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31 results for “Process Mining”
An IoT-Enriched Event Log for Process Mining in Smart Factories
<p><strong>DEPRECATED - current version: </strong><a href="https://figshare.com/articles/dataset/Dataset_An_IoT-Enriched_Event_Log_for_Process_Mining_in_Smart_Factories/20130794">https://figshare.com/articles/dataset/Dataset_An_IoT-Enriched_Event_Log_for_Process_Mining_in_Smart_Factories/20130794</a></p> <p> </p> <p>Modern technologies such as the Internet of Things (IoT) are becoming increasingly important in various domains, including Business Process Management (BPM) research. One main research area in BPM is process mining, which can be used to analyze event logs, e.g., for checking the conformance of running processes. However, there are only a few IoT-based event logs available for research purposes. Some of them are artificially generated, and the problem occurs that they do not always completely reflect the actual physical properties of smart environments. In this paper, we present an IoT-enriched XES event log that is generated by a physical smart factory. For this purpose, we created the DataStream XES extension for representing IoT-data in event logs. Finally, we present some preliminary analysis and properties of the log.</p>
Processed data for "Model identification of neural encoding (MINE)" publication
<p>This dataset contains mouse and zebrafish data processed by MINE. These datafiles were used to generate the publication figures for the mouse cortical dataset [m<em>usall.hdf5</em>] (Figure 5) and the zebrafish whole-brain [<em>main_analysis.hdf5</em>] (Figures 6 and 7) and reticulospinal datasets [r<em>s_analysis.hdf5</em>] (Figure 6).</p> <p> </p> <p><em>Musall.hdf5 </em>contains reordered data from "Musall, S., Kaufman, M.T., Juavinett, A.L. <em>et al.</em> Single-trial neural dynamics are dominated by richly varied movements. <em>Nat Neurosci</em> <strong>22</strong>, 1677–1686 (2019)."</p> <p>The contents of each dataset are described in <em>DataContent_xxx.pdf</em></p>
Dataset: Behavior of Participants in Hands-on Cybersecurity Training Suitable for Process Mining
<p>This repository contains supplementary materials for the following journal paper:</p> <p>Radek Ošlejšek, Martin Macák, Karolína Dočkalová Burská.<br><em>Hands-on cybersecurity training behavior data for process mining.</em><br>In Elsevier Data in Brief. 2023.<br>Available as open-access article on <a href="https://doi.org/10.1016/j.dib.2023.109956">https://doi.org/10.1016/j.dib.2023.109956</a></p> <p><strong>Contents</strong></p> <p>Datasets store event logs of trainees participating in hands-on cybersecurity exercises organized in the <a href="https://www.kypo.cz">KYPO Cyber Range</a>. The data includes training scenarios (expected behavior), raw event logs in the JSON format, and aggregated behavioral data suitable for process mining analysis.</p> <ol> <li><strong>Data1:</strong> A dataset of 52 trainees participating in the <a href="https://gitlab.ics.muni.cz/muni-kypo-trainings/games/locust-3302">Locust 3302</a> exercise adapted an insider attack scenario. No time restrictions were posed on playtime. The data file is structured as follows: <ul> <li>training_definition.json: The exercise content – cybersecurity tasks and hints. The training is based on the <a href="https://gitlab.ics.muni.cz/muni-kypo-trainings/games/locust-3302">Locust 3302</a> game adapted to an insider attack scenario.</li> <li>training_events: Recorded progress of trainees within the exercise, i.e., the status of completing tasks.</li> <li>command_histories: Recorded commands executed on network hosts.</li> <li>process_mining.csv: Complete PM-ready dataset suitable for process discovery or conformance analysis.</li> <li>process_mining_simplified.csv : Reduced PM-ready dataset with semantically identical events being removed.</li> </ul> </li> <li><strong>Data2:</strong> A dataset of 48 trainees participating in the original <a href="https://gitlab.ics.muni.cz/muni-kypo-trainings/games/locust-3302">Locust 3302</a> exercise. Three supervised training sessions were restricted to two hours of playtime. The structure follows the structure of Data1.</li> <li><strong>Tool:</strong> A Java application used to aggregate raw JSON data and transform them into a CSV format suitable for process mining techniques.</li> </ol> <p><strong>How to cite</strong></p> <p>If you use or build upon the materials, please use the BibTeX entry below to cite the original work.</p> <pre><code>@article{Oslejsek2023dataset, author = {Radek O\v{s}lej\v{s}ek and Martin Mac\'{a}k and Karol\'{i}na {Do\v{c}kalov\'{a} Bursk\'{a}}}, title = {Hands-on cybersecurity training behavior data for process mining}, journal = {{Data in Brief}}, publisher = {Elsevier}, issn = {2352-3409}, year = {2023}, volume = {52}, doi = {10.1016/j.dib.2023.109956}, url = {https://www.sciencedirect.com/science/article/pii/S2352340923009873} }</code></pre>
Comprehensive Ethereum Execution Data for Object-Centric Process Mining of Decentralized Applications (DApps)
<p>The dataset pertains to the collection and analysis of blockchain execution data, particularly from Ethereum-based Decentralized Applications (DApps). This data includes transactions, transaction receipts, and detailed transaction traces, documenting the execution steps performed by the Ethereum Virtual Machine (EVM). Such traces are essential for understanding the interaction between smart contracts and accounts, including Contract Accounts (CAs) and Externally Owned Accounts (EOAs).</p> <p>A blockchain is an append-only ledger that chronologically records data in blocks. Each block contains transactions that signify state transitions, and transaction receipts that provide a hashed result of these transitions to ensure uniform results across different executions. The dataset includes a classification of Ethereum accounts, detailing the functions and interactions between EOAs and CAs, where CAs deploy and execute smart contract code.</p> <p>The dataset captures the granular operational data of blockchain transactions, such as function calls, contract creations, and log entries generated by smart contracts. These details are crucial for creating object-centric event logs, aiding in process mining and analysis to bridge the gap between theoretical process models and actual execution.</p> <p>Contract creations and function calls are fundamental components of the dataset. The former documents the deployment of smart contracts, including the mechanics of contract updates and additions through various design patterns. Function calls between accounts are also extensively logged, providing insights into the flow of Ethereum's native token, Ether, and other transactional data within the blockchain.</p> <p>Delegated calls and log entries represent more specialized interactions within Ethereum, where delegated calls allow contracts to use code from other contracts to manipulate their own state, supporting upgradeable contract designs. Log entries, specified within smart contract code, facilitate the communication of contract execution details to external systems.</p> <p>To handle the diverse and dynamic nature of blockchain data, the dataset employs the Object-Centric Event Log (OCEL) format. This format accommodates multiple object types in a single log, addressing issues such as event divergence and convergence, typical of traditional single-case logs. The latest version, OCEL 2.0, supports documenting dynamic object roles and relationships, improving the fidelity of logs in capturing blockchain operations.</p> <p>In summary, the dataset is structured to support a comprehensive analysis of blockchain behaviors, particularly focusing on Ethereum DApps. It is tailored to assist researchers and practitioners in understanding and analyzing the decentralized execution of smart contracts and the associated data flows within the blockchain environment.</p>
An Empirical Evaluation of Unsupervised Event Log Abstraction Techniques in Process Mining
<p>This upload contains the event logs, generated by L-Sim, on which the experiments of the related paper were performed.</p><p>The related paper is accepted in the journal Information Systems.</p>
(Un)Fair Process Mining Event Logs
<p><strong>License: </strong>CC-BY-4.0</p> <p><strong>Event Logs:</strong></p> <p>We introduce a set of 12 distinct event logs, three for each of the four domains: hiring, healthcare, lending, and renting. These event logs have been carefully curated and simulated, each containing 10,000 cases, thereby providing an extensive resource for researchers focusing on fairness in process mining.</p> <p>In each of these domains, the three event logs represent varying degrees of discrimination, offering researchers an opportunity to explore the nuances and complexities that arise in diverse real-world scenarios. By presenting each log with a thorough description of the inherent processes and their respective attributes, we aim to provide a robust groundwork for understanding the potential sources of discrimination and addressing fairness in process mining.</p> <p>We have ensured that all the event logs are provided in the eXtensible Event Stream (XES) standard format. This adherence to a recognized standard not only ensures broad compatibility but also facilitates interoperability across a variety of process mining tools. By choosing this common format, we aim to encourage and simplify the utilization of these logs for researchers across different platforms.</p> <p><strong>* Hiring</strong></p> <p>The data describes a multifaceted recruitment process with diverse application pathways ranging from minimal processing to extensive multi-step procedures. The variability of these routes, largely dependent on numerous determinants, yields a spectrum of outcomes from instant rejection to successful job offers.</p> <p>The logs include attributes such as age, citizenship, German proficiency, gender, religion, and years of education. While these attributes may inform candidate profiles, their misuse could engender discrimination. Variables like age and education may signify experience and skills, citizenship and German language may address job logistics, but these should not unjustly eliminate applicants. Gender and religion, unrelated to job performance, must not sway hiring. Therefore, the use of these attributes must uphold fairness, avoiding any potential bias.</p> <p><strong>* Hospital</strong></p> <p>The data depicts a hospital treatment process that commences with registration at an Emergency Room or Family Department and advances through stages of examination, diagnosis, and treatment. Notably, unsuccessful treatments often entail repetitive diagnostic and treatment cycles, underscoring the iterative nature of healthcare provision.</p> <p>The logs incorporate patient attributes such as age, underlying condition, citizenship, German language proficiency, gender, and private insurance. These attributes, influencing the treatment process, may unveil potential discrimination. Factors like age and condition might affect case complexity and treatment path, while citizenship may highlight healthcare access disparities. German proficiency can impact provider-patient communication, thus affecting care quality. Gender could spotlight potential health disparities, while insurance status might indicate socio-economic influences on care quality or timeliness. Therefore, a comprehensive examination of these attributes vis-a-vis the treatment process could shed light on potential biases or disparities, fostering fairness in healthcare delivery.</p> <p><strong>* Lending</strong></p> <p>This data illustrates the steps within a loan application process. From an initial appointment request, the process navigates various stages, including information verification and underwriting, culminating in loan approval or denial. Additional steps may be required, such as co-signer enlistment or collateral assessment. Some cases experience outright appointment denial, indicating the process's variability, reflecting applicants' differing credit situations.</p> <p>The logs' attributes can aid in identifying influences on outcomes and detecting discrimination. Personal characteristics ('age', 'citizen', 'German speaking', and 'gender') and socio-economic indicators ('YearsOfEducation' and 'CreditScore') can impact the process. While 'yearsOfEducation' and 'CreditScore' can validly inform creditworthiness, 'age', 'citizen', 'language ability', and 'gender' should not bias loan decisions, ensuring these attributes are used responsibly fosters equitable loan processes.</p> <p><strong>* Renting</strong></p> <p>The data represents a rental process. It begins with a prospective tenant applying to view a property. Subsequent steps include an initial screening phase, viewing, decision-making, and a potential extensive screening. The process ends with the acceptance or rejection of the prospective tenant. In some cases, a tenant may apply for viewing but be rejected without the viewing occurring.</p> <p>The logs contain attributes that can shed light on potential biases in the process. 'Age', 'citizen', 'German speaking', 'gender', 'religious affiliation', and 'yearsOfEducation' might influence the rental process, leading to potential discrimination. While some attributes may provide useful insights into a potential tenant's reliability, misuse could result in discrimination. Thus, fairness must be observed in utilizing these attributes to avoid potential biases and ensure equitable treatment.</p>
Process Mining from Information-SeekingConversations
<p>This deposit contains the results from the paper "Process Mining from Information-Seeking Conversations" submitted to WCCI 2020. The results are in .xes format, and ready to be analyzed in a process mining tool. </p>
A Systematic Approach for Performance Assessment Using Process Mining
<p>The datasets include the detailed results of the performance analysis of the Posidonia Operations case study:</p> <ul> <li>Parser scenario (PosidoniaParserResults-2017)</li> <li>CEP scenario (PosidoniaCEPresults-2017)</li> </ul>
Identification of processes in Cu-ore heap leaching using Cu isotopes and leachate chemistry at Tschudi mine, northern Namibia - Supplementary data
<p>This is a supplementary dataset to the paper:</p> <p>Sracek O., Ettler V., Mihaljevič M., Kříbek B., Mapani B., Penížek V., Zádorová T., Vaněk A. (2024): Identification of processes in Cu-ore heap leaching using Cu isotopes and leachate chemistry at Tschudi mine, northern Namibia. <em>Hydrometallurgy</em> <strong>228</strong>, 106356.</p> <p>This research was supported by the Johannes Amos Comenius Programme (OP JAC), project No. CZ.02.01.01/00/22_008/0004605, Natural and anthropogenic georisks. The dataset is published under the Creative Commons Attribution 4.0 International License (CC-BY-4.0). This license allows others to distribute, remix, adapt, and build upon the dataset for any purpose, even commercially, as long as they give appropriate credit to the original creator(s).</p>
(Un)Fair Process Mining Event Logs (Converted to OCEL)
<p><strong>Converted to OCEL 1.0 JSONOCEL and OCEL 2.0 XML from traditional event logs available at: </strong><a href="https://zenodo.org/records/8059489" target="_new" rel="noopener">Zenodo - Record 8059489</a>.</p> <p><strong>Object Types:</strong> <code>{ Person }</code></p> <p><strong>Person-level Attributes:</strong></p> <ul> <li><strong>(int) overallProtected:</strong> An attribute (0/1) indicating whether the person has experienced discrimination. <em>(Note: If you're developing a fairness assessment algorithm, only use this attribute in the testing phase!)</em></li> <li><strong>(int) sumBoolDiscrFactors:</strong> Counts the number of possible discrimination factors that apply to the person.</li> <li><strong>(int) reworkedActivities:</strong> The total amount of rework involved in the person’s processing.</li> <li><strong>(float) throughputTime:</strong> The total processing time for a person.</li> <li><strong>(int) numOcc_ACTIVITY:</strong> Counts the number of times an activity occurs in the person’s lifecycle.</li> </ul> <p><strong>Event-level Attributes:</strong></p> <ul> <li><strong>resource:</strong> The resource involved in processing a given person.</li> </ul> <p> </p> <p><strong>* Hiring</strong></p> <p>The data describes a multifaceted recruitment process with diverse application pathways ranging from minimal processing to extensive multi-step procedures. The variability of these routes, largely dependent on numerous determinants, yields a spectrum of outcomes from instant rejection to successful job offers.</p> <p>The logs include attributes such as age, citizenship, German proficiency, gender, religion, and years of education. While these attributes may inform candidate profiles, their misuse could engender discrimination. Variables like age and education may signify experience and skills, citizenship and German language may address job logistics, but these should not unjustly eliminate applicants. Gender and religion, unrelated to job performance, must not sway hiring. Therefore, the use of these attributes must uphold fairness, avoiding any potential bias.</p> <p><strong>* Hospital</strong></p> <p>The data depicts a hospital treatment process that commences with registration at an Emergency Room or Family Department and advances through stages of examination, diagnosis, and treatment. Notably, unsuccessful treatments often entail repetitive diagnostic and treatment cycles, underscoring the iterative nature of healthcare provision.</p> <p>The logs incorporate patient attributes such as age, underlying condition, citizenship, German language proficiency, gender, and private insurance. These attributes, influencing the treatment process, may unveil potential discrimination. Factors like age and condition might affect case complexity and treatment path, while citizenship may highlight healthcare access disparities. German proficiency can impact provider-patient communication, thus affecting care quality. Gender could spotlight potential health disparities, while insurance status might indicate socio-economic influences on care quality or timeliness. Therefore, a comprehensive examination of these attributes vis-a-vis the treatment process could shed light on potential biases or disparities, fostering fairness in healthcare delivery.</p> <p><strong>* Lending</strong></p> <p>This data illustrates the steps within a loan application process. From an initial appointment request, the process navigates various stages, including information verification and underwriting, culminating in loan approval or denial. Additional steps may be required, such as co-signer enlistment or collateral assessment. Some cases experience outright appointment denial, indicating the process's variability, reflecting applicants' differing credit situations.</p> <p>The logs' attributes can aid in identifying influences on outcomes and detecting discrimination. Personal characteristics ('age', 'citizen', 'German speaking', and 'gender') and socio-economic indicators ('YearsOfEducation' and 'CreditScore') can impact the process. While 'yearsOfEducation' and 'CreditScore' can validly inform creditworthiness, 'age', 'citizen', 'language ability', and 'gender' should not bias loan decisions, ensuring these attributes are used responsibly fosters equitable loan processes.</p> <p><strong>* Renting</strong></p> <p>The data represents a rental process. It begins with a prospective tenant applying to view a property. Subsequent steps include an initial screening phase, viewing, decision-making, and a potential extensive screening. The process ends with the acceptance or rejection of the prospective tenant. In some cases, a tenant may apply for viewing but be rejected without the viewing occurring.</p> <p>The logs contain attributes that can shed light on potential biases in the process. 'Age', 'citizen', 'German speaking', 'gender', 'religious affiliation', and 'yearsOfEducation' might influence the rental process, leading to potential discrimination. While some attributes may provide useful insights into a potential tenant's reliability, misuse could result in discrimination. Thus, fairness must be observed in utilizing these attributes to avoid potential biases and ensure equitable treatment.</p>
Dataset: Process Mining for Reliability Modeling of Smart Manufacturing Systems with Reduced Data Requirements
<p>Operational state logs from the Industry 4.0 Lab, University of Southern Denmark.</p> <p>"I4.0Lab_state_log.csv" -> without failures</p> <p>I4.0Lab_state_log_failures.csv -> with failures</p>
Dataset and Code for "Mining and Predicting Micro-Process Patterns of Issue Resolution for Open Source Software Projects"
<p>Dataset and Code for "Mining and Predicting Micro-Process Patterns of Issue Resolution for Open Source Software Projects" with README included</p>
Process Mining from Information-Seeking Conversations
<p>Synthetic dataset and event logs for the paper 'Process Mining from Information-Seeking Conversations' submitted to WCCI 2020. </p>
Designing a User Interface to Explore Collections of Directly-Follows Graphs for Process Mining Analysis - Supplementary Material
<p>Supplementary Material for paper: "Designing a User Interface to Explore Collections of Directly-Follows Graphs for Process Mining Analysis", submitted to BPMDS 2024</p>
A Dataset for Business Process Deviance Mining with Sequential and Declarative Patterns
<p>This repository contains the data for running the experiments in the journal paper "Business Process Deviance Mining with Sequential and Declarative Patterns". The relative source code can be found at this <a href="https://github.com/ivanDonadello/Deviance_mining_sequential_declarative_patterns" target="_blank" rel="noopener">GitHub repository</a>.</p>
Differentially Private Release of Event Logs for Process Mining
<p>The anonymized event logs of the experiments in the paper "Differentially Private Release of Event Logs for Process Mining"</p>
High-Utility Anonymization of Event Logs for Process Mining: Supplementary Material
<p>In this document, we list the selected event logs, their characteristics, and their descriptive statistics. Also, attached to this document the anonymized event logs.</p>
Gender equality and hiring process in oil mining companies: A case of Lokichar in Turkana, Kenya
<p><span>This paper centers on the findings of a study done by the authors of this paper in 2019 titled <em>Change in Gender Roles as a Factor in Gender Participation and Empowerment in the Oil Mining Industry: A Case of Lokichar, Kenya</em>. One of the objectives; determining whether the oil mining companies promotes gender equity in Lokichar is explored. The null hypothesis testing on whether </span>there is no relationship between equal hiring and equal opportunity for men <span>and women to work in mining activities is discussed</span>. The data was collected from a sample of three hundred (300) respondents selected through systematic random and purposive sampling methods. The studies target group was the active labour force aged between 15 to 64 years. Those who retired from work were also targeted. Two levels of analysis were adopted: Univariate and Bivariate. Chi-square was used to test the hypotheses. Major finding indicates that there is a relationship between equal hiring and equal opportunity for men and women to work in mining activities. The paper recommends that issues like compensation, decision making, royalty sharing, power relations are well spelt in policies like the Mining Bill (2014) to ensure gender mainstreaming and no ambiguity in interpretation.</p>
Dataset used in Can process mining help in anomaly-based intrusion detection?
<p>This is the dataset used in the paper Can process mining help in anomaly-based intrusion detection?</p>
Appendix to "Process Mining Pipelines with Controlled Sharing of Data and Algorithms"
<p><strong>Abstract: </strong>Process mining leverages execution traces within an organisation's IT systems to gain insights into its processes. Despite being a mature discipline in academia and industry, setting up process mining pipelines is still a complex task and involves programming, manual steps, and considerations of privacy and intellectual property.</p> <p>This paper introduces a platform based on a distributed architecture that helps define, deploy, and execute process mining pipelines across organisations. The requirements for this distributed architecture and platform are derived from a set of process mining scenarios, whose relevance is validated through a survey.</p> <p>Furthermore, this paper introduces a prototype for an initial version of the platform, demonstrating feasibility and supporting the specified requirements. This development is a major step in advancing process mining, offering simpler and more efficient ways of implementing and managing complex process mining pipelines on a larger scale.</p> <p><strong>Description: </strong>This dataset presents the support for non-functional requirements identified in the paper "Process Mining Pipelines with Controlled Sharing of Data and Algorithms" by existing process mining platforms.</p> <p><strong>Legend:</strong> Green cells indicate complete fulfilment. Yellow indicates partial fulfilment. Blue cells indicate uncertain fulfilment. Red indicates no fulfilmnet.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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