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Process mining has become an established set of tools and methods for analyzing process data, while blockchain is emerging as a platform for decentralized applications and inter-organizational processes. Approaches and tools have been developed for analyzing blockchain data with process mining methods, including the tools created by us: BlockXES, ELF, and BLF. Recently, we have shown that process mining on blockchain data is valuable, among others for understanding user behavior and for security audits. With this resources paper, we make four different data sets available in XES format, stemming from four different blockchain applications: Augur, Forsage, CryptoKitties, and ChickenHunt. We describe the method of extraction, data sets, and conduct preliminary analyses to demonstrate feasibility. This publication aims to help researchers and practitioners to understand the application domain, and enables future process mining research on the data sets.
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Event Logs of Ethereum-Based Applications
A Collection of Resources for Process Mining on Blockchain Data
H.M.N. Dilum Bandara1,4,Hendrik Bockrath2,Richard Hobeck2,
Christopher Klinkmüller1,Luise Pufahl2,Martin Rebesky2,Wil van der Aalst3and
Ingo Weber2
1Data61, CSIRO, Sydney, Australia
2Chair of Software and Business Engineering, Technische Universitaet Berlin, Germany
3RWTH Aachen University, Germany
4The authors are ordered alphabetically by family name.
Abstract
Process mining has become an established set of tools and methods for analyzing process data, while
blockchain is emerging as a platform for decentralized applications and inter-organizational processes.
Approaches and tools have been developed for analyzing blockchain data with process mining methods,
including the tools created by us: BlockXES, ELF, and BLF. Recently, we have shown that process mining
on blockchain data is valuable, among others for understanding user behavior and for security audits.
With this resources paper, we make four dierent data sets available in XES format, stemming from
four dierent blockchain applications: Augur, Forsage, CryptoKitties, and ChickenHunt. We describe
the method of extraction, data sets, and conduct preliminary analyses to demonstrate feasibility. This
publication aims to help researchers and practitioners to understand the application domain, and enables
future process mining research on the data sets.
Keywords
Ethereum Logging Framework, Event logs, Process Mining, Blockchain
1. Introduction
Process mining [
1
] has established as a set of tools and methods for analyzing process data.
Blockchain [
2
] is emerging as a platform for decentralized applications and inter-organizational
processes. Approaches and tools have been developed for analyzing blockchain data with process
mining methods, including the tools created by us: BlockXES [
3
], ELF [
4
], and BLF [
5
].Although
challenging [
6
], we recently showed that process mining on blockchain data is valuable, among
others to understand user behavior and for security audits [7].
With this resource paper, we publish a collection of event logs from blockchain-based
decen-
tralized applications
(DApps). The event logs are available in XES format and currently cover
four DApps:
Demonstration & Resources Track, Best BPM Dissertation Award, and Doctoral Consortium at BPM 2021 co-located
with the 19th International Conference on Business Process Management, BPM 2021, Rome, Italy, September 6-10, 2021
"dilum.bandara@data61.csiro.au (H.M.N. D. Bandara); rstname.lastname@tu-berlin.de (H. Bockrath);
rstname.lastname@tu-berlin.de (R. Hobeck); christopher.klinkmueller@data61.csiro.au (C. Klinkmüller);
rstname.lastname@tu-berlin.de (L. Pufahl); rstname.lastname@tu-berlin.de (M. Rebesky);
wvdaalst@pads.rwth-aachen.de (W. van der Aalst); rstname.lastname@tu-berlin.de (I. Weber)
©2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
1
H.M.N. Dilum Bandara et al. CEUR Workshop Proceedings 1–5
1. Augur, a prediction and betting marketplace;
2. Forsage, an investment application, which turns out to be a Ponzi scheme;
3. CryptoKitties, a game where virtual cats can be bred and traded as assets;
4. ChickenHunt, a game for collecting chickens and upgrading the avatar.
All four applications and their data are available on the public Ethereum blockchain. Still,
extracting the data is non-trivial [
6
], and with this publication, we also release the artifacts
for the data extraction. In particular, we use the open-source
Ethereum Logging Framework
(ELF) [
4
], which takes a manifest as input. Manifests dene which on-chain data to extract, and
how to transform and format it, e.g., as CSV or XES les. They can hence be used for various
purposes. For example, users can query log entry data from a given smart contract address over
a range of blocks. For each of the four DApps, a manifest was crafted and used with ELF to
extract data from a full Ethereum archival node. The collection of event logs is made available
via a website
1
. For each data set it includes the ELF manifest, the XES event log, links to the
DApp source code and website, a description of the XES log content, and preliminary analysis
results.
In the following, we describe the data sets and conduct preliminary analyses to demonstrate
feasibility. This publication aims to help researchers and practitioners to understand the
application domain, and enables future process mining research on the data sets, e.g., for
analysis and evaluation purposes.
2. Description of the data sets
All data sets are made available as event logs in XES format. The events we extracted from
the DApps were encoded in the blocks of the public Ethereum blockchain. Data extraction for
each DApp started with the rst block after its deployment and ends with block 12,243,999
(one block before the Berlin Hard Fork)
2
. Note that we extracted data from Augur at an earlier
point for our case study in [
7
]. Hence, the respective log only covers data until block 10,336,628.
While the logs have a varying number of attributes depending on the events generated by
the corresponding DApp, each log has a common set of attributes, namely
Case ID
,
Activity
,
Complete Timestamp
, and
lifecycle:transition
. The additional attributes are described on the
website accompanying this paper (see Footnote 1). The timestamps of the events correspond to
the timestamps of the block they were extracted from. Additionally, the logs contain DApp or
Ethereum-specic attributes, e.g.,
gasPaid
or
receivingContract
in Augur. Table 1presents key
gures of the data sets.
3. Preliminary analysis
For the preliminary analysis, we focus on the event log of ChickenHunt. For Augur, an extensive
case study has been published recently [
7
]. Preliminary analyses of Forsage and CryptoKitties
can be found on the accompanying website (see Footnote 1).
1https://ingo-weber.github.io/dapp-data/
2https://blog.ethereum.org/2021/03/08/ethereum-berlin-upgrade-announcement/
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H.M.N. Dilum Bandara et al. CEUR Workshop Proceedings 1–5
Table 1
Overview over the data sets.
DApp data set Augur Forsage CryptoKitties ChickenHunt
Start date 2018-07-10 2020-01-31 2017-11-23 2018-06-25
Start block 5,937,093 9,391,531 4,605,167 5,851,533
Last date 2020-11-10 2021-04-15 2021-04-15 2021-02-16
Last block 10,336,628 12,243,749 12,243,893 11,866,129
Events 23,021 13,368,052 18,059,296 138,889
Cases 2897 1,055,931 1,997,604 715
Activities 11 12 12 17
ChickenHunt is an incremental game that is deployed as a DApp on Ethereum. The game’s
goal is to collect chickens through farming and attacking other players. Players also have the
option to upgrade the attack (“Upgrade Hunter”), defense (“Upgrade Depot”), and collection
capabilities (“Upgrade Pet”) of their avatars. The player pays the gas costs for the Ethereum
transactions. The game concept includes two types of incentives for playing.
Shareholder:
through certain transactions, players can become shareholders of the game; and
nancial
reward: players can sacrice collected chickens for Ether.
We loaded the event log into several process mining tools to analyze the players’ behavior,
but here we focus on results obtained with ProM. In Fig. 1a, the most common behavior of
(a) Most common traces in ChickenHunt (b) DFG with only upgrade activities
Figure 1: Initial process mining results from the ChickenHunt log.
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H.M.N. Dilum Bandara et al. CEUR Workshop Proceedings 1–5
Figure 2: Dotted chart: ChickenHunt attack behavior.
players is shown: 107 players out of the 715 cases join chicken hunt and never did anything
else. Several frequent traces show players joining, and then being attacked (one or more times)
without doing anything else. Some players follow a similar pattern, but rst they succeed with
bringing chickens to the altar. These insights could help understand why players stop early, and
be used by the developers when working on improvements to promote the user base to grow.
Players who actively play the game have highly varied individual traces: 402 distinct traces
exist for the 715 cases. In Fig. 1b, we analyzed the order and frequency of the dierent types of
upgrades. Upgrading the hunter avatar is the most popular choice, and by far the most frequent
rst and last upgrade. In other words, active players may also upgrade their pet and their depot,
but they typically come back to upgrade their hunter further. These insights, too, appear to be
of value for the providers of such a game.
Next, we direct our attention to attack behavior. The dotted chart in Fig. 2shows only the
events from joining, attacking, and suering from an attack. It can be observed that only a few
players attack others, but a large number of players are suering from attacks. Additionally, the
attacks appear to happen in synchronized waves, as indicated by the vertical patterns in the
dotted chart. The reasons behind those waves may well be connected to the gas prices (and
accordingly the fees) per transaction on Ethereum
3
: from a visual comparison of the timelines,
higher gas prices on Ethereum may well correlate with periods without attacks on ChickenHunt.
Presumably, the attackers stole chickens from ordinary users, brought them to the altar, and
received Ether in return, all of which entailing transactions with associated fees. If the returns
in Ether are not high enough, the fees may well render this operation a nancial loss.
3https://etherscan.io/chart/gasprice
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H.M.N. Dilum Bandara et al. CEUR Workshop Proceedings 1–5
4. Conclusion
With this paper, we provide a collection of four event logs extracted from blockchain applications,
with detailed descriptions and preliminary analyses. The collection is publicly available (see
Footnote 1). Currently, it comprises a set of four event logs that were extracted with the tool
ELF from DApps deployed and executed on the public Ethereum blockchain. In the paper,
we included an analysis based on the ChickenHunt event log, which serves as evidence that
insights can be discovered from these logs with standard process mining techniques. For the
other logs, analyses are available via the website. The data can be analyzed in much more detail
by applying additional process mining methods, and presumably holds blockchain-specic and
independent insights which we invite the community to explore.
We plan to amend the collection with additional data sets. In addition, we invite other
researchers to contribute their data sets via the openly accessible GitHub repository4.
References
[1]
W. M. P. van der Aalst, Process mining: Data science in action, Springer-Verlag, Berlin, 2016.
[2] X. Xu, I. Weber, M. Staples, Architecture for Blockchain Applications, Springer, 2019.
[3]
C. Klinkmüller, A. Ponomarev, A. B. Tran, I. Weber, W. M. P. van der Aalst, Mining
blockchain processes: Extracting process mining data from blockchain applications, in:
BPM (Blockchain Forum), 2019, pp. 71–86.
[4]
C. Klinkmüller, I. Weber, A. Ponomarev, A. B. Tran, W. Aalst, Ecient Logging for Blockchain
Applications, Computing Research Repository (CoRR) in arXiv abs/2001.10281 (2020). URL:
https://arxiv.org/abs/2001.10281.
[5]
P. Beck, H. Bockrath, T. Knoche, M. Digtiar, T. Petrich, D. Romanchenko, R. Hobeck, L. Pufahl,
C. Klinkmüller, I. Weber, A blockchain logging framework for mining blockchain data, in:
BPM (Demos & Resources Forum), 2021.
[6]
C. Di Ciccio, et al., Blockchain-based traceability of inter-organisational business processes,
in: BMSD, 2018.
[7]
R. Hobeck, C. Klinkmüller, H. M. N. D. Bandara, I. Weber, W. van der Aalst, Process mining
on blockchain data: A case study of augur, in: BPM, 2021.
4https://github.com/ingo-weber/dapp-data
5
ResearchGate has not been able to resolve any citations for this publication.
Chapter
Full-text available
Blockchain technology opens up new opportunities for Business Process Management. This is mainly due to its unprecedented capability to let transactions be automatically executed and recorded by Smart Contracts in multi-peer environments, in a decentralised fashion and without central authoritative players to govern the workflow. In this way, blockchains also provide traceability. Traceability of information plays a pivotal role particularly in those supply chains where multiple parties are involved and rigorous criteria must be fulfilled to lead to a successful outcome. In this paper, we investigate how to run a business process in the context of a supply chain on a blockchain infrastructure so as to provide full traceability of its run-time enactment. Our approach retrieves information to trace process instances execution solely from the transactions written on-chain. To do so, hash-codes are reverse-engineered based on the Solidity Smart Contract encoding of the generating process. We show the results of our investigation by means of an implemented software prototype, with a case study on the reportedly challenging context of the pharmaceutical supply chain.
Book
This is the second edition of Wil van der Aalst’s seminal book on process mining, which now discusses the field also in the broader context of data science and big data approaches. It includes several additions and updates, e.g. on inductive mining techniques, the notion of alignments, a considerably expanded section on software tools and a completely new chapter of process mining in the large. It is self-contained, while at the same time covering the entire process-mining spectrum from process discovery to predictive analytics. After a general introduction to data science and process mining in Part I, Part II provides the basics of business process modeling and data mining necessary to understand the remainder of the book. Next, Part III focuses on process discovery as the most important process mining task, while Part IV moves beyond discovering the control flow of processes, highlighting conformance checking, and organizational and time perspectives. Part V offers a guide to successfully applying process mining in practice, including an introduction to the widely used open-source tool ProM and several commercial products. Lastly, Part VI takes a step back, reflecting on the material presented and the key open challenges. Overall, this book provides a comprehensive overview of the state of the art in process mining. It is intended for business process analysts, business consultants, process managers, graduate students, and BPM researchers.
  • X Xu
  • I Weber
  • M Staples
X. Xu, I. Weber, M. Staples, Architecture for Blockchain Applications, Springer, 2019.
Mining blockchain processes: Extracting process mining data from blockchain applications
  • C Klinkmüller
  • A Ponomarev
  • A B Tran
  • I Weber
  • W M P Van Der Aalst
C. Klinkmüller, A. Ponomarev, A. B. Tran, I. Weber, W. M. P. van der Aalst, Mining blockchain processes: Extracting process mining data from blockchain applications, in: BPM (Blockchain Forum), 2019, pp. 71-86.
A blockchain logging framework for mining blockchain data
  • P Beck
  • H Bockrath
  • T Knoche
  • M Digtiar
  • T Petrich
  • D Romanchenko
  • R Hobeck
  • L Pufahl
  • C Klinkmüller
  • I Weber
P. Beck, H. Bockrath, T. Knoche, M. Digtiar, T. Petrich, D. Romanchenko, R. Hobeck, L. Pufahl, C. Klinkmüller, I. Weber, A blockchain logging framework for mining blockchain data, in: BPM (Demos & Resources Forum), 2021.