2. Research Contribution and Plan
The dissertation will aim at providing an approach to process mining on blockchain data. Initially,
the usefulness and suitability of process mining on blockchain application data obtained with
existing extraction tools will be examined in case studies [
5
] to establish two base assumptions:
CEUR
1) Smart contract code can represent a meaningful process that can be executed, and: 2) Process
execution data of smart contract code can be extracted from a blockchain and its analysis
with process mining techniques can be valuable. Would these assumptions not hold, process
mining on blockchain data would not be possible. That results in the research question: RQ1) Is
applying process mining techniques to blockchain application data feasible and useful? To achieve
that, we will make event data available by extracting it from the blockchain, for which queries
and third party solutions already exist. In a second step, we intend to answer: RQ2) How can
existing tools for extracting event data from blockchains be extended benefiting event log quality?
Foremost, existing solutions currently are limited to smart contracts with known addresses,
while it is an open challenge to identify temporarily invoked contracts and extract data they
logged. For logging, these temporary nested contracts go unnoticed. Primary suspect for the
identification of invoked contracts is the transaction receipt that is stored on-chain to log called
contract’s addresses. Open to research remains extracting the contract code and specifying
which event data shall be logged automatically - a task that so far has been manual. Tied to
nested logging, is the logging format. In a nested logging scenario, a clear separation of traces,
vital for logging in XES format, may not be suitable. Hence, we plan to explore shifting to
OCEL or event knowledge graphs to better accommodate documenting blockchain application
event logs. Based on RQ1 and RQ2, we will move from a post-mortem (historic) analysis of
event logs into an online-setting in RQ3) What does a process monitoring solution for blockchain
applications look like that benefits application designers and users? We will conceptualize a
process monitoring approach tailored to the blockchain environment. The approach will be
designed with respect to the following design challenges: event streaming in a blockchain
environment, blockchain-specific process KPIs, and technical blockchain peculiarities, e.g., block
inclusion for ledgers with diferent consensus protocols.
Generally, this dissertation will be written in the spirit of Design Science Research, taking into
account the design guidelines [
6
]. Our research endeavors will mostly be viewed as problems
that can be solved by designing and developing solutions which need to be demonstrated and
evaluated. The approaches will be implemented and evaluated using existing technologies such
as [
7, 8
]. The evaluations will take into account previously defined requirements against which
the implementations are checked.
3. Related Work
Several prior contributions with related content have been published in the past and will be
contextualized briefly. Data Extraction. Process execution data of blockchain-based
applications that is stored on-chain can generally be accessed through native APIs or language-specific
API wrappers. The extraction of the data and its provision in an event log, however, is not an
easy task [
9
] and was subject to prior research [
10
]. Most notably the tool Ethereum Logging
Framework (ELF) ofers a configurable logging solution for the Ethereum blockchain [
11, 8
].
Utilizing blockchain application data. Extracted blockchain data has been put to use for
validating smart contracts on Hyperledger Fabric [
12
], auditing blockchain applications on the
Ethereum [
13
], analyzing transactions on Ethereum without focusing on specific DApps [ 14],
and using process mining on blockchain-wide data [15] but no single process in particular.
Case studies. The suitability of process mining for data from classic data sources (ERP, CRM,
etc.) has been covered in various case studies across industries [16]. In these industries process
mining was applied to analyze processes from diferent perspectives including control flow,
conformance, drifts, and performance [ 17]. A study showing value in process mining on blockchain
data has been added only recently as part of the dissertation [18]. Monitoring solutions.
Monitoring of processes is based on the analysis of process execution data. Process monitoring
is widely considered to be based on process mining techniques and divided in ofline and online
monitoring [1, p. 413]. Online monitoring requires event streaming [19] which has been subject
of research [20]. An example of monitoring a choreography of process participants based on
message-flow was provided by [ 21]. Furthermore, potential and challenges for monitoring
business processes have been sketched previously [22].
[14] R. Mühlberger, S. Bachhofner, C. Di Ciccio, L. García-Bañuelos, O. López-Pintado,
Extracting event logs for process mining from data stored on the blockchain, in: Business Process
Management Workshops, 2019, pp. 690–703.
[15] M. Müller, P. Ruppel, Process mining for decentralized applications, in: IEEE Int. Conf. on
Decentralized Applications and Infrastructures, 2019, pp. 164–169.
[16] R. Andrews, S. Suriadi, M. Wynn, A. H. M. ter Hofstede, S. Rothwell, 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, 2018, pp. 311–333.
[17] L. Reinkemeyer, Process mining in action: Principles, use cases and outlook,
Springer
Verlag, Berlin, 2020.
[18] R. Hobeck, C. Klinkmüller, H. D. Bandara, I. Weber, W. van der Aalst, Process mining on
blockchain data: A case study of Augur, in: BPM’21: International Conference on Business
Process Management, Rome, Italy, 2021, pp. 306–323.
[19] J. Gama, Knowledge discovery from data streams, CRC Press, 2010.
[20] A. Burattin, Streaming process mining, Process Mining Handbook (2022) 349.
[21] I. Weber, X. Xu, R. Riveret, G. Governatori, A. Ponomarev, J. Mendling, Untrusted business
process monitoring and execution using blockchain, in: Int. Conf. on Business Process
Management, Rio de Janeiro, Brazil, 2016.
[22] C. Di Ciccio, G. Meroni, P. Plebani, On the adoption of blockchain for business process
monitoring, Software and Systems Modeling 21 (2022) 915–937. URL: https://doi.org/10.
1007/s10270-021-00959-x. doi:1 0 . 1 0 0 7 / s 1 0 2 7 0 - 0 2 1 - 0 0 9 5 9 - x .