiness. Hence, the new version is deployed in parallel to the old version, as such not comprising an update in any traditional sense. However, once the new version was deployed and users migrated to it, the old version became ‘‘economically insecure’’ according to the developer team, and therefore should no longer be used. Because prediction markets are long-running and hence extended observation time frames are crucial for their analysis, we nevertheless focused on 9 https://docs.etherscan.io/api-endpoints/accounts, accessed 1 Jun 2022. 10 https://ethereum.org/en/developers/docs/nodes-and-clients/ archive-nodes/, accessed: 12 Dec 2023. 11 https://twitter.com/AugurProject/status/1245715269042888706, accessed 14 Mar 2021. 12 https://www.augur.net/blog/augur-v2-launch/, accessed 14 Mar 2021. 123 784 R. Hobeck et al.: On the Suitability of Process Mining for Enhancing. . ., Bus Inf Syst Eng 67(6):777–796 (2025) Observation 17 Performance analysis delivered clues to confirm claims about temporal on-chain activity-outcome relations of Forsage. We also found social media posts with Forsage referral links and could associate them with some of the Group A accounts. 18 This indicates that accounts with active recruiting efforts may have generated higher income, supporting the fourth claim. However, searching for the most successful accounts or their referral links did not always turn up results. Observation 18 Blockchain account pseudonymity does not permit linking individuals to blockchain accounts, hampering the analysis of factors for user success based on off-chain components. 7 Discussion We started this paper by posing two research goals regarding the contributions of process mining to information transparency in a blockchain environment. In particular, we set a thematic focus on the support process mining can offer to address two widely recognized challenges of the blockchain community: code validation & verification and user behavior analysis. In the preceding sections, we applied process mining to blockchain applications and highlighted our observations. These observations are summarized in Table 2. In this section, we discuss these observations and put them in context of the two research goals. Subsequently, we discuss threats to validity. 7.1 Code Validation and Verification G1: Code Validation & Verification: Determine to what extent process mining can contribute to making DApps more transparent by supporting the validation and verification of their source code. In Sect. 2.3, we argued that process mining can contribute to solving code validation & verification challenges in the blockchain domain. In the two DApp analyses, we observed that applying process mining tackles these challenges to some degree. Process discovery helped with the coarse-grained validation of a DApp-based service by providing a control-flow visualization (O5). Process discovery also offered more fine-grained insights when the frequency and order of events were the subject of analysis, helping to validate and invalidate claims about expected behavior (O13). In this way, it delivered clues to identify a fraudulent scheme implemented in smart contracts (O13), a specific challenge highlighted in the blockchain literature (Risius and Spohrer 2017; Casino et al. 2019; Zheng et al. 2020). Conformance checking yielded detailed comparisons between the expected behavior of smart contracts and their actual execution. It showed conforming behavior (O9) and deviations from design specifications (O16), validating and invalidating different functionalities of the smart contract code. Conformance checking also helped to detect a software bug in smart contract code (O10) that can be addressed in new or updated versions of the DApp before deployment, a blockchain challenge highlighted by Rossi et al. (2019); Zheng et al. (2020). Using performance analysis, temporal aspects of code validation & verification can be checked (O17). To validate the correctness of our findings and assess the usefulness of the insights generated by our analyses, we interviewed Paul Gebheim, the chief architect of Augur. Given that we only interviewed one person, we classify results from this interview as anecdotal evidence; however, given his position, we believe this evidence is valuable. We asked him to check our assumptions – all of which he confirmed – and presented intermediate results from our analyses to him. From his perspective, using process mining for analyzing DApps generally, and Augur, in particular, provides value in three ways. First, it helps to verify the design mechanisms and check for unintended behavior and bugs in the (immutable) code; immutability poses a challenge from a BPM perspective (Mendling et al. 2018) and software engineering in general (Weber and Staples 2021). Second, process mining provides a clear view of how an application is used, which is also helpful for designing updated versions of an application. Third, it has great potential for technical and economic security analysis, e.g., an auditor could create a model and conformance-check it against actual user behavior. Also, even though a smart contract typically implements a fixed set of rules, analyses of process variability may reveal valuable insights that could help evolve future versions of the smart contract, e.g., to align them better with changed user expectations. Based on these findings, we infer that process mining can contribute to code validation & verification efforts intended to strengthen DApp transparency. Like other means of visualization and analysis (van Wijk 2005), the utility of process mining for code validation & verification depends on whether users can expect the value of the insights to exceed the cost to generate them. In this regard, we encountered a few issues that might impact the cost or value of process-mining insights. Familiarization with the DApp and its implementation is a crucial step for preparing data extraction and analysis. Costs to execute this step can be affected by the complexity 18 https://www.youtube.com/c/SergeyMaslakovprofitbiz, accessed 22 Jun 2022. 123 R. Hobeck et al.: On the Suitability of Process Mining for Enhancing. . ., Bus Inf Syst Eng 67(6):777–796 (2025) 791 and unavailability of information and code (O1). The costs might further be affected when the log entries emitted by the DApp do not sufficiently cover relevant process steps (O3). In the case of the Ponzi scheme Forsage, this was presumably due to the developers trying to obscure their intentions. Also, emitting log entries (on Ethereum) generally incurs transaction fees (Wood et al. 2014) that are ultimately paid by DApp users and might hence be avoided to not jeopardize DApp usage. Moreover, ensuring data quality required multiple iterations of developing and testing extraction scripts (O2). Testing of the data extraction is a common step in any process-mining project. However, in the context of blockchain applications the complexity (and hence the costs) of data extraction is higher when log entries are insufficient and users need to resort to other on-chain data sources (e.g., blocks, transactions, and transaction replay), or off-chain databases (O3). Lastly, costs are potentially incurred by hardware and capacity requirements that must be satisfied to synchronize and access the data of a blockchain node or to perform data analysis (O4). Regarding the value generated by process mining, there are a few constraints. In general, process mining focuses on behavioral aspects and might hence not be appropriate when users want to validate & verify aspects beyond DApp behavior, such as safety or costs. Similarly, we did not apply process mining as a design time or pre-deployment test for software vulnerabilities. That is, we relied on event logs that consist of blockchain events that were emitted at runtime, i.e., when code segments were invoked by users of the deployed DApps. Consequently, we could only analyze code sections and behavior when they were executed during runtime. Code that was not executed remained hidden from our analysis. Nevertheless, our DApp analyses show that process mining can serve as a tool to detect bugs and performance issues for blockchain applications post-deployment (based on actual code execution). Note that users could, in principle, also apply process mining for static analysis before code deployment – however, this is outside the scope of this article. Lastly, some process-mining analyses (in particular conformance checking) hinge on the documentation and Table 2 Observations (O) from the process-mining projects on Augur and Forsage Augur Forsage O1: Obtaining a foundational understanding of the two DApps and their inner workings was a critical step for data extraction and analysis, but was exacerbated by the unavailability and complexity of source code and documentation O2: While software such as ELF reduced technical implementation effort for data extraction, we still needed multiple iterations to diligently develop queries and ensure high data quality O4: Data extraction and analysis required access to sufficient computing infrastructure. For data analysis, we had to create subsets of the Forsage event log due to limitations of available hardware and software O5: Process discovery helped to visualize Augur’s smart contract execution and thus to conduct a first coarse-grained validation of the DApp’s implementation O6: Process-exploration helped plot an overview of DApp execution data, showing user activity gradients throughout Augur’s life cycle, incl. seasonal peak activity intervals O7: Conformance checking required information about a normative process that we had to transfer into a process model format suitable for the conformance-checking algorithm O8: Blockchain account pseudonymity allowed us to relate a behavioral pattern to a user, although personal information about the individual was not available O9: Using conformance checking, we found that in most instances Augur’s execution could be explained with a normative process description. We also found hints towards user-implemented automation protocols interacting with Augur’s application interface. O10: Using conformance checking, we found a deviation from the expected DApp behavior that turned out to be a bug in Augur’s smart contract code O11: With the help of performance analysis, we observed user strategy adjustments and maturing effects in the community behavior, streamlining market processes over time O3: The data available in blockchain log entries of Forsage did not cover all aspects relevant for the analysis. We were able to enhance the log-entry-based data structure with on-chain data on token movement to establish a database for our analysis O12: Blockchain account addresses provide a notion to assign events to actors on the blockchain. The pseudonymity of the accounts, however, did not permit relating user accounts to individuals interacting with the application O13: With process exploration and process discovery, we respectively confirmed or falsified claims about outcomes of usage scenarios of Forsage, based on event order and frequency. We found indicators for a fraudulent scheme using time-sensitive profit/loss analysis O14: With process exploration and process discovery, we analyzed user engagement with Forsage and user strategies across user groups, including strategy adjustments over time O15: Conformance checking required information about a normative process, which we captured in precedence rules that served as input for the conformance-checking algorithm O16: Using conformance checking, we tested the mechanics of Forsage and were able to point out deviations between the DApp’s communicated design specifications and the execution of the smart contract code O17: Performance analysis delivered clues to confirm claims about temporal on-chain activity-outcome relations of Forsage O18: Blockchain account pseudonymity does not permit linking individuals to blockchain accounts, hampering the analysis of factors for user success based on off-chain components 123 792 R. Hobeck et al.: On the Suitability of Process Mining for Enhancing. . ., Bus Inf Syst Eng 67(6):777–796 (2025) descriptions of the DApps’ to-be processes (O7 and O15), which may not be exhaustive or available in full. For behaviors that are not documented and/or that users might not be aware of, they will thus not be able to validate & verify them based on the actual code execution. 7.2 User Behavior Analysis G2: User Behavior Analysis: Determine to what extent process mining can contribute to making DApps more transparent by supporting the analysis of their users’ behavior. As pointed out in Sect. 2.3, the blockchain literature recognizes user behavior analysis as a research challenge. Our observations from the DApp analyses imply that process mining can contribute to understanding user behavior. The visualizations from process exploration provided insights about user behavior on a macro level, including waxing and waning user activity in the application’s lifetime and peak activity phases (O6). Gaining more fine-grained insights into user behavior required combining several findings from process exploration and process discovery. They included comparisons of behavior between user groups and information on user strategies adjusting to a changing application environment (O14). Additional behavioral adjustments over time, e.g., maturing effects in user behavior, can also be observed with performance analysis (O11). Performance analysis also helped to examine behavior across user groups in more depth, with information on user reaction time (O17). A different aspect of user behavior became evident from conformance checking, where results suggest that users automated part of their interaction with a DApp and thereby, by proxy, their interaction with other users (O9). Given the insights presented above, we infer that process mining can indeed contribute to DApp transparency by supporting the analysis of user behavior. Similar to code validation & verification, considerations related to the cost and value associated with applying process mining might limit the utility of process mining in a specific context. That is, familiarization can be exacerbated by the complexity and unavailability of information and code (O1), data extraction requires thorough testing (O2), and compute & storage capacity requirements must be met (O4). Regarding the degree to which log entries provide relevant data (O3), we note that essential user activity might not be organized on-chain, but off-chain, e.g., user recruiting on social media platforms in Forsage. Account pseudonymity allowed assigning events to accounts in the first place (O8, O12). However, in part due to the account pseudonymity, establishing data connectivity between offand on-chain data is challenging (O12, O18). In fact, during our analyses, we learned about off-chain activity but could not reliably relate such data with process participants in our event data on a larger scale (O18), apart from a few exceptions where pseudonyms were disclosed. The pseudonymity of blockchains does not only impact analysis costs but also limits the value of applying process mining to analyze user behavior. In our analyses (O11, O17), we interpreted results under the assumption that every Ethereum account address represents a unique user. The pseudonymity of accounts on Ethereum does not guarantee that assumption, so that multiple accounts may belong to the same user(s) (O12). For example, for Forsage, our loss-profit calculations may not represent some user’s net gains through Forsage. We also cannot distinguish multiple users copying each other’s behavior, or a single user applying the same (possibly automated) behavior multiple times. Finally, one (or multiple) accounts might be controlled by a group or team of users. We addressed the issue by differentiating users from user addresses in our writing (see Sect. 6), but the limitations on the possible insights remain. 7.3 Threats to Validity There are several threats to the validity of our findings (Wohlin et al. 2012, p. 68). For our analyses, we took on the role of conductors of process-mining activities and observers examining the process-mining activities. Our taking on the role of conductors does not affect the external validity of our analyses. On the one hand, we were not involved in the development of the DApps and hence took on the role of DApp users whose goal is to make these DApps more transparent. On the other hand, we followed a commonly applied methodology for applying process mining (see Sect. 3). However, we might have introduced a confirmation bias to our findings. The bias is mitigated to a certain degree as we initially conducted the Augur analysis open-ended, before finalizing this paper’s research goals. Furthermore, our analyses results are constrained by a few threats to internal validity. We might have introduced a bias in our conformance-checking approach for Augur. As a basis for conformance checking, we used the entire normative process model (see Fig. 4) and thus the overall control flow without checking the gate conditions for individual cases. That might have led to overly generalized results, ignoring non-conforming cases. Similarly, we might not have included all possible combinations of rules in the rule-based conformance-checking approach and hence might have missed non-conforming cases in Forsage. There is also a chance that Group B user addresses were not representative due to the random sampling. We might have introduced a form of selection bias by the choice of conformance-checking techniques, applying an alignment123 R. Hobeck et al.: On the Suitability of Process Mining for Enhancing. . ., Bus Inf Syst Eng 67(6):777–796 (2025) 793 based technique to Augur’s well-structured market process and rule checking to Forsage’s flexible investment process. Given the nature of the processes, these choices are sensible but might have influenced the quality of the analysis results. An external threat concerns our selection of DApps. We intended to strengthen the generalizability by conducting two DApp analyses but might have introduced a bias by our choice of use cases – different DApps might not have had deviations between their specifications and actual code execution. Similarly, another DApp’s log entries might not have covered relevant sections of the code execution leading to less insights with respect to the research goals. Another external threat to the study may be that the data we performed our analysis on were incomplete or its quality was corrupted. We did, however, take precautions in reducing these threats by validating intermediate results and findings with Augur’s user interface and their chief architect and cross-checking our results for Forsage with findings by Kell et al. (2021). 8 Conclusion and Future Work Information transparency is attributed to blockchain environments. Achieving such transparency in practice, however, depends on the availability of information and adequate data processing. We suggested that process mining could provide a data processing toolkit fit to contribute to blockchain transparency by addressing two blockchain research challenges: (1) code validation & verification and (2) user behavior analysis. We conducted two process-mining analyses for data extracted from the blockchain applications Augur and Forsage. To this end, we used ELF to extract data over essentially the entire lifecycle of Augur v1.0 and Forsage in its two-matrix setup. We used process-mining methods and tools to explore the data, discover process models, and conduct conformance-checking and performance analyses. We were able to show deviations from the expected code execution, we detected a bug in Augur’s smart contract code, and we falsified three of the four main claims about the system from Forsage’s promotional material. Process mining was also helpful for examining user behavior, including individual user activities and user strategies and their adjustments over time. Finally, we interviewed the chief architect of Augur to validate our insights and understand their usefulness, and cross-checked our Forsage results with academic publications. In summary, we identified patterns and motives in the blockchain data using process mining. Therefore, we conclude that there is strong support that process mining contributes to establishing transparency in blockchain environments in terms of (1) code validation & verification and (2) user behavior analysis. A critical direction for future research is the development of methods and tools that help users with DApp familiarization, data extraction, and insight validation – three areas that incurred costs or limited the utility of process-mining insights in our analyses. Moreover, the data basis can be extended beyond log entries and token transactions, e.g., to comprise replayed transaction traces in order to ensure completeness of the execution data. Also, our use cases relied on historic data generated partially years before the extraction. Real-time monitoring could provide timely insights into blockchain operations enabling swift responses to irregularities. Integrating process mining into the development lifecycle of smart contracts could provide pre-deployment and post-deployment monitoring, ensuring that smart contracts perform as intended. This integration could also facilitate continuous improvement of blockchain applications, aligning them with user expectations and requirements. Acknowledgements We are very grateful for the input of Paul Gebheim, chief architect at the Augur Project. We would also like to thank Martin Rebesky and Hendrik Bockrath for writing the first versions of the ELF manifests to extract Augur and Forsage event logs. Funding Open Access funding enabled and organized by Projekt DEAL. 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IEEE Comput Intell Mag 12(2):4–8 Andrews R, Suriadi S, Wynn M, ter Hofstede AHM, Rothwell S (2018) Improving patient flows at St. Andrew’s War Memorial Hospital’s emergency department through process mining. In: Business process management cases: digital innovation and business transformation in practice, pp 311–333 Bandara HD, Bockrath H, Hobeck R, Klinkmu ¨ller C, Pufahl L, Rebesky M, van der Aalst W, Weber I (2021) Event logs of ethereum-based applications. In: BPM’21: international conference on business process management, Rome, Italy 123 794 R. Hobeck et al.: On the Suitability of Process Mining for Enhancing. . ., Bus Inf Syst Eng 67(6):777–796 (2025) Bartoletti M, Carta S, Cimoli T, Saia R (2019) Dissecting ponzi schemes on Ethereum: identification, analysis, and impact. arXiv:1703.03779. Accessed 22 Jun 2022 Carmona J, Dongen B, Solti A, Weidlich M (2018) Conformance checking: relating processes and models. Springer, Heidelberg Casino F, Dasaklis TK, Patsakis C (2019) A systematic literature review of blockchain-based applications: current status, classification and open issues. Telemat Inform 36:55–81 Corradini F, Marcantoni F, Morichetta A, Polini A, Re B, Sampaolo M (2019) Enabling auditing of smart contracts through process mining. In: From software engineering to formal methods and tools, and back, pp 467–480 De Weerdt J, Schupp A, Vanderloock A, Baesens B (2013) Process mining for the multi-faceted analysis of business processes—a case study in a financial services organization. Comput Ind 64(1):57–67 Di Ciccio C et al (2018) Blockchain-based traceability of interorganisational business processes. In: Business modeling and software design, pp 56–68 Di Ciccio C, Meroni G, Plebani P (2020) Business process monitoring on blockchains: potentials and challenges. In: Enterprise, business-process and information systems modeling, pp 36–51 Hobeck R, Weber I (2023) Towards object-centric process mining for blockchain applications. In: Ko ¨pke J, Lo ´pez-Pintado O, Plattfaut R, Rehse JR, Gdowska K, Gonzalez-Lopez F, Munoz-Gama J, Smit K, van der Werf JMEM (eds) Business process management: blockchain, robotic process automation and educators forum. Springer, Cham, pp 51–65 Hobeck R, Klinkmu ¨ller C, Bandara HD, Weber I, van der Aalst W (2021) Process mining on blockchain data: a case study of Augur. In: BPM’21: International conference on business process management, Italy, Rome, pp 306–323 IEEE Task Force on Process Mining (2011) Process mining manifesto. In: Bpm workshops, LNBIP, vol 99. Springer, Heidelberg Jans M, van der Werf JM, Lybaert N, Vanhoof K (2011) A business process mining application for internal transaction fraud mitigation. Expert Syst Appl 38(10):13351–13359 Kell T, Yousaf H, Allen S, Meiklejohn S, Juels A (2021) Forsage: anatomy of a smart-contract pyramid scheme. CoRR abs/ 2105.04380. Accessed 22 Jun 2022 Klinkmu ¨ller C, Mu ¨ller R, Weber I (2019) Mining process mining practices: an exploratory characterization of information needs in process analytics. In: Hildebrandt T, van Dongen BF, Ro ¨glinger M, Mendling J (eds) Business process management. Springer, Cham, pp 322–337 Klinkmu ¨ller C, Ponomarev A, Tran AB, Weber I, van der Aalst WMP (2019) Mining blockchain processes: extracting process mining data from blockchain applications. In: BPM blockchain forum, pp 71–86 Klinkmu ¨ller C, Weber I, Ponomarev A, Tran AB, van der Aalst W (2020) Efficient logging for blockchain applications. CoRR abs/ 2001.10281. Accessed 21 Mar 2021 Koschmider A, Duchmann F (2021) Extraction of meaningful events for process mining from blockchain. Springer, Cham, pp 13–29 Lamghari Z (2023) Towards the process mining applicability in the chickenhunt blockchain game. Int J Comput Digital Syst 13(1):1–1 Leemans SJ, Poppe E, Wynn MT (2019) Directly follows-based process mining: exploration and a case study. In: 2019 international conference on process mining (ICPM), pp 25–32. https:// doi.org/10.1109/ICPM.2019.00015 Leite JCSP, Cappelli C (2010) Software transparency. Bus Inf Syst Eng 2(3):127–139. https://doi.org/10.1007/s12599-010-0102-z Lemos AM, Sabino CC, Lima RMF, Oliveira CAL (2011) Using process mining in software development process management: a case study. In: 2011 IEEE international conference on systems, man, and cybernetics, pp 1181–1186 Leonardi PM, Treem JW (2020) Behavioral visibility: a new paradigm for organization studies in the age of digitization, digitalization, and datafication. Organ Stud 41(12):1601–1625 Letia IA, Goron A (2015) Model checking as support for inspecting compliance to rules in flexible processes. J Vis Lang Comput 28:100–121 Lo ´pez-Pintado O, Garcı ´a-Ban ˜uelos L, Dumas M, Weber I (2017) Caterpillar: a blockchain-based business process management system. BPM (Demos) 172 Mans R, Schonenberg MH, Song M, van der Aalst WMP, Bakker P (2009) Application of process mining in healthcare: a case study in a Dutch hospital. Biomed Eng Syst Technol 25:425–438 Mendling J et al (2018) Blockchains for business process management–challenges and opportunities. ACM Transact Manag Inf Syst (TMIS) 9(1):4:1-4:16 Moctar M’Baba L, Assy N, Sellami M, Gaaloul W, Farouk Nanne M (2023) Process mining for artifact-centric blockchain applications. Sim Model Pract Theor 127(102):779. https://doi.org/10. 1016/j.simpat.2023.102779 Mu ¨hlberger R, Bachhofner S, Di Ciccio C, Garcı ´a-Ban ˜uelos L, Lo ´pez-Pintado O (2019) Extracting event logs for process mining from data stored on the blockchain. In: Business process management workshops, pp 690–703 Mu ¨ller M, Ruppel P (2019) Process mining for decentralized applications. In: IEEE international conference on decentralized applications and infrastructures, pp 164–169 Peterson J, Krug J, Zoltu M, Williams AK, Alexander S (2018) Augur: a decentralized oracle and prediction market platform. Technical report, Forecast Foundation. https://github.com/Augur Project/whitepaper/blob/master/v1/english/whitepaper.pdf. Accessed 05 Jan 2021 Prusty N (2017) Building blockchain projects. Packt, Birmingham Qasse IA, Spillner J, Talib MA, Nasir Q (2020) A study on Ðapps characteristics. In: 2020 IEEE international conference on decentralized applications and infrastructures (DAPPS), pp 88–93 Recker J (2021) Scientific research in information systems: a beginner’s guide. Springer, Heidelberg Reinkemeyer L (2020) Process mining in action: principles, use cases and outlook. Springer, Heidelberg Risius M, Spohrer K (2017) A blockchain research framework: what we (don’t) know, where we go from here, and how we will get there. Bus Inf Syst Eng 59:385–409 Rossi M, Mueller-Bloch C, Thatcher JB, Beck R (2019) Blockchain research in information systems: current trends and an inclusive future research agenda. J Assoc Inf Syst 20(9):14 Rovani M, Maggi FM, Leoni M, van der Aalst WMP (2015) Declarative process mining in healthcare. Expert Syst Appl 42(23):9236–9251 Rozinat A, de Jong ISM, Gu ¨nther CW, van der Aalst WMP (2009) Process mining applied to the test process of wafer scanners in ASML. IEEE Trans Syst Man Cybern Part C 39(4):474–479 Sharma P, Jindal R, Borah MD (2023) A review of smart contractbased platforms, applications, and challenges. Cluster Comput 26(1):395–421 Suriadi S, Mans RS, Wynn MT, Partington A, Karnon J (2014) Measuring patient flow variations: a cross-organisational process mining approach. In: Asia pacific business process management, pp 43–58 Vacca A, Di Sorbo A, Visaggio CA, Canfora G (2021) A systematic literature review of blockchain and smart contract development: techniques, tools, and open challenges. J Syst Softw 174(110):891 123 R. Hobeck et al.: On the Suitability of Process Mining for Enhancing. . ., Bus Inf Syst Eng 67(6):777–796 (2025) 795 van der Aalst WMP et al (2007) Business process mining: an industrial application. Inf Syst 32(5):713–732 van der Aalst WMP (2016) Process mining: data science in action. Springer, Heidelberg van Eck ML, Lu X, Leemans SJJ, van der Aalst WMP (2015) PM2: a process mining project methodology. In: Zdravkovic J, Kirikova M, Johannesson P (eds) Advanced information systems engineering. Springer International, Cham, pp 297–313 van Wijk J (2005) The value of visualization. In: Vis 05. IEEE visualization, pp 79–86. https://doi.org/10.1109/VISUAL.2005. 1532781 vom Brocke J, Jans M, Mendling J, Reijers HA (2021) A five-level framework for research on process mining. Bus Inf Syst Eng 1–8 Weber I, Staples M (2021) Programmable money: next-generation conditional payments using blockchain—keynote paper. In: International conference on cloud computing and services science (CLOSER) Weber I, Xu X, Riveret R, Governatori G, Ponomarev A, Mendling J (2016) Untrusted business process monitoring and execution using blockchain. In: International conference on business process management, Rio de Janeiro, Brazil Weber I et al (2017) On availability for blockchain-based systems. In: IEEE international symposium on reliable distributed systems (SRDS), pp 64–73 Wohlin C, Runeson P, Ho ¨st M, Ohlsson MC, Regnell B, Wessle ´nA (2012) Experimentation in software engineering. Springer, Heidelberg Wood G et al (2014) Ethereum: a secure decentralised generalised transaction ledger. Ethereum project yellow paper 151:1–32 Wu K, Ma Y, Huang G, Liu X (2021) A first look at blockchain-based decentralized applications. Softw Pract Exp 51(10):2033–2050 Xu X, Weber I, Staples M (2019) Architecture for blockchain applications. Springer, Heidelberg Zheng Z, Xie S, Dai HN, Chen X, Wang H (2018) Blockchain challenges and opportunities: a survey. Int J Web Grid Serv 14(4):352–375 Zheng Z, Xie S, Dai HN, Chen W, Chen X, Weng J, Imran M (2020) An overview on smart contracts: challenges, advances and platforms. Futur Gener Comput Syst 105:475–491 123 796 R. Hobeck et al.: On the Suitability of Process Mining for Enhancing. . ., Bus Inf Syst Eng 67(6):777–796 (2025)