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5 results for “Business Process Management”
Model Collection of the Business Process Management Academic Initiative
<p>This model collection contains 29,810 models. The collection originates from the BPM Academic Initiative (BPMAI). In 2012, the initial members of this initiative were the following institutions represented by the corresponding professors: Mathias Weske (HPI, University of Potsdam), Marlon Dumas (University of Tartu), Marcello La Rosa (University of Melbourne), Jan Mendling (WU Vienna), Hajo A. Reijers (Utrecht University), Michael Rosemann (Queensland University of Technology, Australia), Jan Recker (University of Cologne, Germany), Wil van der Aalst (RWTH Aachen, Germany), Michael zur Mühlen (Stevens Institute of Technology, Hoboken, NJ) and Frank Leymann (University of Stuttgart), respectively, complemented by Dr. Gero Decker (Signavio GmbH, Berlin). The vendor Signavio provides a <a href="http://www.signavio.com/en/academic.html">free workspace</a> to the members of the BPM Academic Initiative and the models being created are made available for reseach purposes on the Creative Commons licence.</p> <p>The BPM Academic Initiative collection comprises tens of thousands of models, of various process modeling languages, and size. Models of the collection are available in several revisions, which opens a new perspective in researching the way people model.<br> The BPMAI collection is shared in a JSON format. Tools have been developed at HPI, University of Potsdam for efficiently processing these files. See the <a href="https://github.com/tobiashoppe/promnicat">PromniCat project</a>. Note that this project is no longer further developed, so you have to work with the information available there. Various analysis techniques can be applied for these models using the <a href="https://code.google.com/archive/p/jbpt/">jBPT library</a> or <a href="https://code.google.com/archive/p/apromore/">Apromore</a>.</p>
Figure 3. Traditional Approach and Process Management System Approach – Effort Percentage Comparison-Business Process Management – A Traditional Approach versus a Knowledge Based Approach
<p>Comparing the results obtained in using the two approaches (Figure 3), it is possible to note<br> a significant reduction in terms of both effort and working hours in correspondence of design and<br> development phases.</p>
WONDERBREAD: A Benchmark + Dataset for Business Process Management (BPM) Tasks
<p><strong>Paper:</strong> <a href="https://arxiv.org/abs/2406.13264">WONDERBREAD: A Benchmark for Evaluating Multimodal Foundation Models on Business Process Management Tasks</a></p> <h2><strong>Background</strong></h2> <p>The <em>WONDERBREAD</em> dataset contains <strong>2,928 human demonstrations</strong> of <strong>598 web navigation workflows</strong> across <strong>6 types of BPM tasks</strong>. These tasks measure the ability of a model to generate accurate documentation, assist in knowledge transfer, and improve the efficiency of workflows.</p> <p>Please see our website for more details: <a href="https://wonderbread.stanford.edu/">https://wonderbread.stanford.edu/</a></p> <h2><strong>Quick Start</strong></h2> <p>To start, download <strong>debug_demos.zip</strong> (1 GB). It contains a subset of <strong>24 demonstrations</strong> which can give you a sense of how the dataset is structured.</p> <p>To reproduce the paper, download <strong>gold_demos.zip</strong> (33 GB). It contains <strong>724 demonstrations</strong> corresponding to the 162 "Gold" tasks which were used for all the evaluations in the original paper.</p> <p>To obtain the full dataset, download <strong>demos.zip</strong> (133 GB). This contains all <strong>2,928 demonstrations</strong> and can be used for training, fine-tuning, and evaluating models.</p> <h2><strong>Dataset Structure</strong></h2> <p>The dataset contains several files, defined below.</p> <ol> <li><strong>Raw Data</strong><em> (useful for training/fine-tuning/evaluation)</em> <ol> <li><strong>debug_demos.zip </strong>(1 GB)<strong> -- </strong>a subset of only 24 demonstrations taken from the full dataset. Useful to get a sense of the dataset and for debugging.</li> <li><strong>gold_demos.zip</strong> (22 GB) -- a subset of only 724 demonstrations corresopnding to the 162 "Gold" tasks. This is the dataset that was used for all evaluations in the original <em>WONDERBREAD</em> paper.</li> <li><strong>demos.zip</strong> (133 GB) -- all 2,928 demonstrations across 598 tasks. Useful for training your own models.</li> </ol> </li> <li><strong>Modality-Specific Subsets of Raw Data </strong><em>(useful for specific types of training/fine-tuning/evaluation)</em><br> <ol> <li><strong>All Demos</strong> <ol> <li><strong>demos_sop_only.zip</strong> (4 MB)-- only the SOP <code>.txt</code> files for all 2,928 demonstrations</li> <li><strong>demos_sop_and_trace_only.zip</strong> (770 MB)-- only the SOP <code>.txt</code> files and action trace <code>.json</code> files for all 2,928 demonstrations</li> <li><strong>demos_sop_and_trace_and_screenshots_only.zip</strong> (22 GB)-- only the SOP <code>.txt</code> files and action trace <code>.json</code> files and screenshot images for all 2,928 demonstrations</li> </ol> </li> <li><strong>"Gold" Demos</strong> <ol> <li><strong>gold_demos_sop_only.zip</strong> (1 MB)-- only the SOP <code>.txt</code> files for the 724 demonstrations in the "Gold" tasks.</li> <li><strong>gold_demos_sop_and_trace_only.zip</strong> (190 MB) -- only the SOP <code>.txt</code> files and action trace <code>.json</code> files for the 724 demonstrations in the "Gold" tasks.</li> <li><strong>gold_demos_sop_and_trace_and_screenshots_only.zip </strong>(6 GB) -- only the SOP <code>.txt</code> files and action trace <code>.json</code> files and screenshot images for the 724 demonstrations in the "Gold" tasks</li> </ol> </li> </ol> </li> <li><strong>Evaluation</strong><em> (useful for evaluation)</em> <ol> <li><strong>qa_dataset.csv -- </strong>contains all 120 questions and ground truth answers used in the "Knowlege Transfer" evaluation.<strong><br></strong></li> <li><strong>df_rankings.csv -- </strong>contains the rankings of all "Gold" tasks used in the "SOP Ranking" evaluation.<strong><br></strong></li> </ol> </li> <li><strong>Metadata</strong><em> (can be safely ignored)</em> <ol> <li><strong>Process Mining Task Demonstrations.xlsx --</strong> maps human annotators to specific demonstrations; also contains "Gold" task rankings used in the "SOP Ranking" evaluation.</li> <li><strong>metadata.json -- </strong>maps Google Drive URLs to Google Drive Folder IDs to demonstration names</li> <li><strong>df_valid.csv -- </strong>tracks assets associated with each demonstration</li> </ol> </li> </ol>
Search Protocol for "Conversational Systems for AI-Augmented Business Process Management"
<p>Results obtained from implementing the search protocol devised for the literature survey "<em>Conversational Systems for AI-Augmented Business Process Management</em>".</p> <p>The dataset comprises four spreadsheets, each corresponding to one of the four BPM areas identified in the paper, namely:</p> <ul> <li><em>Descriptive Process Analytics</em>;</li> <li><em>Predictive Process Analytics</em>;</li> <li><em>Prescriptive Process Optimization</em>;</li> <li><em>Augmented Process Execution</em>.</li> </ul> <p>Each spreadsheet consists of multiple sheets:</p> <ul> <li>The first four sheets document the papers collected from each data source (<em>Google Scholar</em>, <em>Scopus</em>, <em>ACM Digital Library</em>, <em>IEEE Xplore</em>) by applying the search strings defined in the paper.</li> <li>"All" reports all the papers obtained in the search.</li> <li>"All(-duplicates)" lists all publications, excluding duplicates.</li> <li>"Inclusion" applies the inclusion criteria defined in the paper to select the works considered in this survey.</li> <li>"Final" comprises the selected papers, representing the outcomes of the search protocol's application.</li> <li>"Results" provides statistical insights into the application of the search protocol for the specific BPM area under analysis."</li> </ul>
Business process management concept. Bibliographic collection from Web of Science (March 21, 2023).
<p>The search query: https://www.webofscience.com/wos/woscc/summary/f99d7b5d-a7bb-4c35-a8ba-d45042c4e0a9-7ab6bd8b/relevance/1. Contains 95 articles (adjusted after screening the relevance of titles, abstracts, keywords for the research purposes).</p>
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