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13 results for “Process Log”
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>
CCE LTER process cruise, in the California Current region, event log records including date, time, position and activity for use in post-cruise data integration based on co-sampling indexes. From 2006 to 2019 CCE LTER used a locally developed event logging system. During P2107, CCE LTER started to utilize the R2R Event Logger on UNOL ships, 2006 - 2024 (ongoing).
The event logger program developed and maintained by the California Cooperative Oceanic Fisheries Investigations, SIO, program is used aboard CCE LTER process cruises to create indexes with temporal, spatial and activity information for post-cruise data integration. The event log is configured aboard the ship for the recording of sampling events by both ship crew personnel on the bridge, and research personnel in the lab. The event log is processed post-cruise to correct for various errors.
Processed Datasets - Imputation in Well Log Data: A Benchmark
<p>Imputation of well log data is a common task in the field. However a quick review of the literature reveals a lack of padronization when evaluating methods for the problem. The goal of the benchmark is to introduce a standard evaluation protocol to any imputation method for well log data. </p> <p>In the proposed benchmark, three public datasets are used:</p> <ul> <li><strong>Geolink:</strong> The Geolink Dataset is another public dataset of wells in the Norwegian offshore. The data is provided by the company of the same name, <a href="https://www.geolink-s2.com/" target="_blank" rel="noopener">GEOLINK</a> and follows the NOLD 2.0 license. <br>This dataset contains a total of 223 wells. It also has lithology labels for the wells with a total of 36 lithology classes. [<a href="https://drive.google.com/drive/folders/1EgDN57LDuvlZAwr5-eHWB5CTJ7K9HpDP" target="_blank" rel="noopener">download original</a>]</li> <li><strong>Taranaki Basin:</strong> The Taranaki Basin Dataset is a curated set of wells and a convenient option for experimentation especially due to it is ease of accessibility and use.<br>This collection, under the CDLA-Sharing-1.0 license, contains well logs extracted from the <a href="https://geodata.nzpam.govt.nz/" target="_blank" rel="noopener">New Zealand Petroleum & Minerals Online Exploration Database</a> and <a href="http://pet.gns.cri.nz/" target="_blank" rel="noopener">Petlab</a>.<br>There are a total of 407 wells, of which 289 are onshore and 118 are offshore exploration and production wells. [<a href="https://developer.ibm.com/exchanges/data/all/taranaki-basin-curated-well-logs/" target="_blank" rel="noopener">download original</a>]</li> <li><strong>Teapot Dome:</strong> The Teapot Dome dataset is provided by the Rocky Mountain Oilfield Testing Center (RMOTC) and the US Department of Energy.<br>It contains different types of data related to the Teapot Dome oil field, such as 2D and 3D seismic data, well logs, and GIS data. The data is licensed under the Creative Commons 4.0 license. <br>In total, the dataset has 1,179 wells with available logs. The number of available logs varies across wells. There are only 91 wells with the gamma ray, bulk density, and neutron porosity logs, while only three wells have the complete basic suite. [<a href="http://s3.amazonaws.com/open.source.geoscience/open_data/teapot/rmotc.tar" target="_blank" rel="noopener">direct download</a>]</li> </ul> <p>Here you can download all three datasets already preprocessed to be used with our implementation, found <a href="https://github.com/uai-ufmg/well-log-imputation" target="_blank" rel="noopener">here</a>.</p> <p> </p> <h3>File Description:</h3> <p>There are six files for each fold partition for each dataset.</p> <ul> <li><code><em>datasetname_fold_k_well_log_metadata_train.json </em></code>: JSON file with general information of the slices of <strong>training </strong>partition of the fold <strong>k</strong>. Contains total number of slices and the number of slices per well.<em> </em></li> <li><em><code>datasetname_fold_k_well_log_metadata_val.json</code> </em>: JSON file with general information of the slices of <strong>validation </strong>partition of the fold <strong>k</strong>. Contains total number of slices and the number of slices per well. </li> <li><em><code>datasetname_fold_k_well_log_slices_train.npy</code>: </em>.npy (numpy) file ready to be loaded with the slices for <strong>training </strong>of the fold <strong>k </strong>already processed. When loaded<em> </em>should have shape of<em> (total_slices, 256, number_of_logs)</em></li> <li><em><code>datasetname_fold_k_well_log_slices_val.npy</code> </em>: .npy (numpy) file ready to be loaded with the slices for <strong>validation </strong>of the fold <strong>k </strong>already processed.</li> <li><em><code>datasetname_fold_k_well_log_slices_meta_train.json</code> : </em>JSON file with the slices info for all slices in the <strong>training </strong>partition of the fold <strong>k</strong>. For each slice, 7 data points are provided, the last four are discarded (it would contain other information that was not used). The first three are in order the: origin well name, the starting position in that well, and the end position of the slice in that well.</li> <li><em><code>datasetname_fold_k_well_log_slices_meta_val.json</code> </em>: JSON file with the slices info for all slices in the <strong>validation </strong>partition of the fold <strong>k</strong>.</li> </ul>
Compositional discovery of architecture-aware and sound process models from event logs of multi-agent systems: experimental data.
<p>This repository contains the experimental data used for the evaluation of the compositional approach to the discovery of process models from event logs of multi-agent systems, where agents interact according to specific patterns of synchronous and asynchronous interactions.</p> <p>According to the experiment plan, there is the folder for each interface pattern containing:</p> <ol> <li>The reference model (Petri net encoded in PNML-file)</li> <li>The event log obtained by simulating the behavior of the reference model (XES-file)</li> <li>The model discovered directly from the generated event log (Petri net encoded in PNML-file)</li> <li>The model discovered by composing the agent model w.r.t. the interface pattern (Petri net encoded in PNML-file)</li> </ol>
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>
Process Models obtained from event logs with with different information-preserving abstractions
<p>This dataset contains results of the experiment to analyze information preservation and recovery by different event log abstractions in process mining described in: Sander J.J. Leemans, Dirk Fahland "Information-Preserving Abstractions of Event Data in Process Mining"<br> Knowledge and Information Systems, ISSN: 0219-1377 (Print) 0219-3116 (Online), accepted May 2019</p> <p>The experiment results were obtained with: https://doi.org/10.5281/zenodo.3243981</p>
HYPERCOG_Process data_Welding log_2020-07-30
<p>The HyperCOG project addresses the full digital transformation of process industry through an innovative Industrial Cyber-Physical System and Data Analytics. It is based on advanced technologies that enable the development of a hyperconnected network of digital nodes. The nodes can catch outstanding streams of data in real-time, which together with the high computing capabilities, provide sensing, knowledge and cognitive reasoning, making companies robust in the face of variant scenarios. The breaking-edge system proposed in this work is validated on productivity, environmental and replicability aspects on three use cases of three di_erent sectors: steel, cement and chemical.</p> <p>Participating entities: LORTEK. The data was gathered and used in the proof of concept of the architecture introduced in the paper in the way that is described in it.</p> <p><strong>Dataset 1: Welding process data (xlsx files)</strong></p> <p>Real time data of the process of a welding cell on Excel sheets. The data is obtained at a frequency of 100 Hz and variables of voltage, current, temperature, gas flux, etc. are registered in the Excel file by rows.</p> <p><strong>Dataset 2: Temperature and movement of the piece constructed (zip files)</strong></p> <p>Data of temperatures obtained by thermocouple sensors and distortion of the structure measured by a laser sensor. The zip files contain coma separated values of 8 thermocouples welded to the substrate of the piece constructed by the welding cell. The reading of a laser sensor is also recorded along the x coordinate of the movement of the robot for synchronization purposes.</p> <p>The article corresponding to these datasets are available in open access in </p> <pre><a href="https://doi.org/10.5281/zenodo.5533904">https://doi.org/10.5281/zenodo.5533904</a></pre>
(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>
Conductivity Temperature Depth (CTD) Log of CTD casts from CCE LTER process cruises in the CCE region, 2006 - 2019 (ongoing).
Individual casts of Conductivity, Temperature and Depth (CTD) are logged on CCE Process cruises (since 2006, ongoing) in the Southern California region. The log includes time, location, number of bottles, cast and event numbers and other information about CTD casts.
Simulated Object-Centric Event Logs (OCEL 2.0) for Order-to-Cash, Procure-to-Pay, Hiring, and Hospital Patient Lifecycle Processes
<p>This dataset contains simulated object-centric event logs for four distinct business processes: <strong>Order-to-Cash (O2C)</strong>, <strong>Procure-to-Pay (P2P)</strong>, <strong>Hiring</strong>, and <strong>Hospital Patient Lifecycle</strong>. Each process is designed to reflect realistic workflows, encompassing multiple object types and capturing key activities, decision points, and process dynamics. The dataset is aimed at providing a rich source of data for process mining, analysis, and modeling activities.</p> <p>1. <strong>Order-to-Cash (O2C)</strong>:<br> The O2C process simulates an end-to-end business flow starting from customer order placement to payment receipt. It includes diverse activities such as order approval, fulfillment, invoice generation, and payment processing, involving object types like Customers, Orders, Products, and Invoices. The dataset captures variability through random decisions, synchronization between departments, and workarounds in credit checks and inventory adjustments. Attributes such as customer tiers, order values, and shipment statuses add further depth, allowing for detailed analysis of this complex process.</p> <p>2. <strong>Procure-to-Pay (P2P)</strong>:<br> The P2P process simulates the procurement lifecycle, from requisition creation to payment of suppliers. Key activities include purchase order creation, three-way matching, goods receipt, and payment processing. The event log records object types such as Purchase Requisitions, Purchase Orders, Suppliers, and Invoices. Variability is introduced through approval decisions, batching, and potential mismatches in the matching process. The dataset represents the inherent complexities of real-world procurement operations, including batching and synchronization issues between different process stages.</p> <p>3. <strong>Hiring Process</strong>:<br> The hiring process log tracks the recruitment lifecycle, from job requisition creation to onboarding. It includes object types like Candidates, Job Requisitions, Recruiters, and Interviewers. The process covers activities such as resume screening, interviews, assessments, and offer management. Variability in the hiring process is introduced through random delays, candidate decisions, and background check durations. Batching occurs in stages like resume screening and onboarding, while synchronization challenges arise during interview scheduling.</p> <p>4. <strong>Hospital Patient Lifecycle</strong>:<br> This log represents the lifecycle of patients within a hospital, capturing interactions with multiple resources such as physicians, beds, and medical equipment. The process begins with pre-admission activities, followed by diagnosis, treatment, and discharge. The dataset includes object types like Patients, Physicians, and Medical Equipment, with attributes related to patient demographics and event severity. The process reflects the dynamic nature of hospital operations, including synchronization of resources and the occurrence of workarounds in case of delays or resource unavailability.</p> <p>Each process simulation captures high variability, synchronization issues, and batching, making this dataset suitable for analyzing real-world operational challenges. The logs provide a comprehensive view of complex workflows, supporting advanced analysis, including object-centric process mining.</p> <p>This description will provide the necessary details about the dataset, highlighting its structure, purpose, and potential uses for researchers and process analysts.</p> <p>Object-centric event logs conceived and simulated by the <strong>o1-preview-2024-09-12</strong> LRM, using the https://github.com/fit-alessandro-berti/llm-ocel-simulator project.</p> <p> </p> <p> </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>
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>
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OpenNeuro
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