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Dataset results
5 results for “XES”
XES Logistics And Transportation Dataset - Small (~1 Day)
<p><strong>Vienna Tram Line 71 (days 2022-12-06 to 2022-12-07): Delays, Weather, Trafic, Construction Sites</strong></p> <p>This dataset has been created by researchers from the Technical University of Munich, Chair for Information Systems and Business Process Management (i17), Boltzmannstraße 3, 85748 Garching b. München. The dataset has been created through <a href="http://cpee.org">https://cpee.org</a>.</p> <p>The data set contains raw data and refined and aggregated data in the XES SensorStream format <a href="https://arxiv.org/abs/2206.11392">https://arxiv.org/abs/2206.11392</a>.</p> <p>The dataset contains data from the following sources:</p> <ol> <li>Tram line delay data & construction sites in the vicinity of the tram line stops: <a href="https://digitales.wien.gv.at/open-data/">https://digitales.wien.gv.at/open-data/</a>. All data collected from this service can, to the best of our knowledge, be freely distributed and used for all purposes (e.g., analysis).</li> <li>Traffic data in the vicinity of the tram line stops: <a href="https://developer.tomtom.com/store/maps-api/">https://developer.tomtom.com/store/maps-api/</a>. We use data gathered through the TomTom "Freemium" plan, which is covered by <a href="https://developer.tomtom.com/terms-and-conditions/">https://developer.tomtom.com/terms-and-conditions/</a>. To the best of our knowledge the gathered traffic flow data can be freely distributed and used for analysis purposes, as TomTom invites researchers (and business) to explore new applications.</li> <li>Weather data: <a href="https://openweathermap.org/">https://openweathermap.org/</a>. We collect data based on the "Free" plan, which is covered by a CC BY-SA 4.0 license (https://openweathermap.org/full-price#licenses).</li> </ol> <p> </p>
XES Chess Pieces Production
<p><strong>Nine Rooks (2023-04-28): Lathe Machining (Aluminium), Robotic Handling, Close To Production Measurement</strong></p> <p>This dataset has been created by researchers from the Technical University of Munich, Chair for Information Systems and Business Process Management (i17), Boltzmannstraße 3, 85748 Garching b. München. The dataset has been created through <a href="http://cpee.org">https://cpee.org</a>.</p> <p>The data set contains raw data and refined and aggregated data in the XES SensorStream format <a href="https://arxiv.org/abs/2206.11392">https://arxiv.org/abs/2206.11392</a>.</p> <p>The dataset contains data from the following sources:</p> <ol> <li>EMCO MT45 Lathe: standard internal sensors + additional custom power measurement (better quality than internal sensors). All data collected from this service can, to the best of our knowledge, be freely distributed and used for all purposes (e.g., analysis).</li> <li>Keyence LS-7000 High-speed, High-accuracy Optical Digital Micrometer: the part is moved through the measurement beam. On data collection we restricted the accuracy to 0.01 mm.</li> <li>ABB IRB-2600 Industrial Robot: movement coordinates and pneumatic valve states (gripper) are collected.</li> </ol> <p>The data is collected for nine manufactured parts: some parts are good, other parts are wrapped in chips from the turning process (see picture).</p>
Synthetic XES Event Log of Malignant Melanoma Treatment
<p>The synthetic event log described in this document consists of 25,000 traces, generated using the process model outlined in Geyer et al. (2024) [1] and the DALG tool [2]. This event log simulates the treatment process of malignant melanoma patients, adhering to clinical guidelines. Each trace in the log represents a unique patient journey through various stages of melanoma treatment, providing detailed insights into decision points, treatments, and outcomes.</p> <p>The DALG tool [2] was employed to generate this data-aware event log, ensuring realistic data distribution and variability. </p> <p> </p> <p>DALG: <a href="https://github.com/DavidJilg/DALG">https://github.com/DavidJilg/DALG</a></p> <p> </p> <p>[1] Geyer, T., Grüger, J., & Kuhn, M. (2024). Clinical Guideline-based Model for the Treatment of Malignant Melanoma (Data Petri Net) (1.0). Zenodo. <a href="https://doi.org/10.5281/zenodo.10785431">https://doi.org/10.5281/zenodo.10785431</a></p> <p>[2] Jilg, D., Grüger, J., Geyer, T., Bergmann, R.: DALG: the data aware event log generator. In: BPM 2023 - Demos & Resources. CEUR Workshop Proceedings, vol. 3469, pp. 142–146. CEUR-WS.org (2023)</p>
Simulated XES event log of a marketing campaign system
<p>We relied on the interview-driven methodology defined in our previous work \cite{benvenuti2022}, which allowed us to specify various simulation scenarios to frame the boundaries of all possible pipeline executions.</p> <p>Then, we generated this simulated event logs in the traditional XES format using the Simio (https://www.simio.com/), obtaining 10,000 execution traces compliant with the simulation scenarios. In the picture it is shown the Directly-Follow Graph (DFG) representing the pipeline structure, discovered by feeding a process discovery tool (https://fluxicon.com/disco/) with the simulated event log.</p> <p>The pipeline is triggered when the system receives a request to model a new marketing campaign or to report on how an already existing one is performing. In both cases, the first two steps of the pipeline are querying the required data and to apply specific transformations on it. Then, if the request was for a report there is the need of merging the queried data, while for the request of a model an algorithm to compute it is launched. Next, if the request was for a model, the result needs to be stored.<br> Finally, the pipeline ends with either the report or the model being generated.</p>
HERFD-XANES and vtc-XES data acquired during CO2RR conditions for Ni single atom catalysts
<p>HERFD-XANES and vtc-XES data (.h5 files) relative to the pubblication: "Unveiling the Adsorbate Configurations in Ni Single Atom Catalysts during CO2 Electrocatalytic reduction using Operando XAS, XES and Machine Learning".</p>
ScienceDex guides
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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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International Brain Laboratory public data
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OpenNeuro
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