Fishing Fort: A System for Graph Analytics with ML Prediction and Logic Deduction

<!DOCTYPE html> <html lang="en"> <head> <meta charset="utf-8"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <meta name="google" content="notranslate"> <meta http-equiv="Content-Language" content="en"> <meta name="viewport" content="width=device-width, initial-scale=1"> <meta name="csrf-token" content="8CLRz3Jb8DJA8zQMpKBJ9xFoSZAnMLXbPl3KBXhW"> <link rel="canonical" href="https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Tannen.6"> <meta name="DC.Publisher" content="Schloss Dagstuhl – Leibniz-Zentrum für Informatik" > <meta name="DC.Title" xml:lang="en" content="Fishing Fort: A System for Graph Analytics with ML Prediction and Logic Deduction" > <meta name="DC.Creator.PersonalName" content="Fan, Wenfei" > <meta name="DC.Creator.PersonalName" content="Liu, Shuhao" > <meta name="DC.Subject" content="graph analytics" > <meta name="DC.Subject" content="data cleaning" > <meta name="DC.Subject" content="association analysis" > <meta name="DC.Date.created" scheme="ISO8601" content="2024-06-07" > <meta name="DC.Date.issued" scheme="ISO8601" content="2024-06-07" > <meta name="DC.Date.modified" scheme="ISO8601" content="2024-06-07" > <meta name="DC.Type" content="InProceedings" > <meta name="DC.Format" scheme="IMT" content="application/pdf" > <meta name="DC.Identifier" scheme="urn:nbn:de" content="urn:nbn:de:0030-drops-201025" > <meta name="DC.Identifier" scheme="DOI" content="10.4230/OASIcs.Tannen.6" > <meta name="DC.Identifier" scheme="URL" content="https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Tannen.6" > <meta name="DC.Source" content="The Provenance of Elegance in Computation - Essays Dedicated to Val Tannen (2024)" > <meta name="DC.Source.URI" content="https://drops.dagstuhl.de/entities/volume/OASIcs-volume-119" > <meta name="DC.Language" scheme="ISO639-1" content="en" > <meta name="DC.Description" xml:lang="en" content="This paper reports Fishing Fort, a graph analytic system developed in response to the following questions. What practical value can we get out of graph analytics? How can we effectively deduce the value from a real-life graph? Where can we get clean graphs to make accurate analyses possible? To answer these questions, Fishing Fort advocates to unify logic deduction and ML prediction by proposing Graph Association Rules (GARs), a class of logic rules in which ML models can be embedded as predicates. It employs GARs to deduce graph associations, enrich graphs and clean graphs. It has been deployed in production lines and proven effective in online recommendation, drug discovery, credit risk assessment, battery manufacturing and cybersecurity, among other things." > <meta name="DC.Rights" scheme="DCTERMS.URI" content="https://creativecommons.org/licenses/by/4.0/legalcode" > <meta name="citation_conference_title" content="The Provenance of Elegance in Computation - Essays Dedicated to Val Tannen (2024)" > <meta name="citation_doi" content="10.4230/OASIcs.Tannen.6" > <meta name="citation_firstpage" content="6:1" > <meta name="citation_lastpage" content="6:18" > <meta name="citation_title" content="Fishing Fort: A System for Graph Analytics with ML Prediction and Logic Deduction" > <meta name="citation_language" content="en" > <meta name="citation_keyword" content="graph analytics" > <meta name="citation_keyword" content="data cleaning" > <meta name="citation_keyword" content="association analysis" > <meta name="citation_author" content="Fan, Wenfei" > <meta name="citation_author_institution" content="Shenzhen Institute of Computing Sciences, China; University of Edinburgh, UK; Beihang University, Beijing, China" > <meta name="citation_author" content="Liu, Shuhao" > <meta name="citation_author_institution" content="Shenzhen Institute of Computing Sciences, China" > <meta name="citation_date" content="2024" > <meta name="citation_keyword" xml:lang="en" content="graph analytics" > <meta name="citation_keyword" xml:lang="en" content="data cleaning" > <meta name="citation_keyword" xml:lang="en" content="association analysis" > <meta name="citation_abstract_html_url" content="https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Tannen.6" > <meta name="citation_pdf_url" content="https://drops.dagstuhl.de/storage/01oasics/oasics-vol119-tannens-festschrift/OASIcs.Tannen.6/OASIcs.Tannen.6.pdf" > <meta name="citation_reference" content="Serge Abiteboul, Richard Hull, and Victor Vianu. 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</div> </div> <hr> <h1>Fishing Fort: A System for Graph Analytics with ML Prediction and Logic Deduction</h1> <h3 class="mt-2 authors"> Authors <a href="#author-details" style="font-size: 0.8em; padding-right: 1em"><i class="bi bi-info-circle"></i></a> <span class="author"> <a href="https://drops.dagstuhl.de/search/documents?author=Fan, Wenfei" class="name">Wenfei Fan</a> <a href="https://orcid.org/0000-0001-5149-2656"><img class="orcid-logo" src="https://drops.dagstuhl.de/images/orcid.png" alt="ORCID-Logo"></a>, </span> <span class="author"> <a href="https://drops.dagstuhl.de/search/documents?author=Liu, Shuhao" class="name">Shuhao Liu</a> <a href="https://orcid.org/0000-0002-4892-0979"><img class="orcid-logo" src="https://drops.dagstuhl.de/images/orcid.png" alt="ORCID-Logo"></a> </span> </h3> <hr> <ul class="mt-4"> <li> <span style="margin-right: 10px; ">Part of:</span> <span style="white-space: nowrap"> <i class="bi bi-book-half"></i> Volume: </span> <a href="https://drops.dagstuhl.de/entities/volume/OASIcs-volume-119">The Provenance of Elegance in Computation - Essays Dedicated to Val Tannen (Tannen's Festschrift)</a> <br> <span style="margin-right: 10px; visibility: hidden;">Part of:</span> <span style="white-space: nowrap"> <i class="bi bi-journals"></i> Series: </span> <a href="https://drops.dagstuhl.de/entities/series/OASIcs">Open Access Series in Informatics (OASIcs)</a> <br> <span style="margin-right: 10px; visibility: hidden;">Part of:</span> <span style="white-space: nowrap"> <i class="bi bi-people"></i> Conference: </span> <a href="https://drops.dagstuhl.de/entities/conference/Tannen">Festschrift Val Tannen </a> </li> <li class="mt-2">License:   <a href="https://creativecommons.org/licenses/by/4.0/legalcode"> <img class="license-logo" src="https://drops.dagstuhl.de/images/cc-by.png" alt="CC-BY Logo"> Creative Commons Attribution 4.0 International license </a> </li> <li>Publication Date: 2024-06-07 </li> </ul> <hr> </section> <a class="fixed-pdf-button" href="https://drops.dagstuhl.de/storage/01oasics/oasics-vol119-tannens-festschrift/OASIcs.Tannen.6/OASIcs.Tannen.6.pdf"><i class="bi bi-file-earmark-pdf-fill" style="color:red"></i> PDF </a> <div class="row mt-2"> <div class="col-lg-4 mt-2"> <section class="thumbnail"> <a href="https://drops.dagstuhl.de/storage/01oasics/oasics-vol119-tannens-festschrift/OASIcs.Tannen.6/OASIcs.Tannen.6.pdf"><img src="https://drops.dagstuhl.de/storage/01oasics/oasics-vol119-tannens-festschrift/thumbnails/OASIcs.Tannen.6/OASIcs.Tannen.6.png" alt="Thumbnail PDF"></a> </section> <section class="files mt-5"> <h2>File</h2> <div class="content"> <div> <a href="https://drops.dagstuhl.de/storage/01oasics/oasics-vol119-tannens-festschrift/OASIcs.Tannen.6/OASIcs.Tannen.6.pdf"><i class="bi bi-file-earmark-pdf-fill" style="color:red"></i> OASIcs.Tannen.6.pdf</a> <ul> <li>Filesize: 2.47 MB</li> <li>18 pages</li> </ul> </div> </div> </section> <section class="identifiers mt-3"> <h2>Document Identifiers</h2> <div class="content"> <ul> <li><b>DOI:</b> <a href="https://doi.org/10.4230/OASIcs.Tannen.6">10.4230/OASIcs.Tannen.6</a></li> <li><b>URN:</b> <a href="https://nbn-resolving.org/process-urn-form?identifier=urn:nbn:de:0030-drops-201025&verb=FULL">urn:nbn:de:0030-drops-201025</a></li> </ul> </div> </section> <section class="authors mt-3" id="author-details"> <h2>Author Details</h2> <div class="author person-details"> <div> <i class="bi bi-person-fill"></i> <span class="name"><b>Wenfei Fan</b></span> <a href="https://orcid.org/0000-0001-5149-2656"><img class="orcid-logo" src="https://drops.dagstuhl.de/images/orcid.png" alt="ORCID-Logo"></a> <a href="https://homepages.inf.ed.ac.uk/wenfei/"><i class="bi bi-house"></i></a> <a href="mailto:wenfei@inf.ed.ac.uk"><i class="bi bi-envelope"></i></a> <a href="https://drops.dagstuhl.de/search/documents?author=Fan, Wenfei"><small><i class="bi bi-search"></i></small></a> </div> <ul> <li class="affiliation">Shenzhen Institute of Computing Sciences, China </li> <li class="affiliation">University of Edinburgh, UK </li> <li class="affiliation">Beihang University, Beijing, China </li> </ul> </div> <div class="author person-details"> <div> <i class="bi bi-person-fill"></i> <span class="name"><b>Shuhao Liu</b></span> <a href="https://orcid.org/0000-0002-4892-0979"><img class="orcid-logo" src="https://drops.dagstuhl.de/images/orcid.png" alt="ORCID-Logo"></a> <a href="https://shuhaoliu.github.io/"><i class="bi bi-house"></i></a> <a href="mailto:shuhao@sics.ac.cn"><i class="bi bi-envelope"></i></a> <a href="https://drops.dagstuhl.de/search/documents?author=Liu, Shuhao"><small><i class="bi bi-search"></i></small></a> </div> <ul> <li class="affiliation">Shenzhen Institute of Computing Sciences, China </li> </ul> </div> </section> <section class="acknowledgements mt-3"> <h2>Acknowledgements</h2> <div class="content"> The paper is a tribute to Professor Val Tannen. Val was a professor at UPenn when Fan was doing PhD there, and has been providing Fan with unfailing support ever since. </div> </section> </div> <div class="col-lg-8 mt-2"> <section class="cite-as mt-3"> <h2>Cite As <span class="btn btn-primary btn-xs" style="float: right" data-bs-toggle="modal" data-bs-target="#bibtex-modal">Get BibTex</span></h2> <div class="content"> Wenfei Fan and Shuhao Liu. Fishing Fort: A System for Graph Analytics with ML Prediction and Logic Deduction. In The Provenance of Elegance in Computation - Essays Dedicated to Val Tannen. Open Access Series in Informatics (OASIcs), Volume 119, pp. 6:1-6:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) <a href="https://doi.org/10.4230/OASIcs.Tannen.6">https://doi.org/10.4230/OASIcs.Tannen.6</a> </div> <div class="modal fade" id="bibtex-modal" tabindex="-1" aria-labelledby="bibtexModalLabel" aria-hidden="true"> <div class="modal-dialog modal-dialog-centered"> <div class="modal-content"> <div class="modal-header"> <h5 class="modal-title" id="bibtexModalLabel">BibTex</h5> <button type="button" class="btn-close" data-bs-dismiss="modal" aria-label="Close"></button> </div> <div class="modal-body"> <pre class="bibtex">@InProceedings{fan_et_al:OASIcs.Tannen.6, author = {Fan, Wenfei and Liu, Shuhao}, title = {{Fishing Fort: A System for Graph Analytics with ML Prediction and Logic Deduction}}, booktitle = {The Provenance of Elegance in Computation - Essays Dedicated to Val Tannen}, pages = {6:1--6:18}, series = {Open Access Series in Informatics (OASIcs)}, ISBN = {978-3-95977-320-1}, ISSN = {2190-6807}, year = {2024}, volume = {119}, editor = {Amarilli, Antoine and Deutsch, Alin}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Tannen.6}, URN = {urn:nbn:de:0030-drops-201025}, doi = {10.4230/OASIcs.Tannen.6}, annote = {Keywords: graph analytics, data cleaning, association analysis} }</pre> <div style="overflow: hidden"> <textarea style="position: absolute; top: -400vh" id="bibtex-input">@InProceedings{fan_et_al:OASIcs.Tannen.6, author = {Fan, Wenfei and Liu, Shuhao}, title = {{Fishing Fort: A System for Graph Analytics with ML Prediction and Logic Deduction}}, booktitle = {The Provenance of Elegance in Computation - Essays Dedicated to Val Tannen}, pages = {6:1--6:18}, series = {Open Access Series in Informatics (OASIcs)}, ISBN = {978-3-95977-320-1}, ISSN = {2190-6807}, year = {2024}, volume = {119}, editor = {Amarilli, Antoine and Deutsch, Alin}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Tannen.6}, URN = {urn:nbn:de:0030-drops-201025}, doi = {10.4230/OASIcs.Tannen.6}, annote = {Keywords: graph analytics, data cleaning, association analysis} }</textarea> </div> </div> <div class="modal-footer"> <button type="button" class="btn btn-secondary" data-bs-dismiss="modal">Close</button> <button type="button" class="btn btn-primary copy-to-clipboard" data-selector="bibtex-input"><i class="bi bi-clipboard"></i> Copy BibTex To Clipboard<span class="bi bi-check -hidden" style="padding-left: 1em; font-weight: bold"></span></button> </div> </div> </div> </div> </section> <section class="abstract"> <h2>Abstract</h2> <pre class="content" style="white-space: -moz-pre-wrap; white-space: -o-pre-wrap; word-wrap: break-word; white-space: pre-wrap;">This paper reports Fishing Fort, a graph analytic system developed in response to the following questions. What practical value can we get out of graph analytics? How can we effectively deduce the value from a real-life graph? Where can we get clean graphs to make accurate analyses possible? To answer these questions, Fishing Fort advocates to unify logic deduction and ML prediction by proposing Graph Association Rules (GARs), a class of logic rules in which ML models can be embedded as predicates. It employs GARs to deduce graph associations, enrich graphs and clean graphs. It has been deployed in production lines and proven effective in online recommendation, drug discovery, credit risk assessment, battery manufacturing and cybersecurity, among other things.</pre> </section> <section class="subject-classifications mt-3"> <h2>Subject Classification</h2> <div class="acm-subject-classifications"> <h5>ACM Subject Classification</h5> <div class="content"> <ul> <li>Information systems → Data mining</li> <li>Information systems → Data management systems</li> <li>Information systems → Information integration</li> </ul> </div> </div> <div class="keywords "> <h5>Keywords</h5> <div class="content"> <ul class="list-style-comma"> <li>graph analytics</li> <li>data cleaning</li> <li>association analysis</li> </ul> </div> </div> </section> <section class="metrics mt-3"> <h2>Metrics</h2> <div class="content"> <ul> <li> <a href="#" class="btn-statistics" data-entity="document/10.4230/OASIcs.Tannen.6" data-title="Fishing Fort: A System for Graph Analytics with ML Prediction and Logic Deduction"> <i class="bi bi-graph-up"></i> Access Statistics </a> </li> <li> Total Accesses (updated on a weekly basis) <div class="stats-total"> <div class="stats total-downloads"> <div class="circle"> <div class="number" data-number="0">0</div> </div> <div class="label">PDF Downloads</div> </div> <div class="stats total-metadata-views"> <div class="circle"> <div class="number" data-number="0">0</div> </div> <div class="label">Metadata Views</div> </div> </div>  </li> </ul> </div> </section> <div class="offcanvas offcanvas-bottom" tabindex="-1" id="statistics-offcanvas" aria-labelledby="statistics-offcanvas"> <div class="offcanvas-header"> <h5 class="offcanvas-title"></h5> <button type="button" class="btn-close text-reset" data-bs-dismiss="offcanvas" aria-label="Close"></button> </div> <div class="offcanvas-body small" data-context=""> <div style="margin-top: 20vh" class="centered-loader"><div class="loader"></div></div> <iframe class="-hidden"></iframe> </div> </div> <section class="references mt-3"> <h2>References</h2> <div class="content"> <ol> <li> <span> Serge Abiteboul, Richard Hull, and Victor Vianu. 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const element = $('#'+$current.attr('data-selector'))[0]; console.log(element); element.select(); document.execCommand("copy"); const $success = $current.find('.bi-check'); $success.removeClass('-hidden'); setTimeout(function() { $success.addClass('-hidden'); }, 1000); }, initTooltips: function() { const tooltipTriggerList = [].slice.call(document.querySelectorAll('[data-bs-toggle="tooltip"]')) const tooltipList = tooltipTriggerList.map(function (tooltipTriggerEl) { return new bootstrap.Tooltip(tooltipTriggerEl) }); }, expandSearch: function() { app.$el.navbarSearch.addClass('expanded'); }, collapseSearch: function(e) { if (!$(e.currentTarget).is('button.btn-outline-success')) { app.$el.navbarSearch.removeClass('expanded'); } }, initDeepLinksTabs: function() { app.methods.initTabLinks(); const innerAnchors =[]; $('.tab-pane').find('[id]').each(function() { innerAnchors.push(this.getAttribute('id')); }); const anchors = []; $('a[role="tab"]').each(function() { anchors.push($(this).attr('aria-controls')); }); innerAnchors.forEach(function(anchor) { if ($('#'+anchor).length > 0) { anchors.push(anchor); } }); if (anchors.length === 0) { return; } let selectedAnchor = window.location.hash.substring(1); if (anchors.indexOf(selectedAnchor) === -1) { $('#publications-tab').tab('show'); window.scrollTo(0,0); return; } if (selectedAnchor !== 'publications') { const innerAnchorIndex = innerAnchors.indexOf(selectedAnchor); // anchor sits inside tab -> open the tab first, then scroll to anchor if (innerAnchorIndex > -1) { const $tab = $('#'+selectedAnchor).closest('.tab-pane'); console.log($tab); try { $('#'+$tab.attr('id')+'-tab').tab('show'); } catch (e) { } setTimeout(function () { $('#' + selectedAnchor)[0].scrollIntoView(); }, 800); } else { try { $('#' + selectedAnchor + '-tab').tab('show'); } catch (e) { } window.scrollTo(0, 0); } } }, initTabLinks: function() { $('[data-tab-link]').on('click', function(e) { e.preventDefault(); const $target = $(e.currentTarget); document.location.href = $target.attr('href'); 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Fishing Fort: A System for Graph Analytics with ML Prediction and Logic Deduction