PUMA publications for /tag/provenancehttps://puma.ub.uni-stuttgart.de/tag/provenancePUMA RSS feed for /tag/provenance2024-03-29T09:33:11+01:00DATA PROVENANCE VISUALIZATION METHODOLOGIEShttps://puma.ub.uni-stuttgart.de/bibtex/2d14efae20c64ccfa62ac860648e79734/diglezakisdiglezakis2023-09-22T15:44:04+02:00metadata provenance visualization <meta content="thesis" itemprop="educationalUse"/><span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ilkay Melek Yazıcı" itemprop="url" href="/person/1ae056def168c423453394fdd098ea1c8/author/0"><span itemprop="name">I. Yazıcı</span></a></span></span>. </span><span class="additional-entrytype-information"><em>YILDIZ TECHNICAL UNIVERSITY, </em>(<em><span>2023<meta content="2023" itemprop="datePublished"/></span></em>)</span>Fri Sep 22 15:44:04 CEST 2023DATA PROVENANCE VISUALIZATION METHODOLOGIES2023metadata provenance visualization Tracing personal data using comicshttps://puma.ub.uni-stuttgart.de/bibtex/2bec80dbfb19e65252cc313f4b9ac8bec/diglezakisdiglezakis2023-09-22T09:32:08+02:00comics forschungsdaten metadata provenance <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Schreiber" itemprop="url" href="/person/12e3b76187cd0a7c585503ee43bcf4263/author/0"><span itemprop="name">A. Schreiber</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Regina Struminski" itemprop="url" href="/person/12e3b76187cd0a7c585503ee43bcf4263/author/1"><span itemprop="name">R. Struminski</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Universal Access in Human--Computer Interaction. Design and Development Approaches and Methods: 11th International Conference, UAHCI 2017, Held as Part of HCI International 2017, Vancouver, BC, Canada, July 9--14, 2017, Proceedings, Part I 11</span>, </em></span><em>page <span itemprop="pagination">444--455</span>. </em><em>Springer, </em>(<em><span>2017<meta content="2017" itemprop="datePublished"/></span></em>)</span>Fri Sep 22 09:32:08 CEST 2023Universal Access in Human--Computer Interaction. Design and Development Approaches and Methods: 11th International Conference, UAHCI 2017, Held as Part of HCI International 2017, Vancouver, BC, Canada, July 9--14, 2017, Proceedings, Part I 11444--455Tracing personal data using comics2017comics forschungsdaten metadata provenance DLR-SC/prov-comics: QS PROV Comics Prototype - Big
fixeshttps://puma.ub.uni-stuttgart.de/bibtex/25ec4063de2265c2e33979d8966072816/diglezakisdiglezakis2023-09-22T09:24:10+02:00comics forschungsdaten javascript metadata provenance software tools <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stefan Bieliauskas" itemprop="url" href="/person/140418dd0c92a9f6c92f90ca2c6db888d/author/0"><span itemprop="name">S. Bieliauskas</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Schreiber" itemprop="url" href="/person/140418dd0c92a9f6c92f90ca2c6db888d/author/1"><span itemprop="name">A. Schreiber</span></a></span></span>. </span><span class="additional-entrytype-information">(<em><span>April 2017<meta content="April 2017" itemprop="datePublished"/></span></em>)</span>Fri Sep 22 09:24:10 CEST 2023apr{DLR-SC/prov-comics: QS PROV Comics Prototype - Big
fixes}2017comics forschungsdaten javascript metadata provenance software tools DLR-SC/prov-comics: QS PROV Comics Prototype - Big fixes | ZenodoTracing nested data with structural provenance for big data analyticshttps://puma.ub.uni-stuttgart.de/bibtex/2cd8061bce8aa1569289c8387816f7998/katharinafuchskatharinafuchs2021-12-08T17:10:08+01:00EXC2075 from:m.herschel ipvs-de pebble peerReviewed pn7 provenance <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ralf Diestelkämper" itemprop="url" href="/person/160303345861e8a5ec1846d6d36028b19/author/0"><span itemprop="name">R. Diestelkämper</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Melanie Herschel" itemprop="url" href="/person/160303345861e8a5ec1846d6d36028b19/author/1"><span itemprop="name">M. Herschel</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the International Conference on Extending Database Technology (EDBT)</span>, </em></span><em>page <span itemprop="pagination">253-264</span>. </em>(<em><span>2020<meta content="2020" itemprop="datePublished"/></span></em>)</span>Wed Dec 08 17:10:08 CET 2021Proceedings of the International Conference on Extending Database Technology (EDBT)DBLP:conf/edbt/2020253-264Tracing nested data with structural provenance for big data analytics2020EXC2075 from:m.herschel ipvs-de pebble peerReviewed pn7 provenance AMNESIA: A Technical Solution towards GDPR-compliant Machine Learninghttps://puma.ub.uni-stuttgart.de/bibtex/25eef471a46a231ca496ce22e610b8781/corinnagieblercorinnagiebler2020-09-23T15:22:44+02:00GDPR access_control data_protection machine_learning model_management myown privacy_zones provenance <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Christoph Stach" itemprop="url" href="/person/14f6d8079e3b0e4cde537b9ede9972e6d/author/0"><span itemprop="name">C. Stach</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Corinna Giebler" itemprop="url" href="/person/14f6d8079e3b0e4cde537b9ede9972e6d/author/1"><span itemprop="name">C. Giebler</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Manuela Wagner" itemprop="url" href="/person/14f6d8079e3b0e4cde537b9ede9972e6d/author/2"><span itemprop="name">M. Wagner</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Christian Weber" itemprop="url" href="/person/14f6d8079e3b0e4cde537b9ede9972e6d/author/3"><span itemprop="name">C. Weber</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Bernhard Mitschang" itemprop="url" href="/person/14f6d8079e3b0e4cde537b9ede9972e6d/author/4"><span itemprop="name">B. Mitschang</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 6ᵗʰ International Conference on Information Systems Security and Privacy</span>, </em></span><em>volume 1 of ICISSP '20, </em><em>page <span itemprop="pagination">21–32</span>. </em><em>Valletta, </em><em><span itemprop="publisher">SciTePress</span>, </em>(<em><span>February 2020<meta content="February 2020" itemprop="datePublished"/></span></em>)</span>Wed Sep 23 15:22:44 CEST 2020VallettaProceedings of the 6ᵗʰ International Conference on Information Systems Security and Privacyfeb21–32ICISSP '20AMNESIA: A Technical Solution towards GDPR-compliant Machine Learning12020GDPR access_control data_protection machine_learning model_management myown privacy_zones provenance Machine Learning (ML) applications are becoming increasingly valuable due to the rise of IoT technologies. That is, sensors continuously gather data from different domains and make them available to ML for learning its models. This provides profound insights into the data and enables predictions about future trends. While ML has many advantages, it also represents an immense privacy risk. Data protection regulations such as the GDPR address such privacy concerns, but practical solutions for the technical enforcement of these laws are also required. Therefore, we introduce AMNESIA, a privacy-aware machine learning model provisioning platform. AMNESIA is a holistic approach covering all stages from data acquisition to model provisioning. This enables to control which application may use which data for ML as well as to make models "forget" certain knowledge.AMNESIA: A Technical Solution towards GDPR-compliant Machine Learninghttps://puma.ub.uni-stuttgart.de/bibtex/25eef471a46a231ca496ce22e610b8781/christophstachchristophstach2020-09-21T11:45:55+02:00GDPR access_control data_protection machine_learning model_management privacy_zones provenance <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Christoph Stach" itemprop="url" href="/person/14f6d8079e3b0e4cde537b9ede9972e6d/author/0"><span itemprop="name">C. Stach</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Corinna Giebler" itemprop="url" href="/person/14f6d8079e3b0e4cde537b9ede9972e6d/author/1"><span itemprop="name">C. Giebler</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Manuela Wagner" itemprop="url" href="/person/14f6d8079e3b0e4cde537b9ede9972e6d/author/2"><span itemprop="name">M. Wagner</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Christian Weber" itemprop="url" href="/person/14f6d8079e3b0e4cde537b9ede9972e6d/author/3"><span itemprop="name">C. Weber</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Bernhard Mitschang" itemprop="url" href="/person/14f6d8079e3b0e4cde537b9ede9972e6d/author/4"><span itemprop="name">B. Mitschang</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 6ᵗʰ International Conference on Information Systems Security and Privacy</span>, </em></span><em>volume 1 of ICISSP '20, </em><em>page <span itemprop="pagination">21–32</span>. </em><em>Valletta, </em><em><span itemprop="publisher">SciTePress</span>, </em>(<em><span>February 2020<meta content="February 2020" itemprop="datePublished"/></span></em>)</span>Mon Sep 21 11:45:55 CEST 2020VallettaProceedings of the 6ᵗʰ International Conference on Information Systems Security and Privacyfeb21–32ICISSP '20AMNESIA: A Technical Solution towards GDPR-compliant Machine Learning12020GDPR access_control data_protection machine_learning model_management privacy_zones provenance Machine Learning (ML) applications are becoming increasingly valuable due to the rise of IoT technologies. That is, sensors continuously gather data from different domains and make them available to ML for learning its models. This provides profound insights into the data and enables predictions about future trends. While ML has many advantages, it also represents an immense privacy risk. Data protection regulations such as the GDPR address such privacy concerns, but practical solutions for the technical enforcement of these laws are also required. Therefore, we introduce AMNESIA, a privacy-aware machine learning model provisioning platform. AMNESIA is a holistic approach covering all stages from data acquisition to model provisioning. This enables to control which application may use which data for ML as well as to make models "forget" certain knowledge.Start Smart and Finish Wise: The Kiel Marine Science Provenance-Aware Data Management Approach.https://puma.ub.uni-stuttgart.de/bibtex/2d001d3fc74a839e81f6b817949109d2a/diglezakisdiglezakis2020-06-26T10:54:35+02:00automated forschungsdaten metadata provenance <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Peer Brauer" itemprop="url" href="/person/12c374597c0ba8f76daa78fdd3384ec0f/author/0"><span itemprop="name">P. Brauer</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Czerniak" itemprop="url" href="/person/12c374597c0ba8f76daa78fdd3384ec0f/author/1"><span itemprop="name">A. Czerniak</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Wilhelm Hasselbring" itemprop="url" href="/person/12c374597c0ba8f76daa78fdd3384ec0f/author/2"><span itemprop="name">W. Hasselbring</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">TAPP</span>, </em></span><em><span itemprop="publisher">USENIX Association</span>, </em>(<em><span>2014<meta content="2014" itemprop="datePublished"/></span></em>)</span>Fri Jun 26 10:54:35 CEST 2020TAPPconf/tapp/2014Start Smart and Finish Wise: The Kiel Marine Science Provenance-Aware Data Management Approach.2014automated forschungsdaten metadata provenance Preserving Workflow Reproducibility: The RePlay-DH Client as a Tool for Process Documentationhttps://puma.ub.uni-stuttgart.de/bibtex/257fd1341f4dc6348dbcecd9884c5d8c4/diglezakisdiglezakis2020-03-31T10:57:18+02:00forschungsdaten metadata provenance tools <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Markus Gärtner" itemprop="url" href="/person/1563ec029269ef62781b93ce3353b10c1/author/0"><span itemprop="name">M. Gärtner</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Uli Hahn" itemprop="url" href="/person/1563ec029269ef62781b93ce3353b10c1/author/1"><span itemprop="name">U. Hahn</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Sibylle Hermann" itemprop="url" href="/person/1563ec029269ef62781b93ce3353b10c1/author/2"><span itemprop="name">S. Hermann</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)</span>, </em></span><em>page <span itemprop="pagination">563--570</span>. </em><em>Paris, France, </em><em><span itemprop="publisher">European Language Resources Association (ELRA)</span>, </em>(<em><span>May 2018<meta content="May 2018" itemprop="datePublished"/></span></em>)</span>Tue Mar 31 10:57:18 CEST 2020Paris, FranceProceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)may563--570Preserving Workflow Reproducibility: The RePlay-DH Client as a Tool for Process Documentation2018forschungsdaten metadata provenance tools Tracing nested data with structural provenance for big data analyticshttps://puma.ub.uni-stuttgart.de/bibtex/2cd8061bce8aa1569289c8387816f7998/m.herschelm.herschel2020-03-26T09:31:39+01:00EXC2075 ipvs-de pebble peerReviewed pn7 provenance <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ralf Diestelkämper" itemprop="url" href="/person/160303345861e8a5ec1846d6d36028b19/author/0"><span itemprop="name">R. Diestelkämper</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Melanie Herschel" itemprop="url" href="/person/160303345861e8a5ec1846d6d36028b19/author/1"><span itemprop="name">M. Herschel</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the International Conference on Extending Database Technology (EDBT)</span>, </em></span><em>page <span itemprop="pagination">253-264</span>. </em>(<em><span>2020<meta content="2020" itemprop="datePublished"/></span></em>)</span>Thu Mar 26 09:31:39 CET 2020Proceedings of the International Conference on Extending Database Technology (EDBT)253-264Tracing nested data with structural provenance for big data analytics2020EXC2075 ipvs-de pebble peerReviewed pn7 provenance PROV Model Primerhttps://puma.ub.uni-stuttgart.de/bibtex/218d33a650966d4945ee371eb4b98d298/diglezakisdiglezakis2019-05-29T14:00:57+02:00PROV metadata provenance <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Khalid Belhajjame" itemprop="url" href="/person/168522d521265ea4153b999647ecec06e/author/0"><span itemprop="name">K. Belhajjame</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Helena Deus" itemprop="url" href="/person/168522d521265ea4153b999647ecec06e/author/1"><span itemprop="name">H. Deus</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Daniel Garijo" itemprop="url" href="/person/168522d521265ea4153b999647ecec06e/author/2"><span itemprop="name">D. Garijo</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Graham Klyne" itemprop="url" href="/person/168522d521265ea4153b999647ecec06e/author/3"><span itemprop="name">G. Klyne</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Paolo Missier" itemprop="url" href="/person/168522d521265ea4153b999647ecec06e/author/4"><span itemprop="name">P. Missier</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stian Soiland-Reyes" itemprop="url" href="/person/168522d521265ea4153b999647ecec06e/author/5"><span itemprop="name">S. Soiland-Reyes</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stephan Zednik" itemprop="url" href="/person/168522d521265ea4153b999647ecec06e/author/6"><span itemprop="name">S. Zednik</span></a></span></span>. </span><span class="additional-entrytype-information"><em><span itemprop="producer">W3C</span>, </em>(<em><span>2013<meta content="2013" itemprop="datePublished"/></span></em>)</span>Wed May 29 14:00:57 CEST 2019PROV Model Primer2013PROV metadata provenance D-PROV: Extending the PROV Provenance Model with Workflow Structure.https://puma.ub.uni-stuttgart.de/bibtex/2451b2a54030815bf61ab4f433250e970/diglezakisdiglezakis2018-12-04T11:51:53+01:00metadata provenance workflow <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Paolo Missier" itemprop="url" href="/person/14853267b92e65ad26cf43aad42a60576/author/0"><span itemprop="name">P. Missier</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Saumen C. Dey" itemprop="url" href="/person/14853267b92e65ad26cf43aad42a60576/author/1"><span itemprop="name">S. Dey</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Khalid Belhajjame" itemprop="url" href="/person/14853267b92e65ad26cf43aad42a60576/author/2"><span itemprop="name">K. Belhajjame</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Víctor Cuevas-Vicenttín" itemprop="url" href="/person/14853267b92e65ad26cf43aad42a60576/author/3"><span itemprop="name">V. Cuevas-Vicenttín</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Bertram Ludäscher" itemprop="url" href="/person/14853267b92e65ad26cf43aad42a60576/author/4"><span itemprop="name">B. Ludäscher</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">TaPP</span>, </em></span><em><span itemprop="publisher">USENIX Association</span>, </em>(<em><span>2013<meta content="2013" itemprop="datePublished"/></span></em>)</span>Tue Dec 04 11:51:53 CET 2018TaPPconf/tapp/2013D-PROV: Extending the PROV Provenance Model with Workflow Structure.2013metadata provenance workflow Provenance map orbiter: Interactive exploration of large provenance graphs.https://puma.ub.uni-stuttgart.de/bibtex/2263489bb008879e8aee40546ffe85a18/diglezakisdiglezakis2018-11-27T11:50:43+01:00metadata provenance tools visualization <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Peter Macko" itemprop="url" href="/person/1942c30c84f70ae3ff687864a277e15cd/author/0"><span itemprop="name">P. Macko</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Margo Seltzer" itemprop="url" href="/person/1942c30c84f70ae3ff687864a277e15cd/author/1"><span itemprop="name">M. Seltzer</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">TaPP</span>, </em></span>(<em><span>2011<meta content="2011" itemprop="datePublished"/></span></em>)</span>Tue Nov 27 11:50:43 CET 2018TaPPProvenance map orbiter: Interactive exploration of large provenance graphs.2011metadata provenance tools visualization Provenance and Scientific Workflows: Challenges and Opportunitieshttps://puma.ub.uni-stuttgart.de/bibtex/287e551163b741a273e80630495b658d1/diglezakisdiglezakis2018-11-27T11:47:45+01:00metadata provenance scientific workflows <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Susan B. Davidson" itemprop="url" href="/person/1dbc657711d758134c92c99bc29883385/author/0"><span itemprop="name">S. Davidson</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Juliana Freire" itemprop="url" href="/person/1dbc657711d758134c92c99bc29883385/author/1"><span itemprop="name">J. Freire</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data</span>, </em></span><em>page <span itemprop="pagination">1345--1350</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2008<meta content="2008" itemprop="datePublished"/></span></em>)</span>Tue Nov 27 11:47:45 CET 2018New York, NY, USAProceedings of the 2008 ACM SIGMOD International Conference on Management of Data1345--1350SIGMOD '08Provenance and Scientific Workflows: Challenges and Opportunities2008metadata provenance scientific workflows A Framework for Provenance Analysis and Visualizationhttps://puma.ub.uni-stuttgart.de/bibtex/2cdc5ccf1fbbde6134160e2fd126e5b6f/diglezakisdiglezakis2018-11-27T11:46:18+01:00provenance visualization <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Weiner Oliveira" itemprop="url" href="/person/176a03ad792f1fceda5c8489067d72d0b/author/0"><span itemprop="name">W. Oliveira</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Lenitta M. Ambrósio" itemprop="url" href="/person/176a03ad792f1fceda5c8489067d72d0b/author/1"><span itemprop="name">L. Ambrósio</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Regina Braga" itemprop="url" href="/person/176a03ad792f1fceda5c8489067d72d0b/author/2"><span itemprop="name">R. Braga</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Victor Ströele" itemprop="url" href="/person/176a03ad792f1fceda5c8489067d72d0b/author/3"><span itemprop="name">V. Ströele</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="José Maria David" itemprop="url" href="/person/176a03ad792f1fceda5c8489067d72d0b/author/4"><span itemprop="name">J. David</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Fernanda Campos" itemprop="url" href="/person/176a03ad792f1fceda5c8489067d72d0b/author/5"><span itemprop="name">F. Campos</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="journal">Procedia Computer Science</span>, </em> </span>(<em><span>2017<meta content="2017" itemprop="datePublished"/></span></em>)<em>International Conference on Computational Science, ICCS 2017, 12-14 June 2017, Zurich, Switzerland.</em></span>Tue Nov 27 11:46:18 CET 2018Procedia Computer ScienceInternational Conference on Computational Science, ICCS 2017, 12-14 June 2017, Zurich, Switzerland1592 - 1601A Framework for Provenance Analysis and Visualization1082017provenance visualization Data provenance is a fundamental concept in scientific experimentation. However, for their proper understanding and use, efficient and user-friendly mechanisms are needed. Research in software visualization, ontologies and complex networks can help in this process. This paper presents a framework to assist in the understanding and use of data provenance using visualization techniques, ontologies and complex networks. The framework capture the provenance data and generates new information using ontologies and provenance graph analysis. The graph is analyzed through complex networks techniques and provide some metrics to help in each node analyzes. The visualization presents and highlights the inferences and results. The framework was used in the E-SECO scientific ecosystem to support the scientific experimentation.noWorkflow: Capturing and Analyzing Provenance of Scriptshttps://puma.ub.uni-stuttgart.de/bibtex/2b930377db9f99131790fbcacd79dbfde/diglezakisdiglezakis2018-11-27T10:55:53+01:00metadata provenance tools <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Leonardo Murta" itemprop="url" href="/person/109dffb0e6c8f588fd08288815f625d45/author/0"><span itemprop="name">L. Murta</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Vanessa Braganholo" itemprop="url" href="/person/109dffb0e6c8f588fd08288815f625d45/author/1"><span itemprop="name">V. Braganholo</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Fernando Chirigati" itemprop="url" href="/person/109dffb0e6c8f588fd08288815f625d45/author/2"><span itemprop="name">F. Chirigati</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="David Koop" itemprop="url" href="/person/109dffb0e6c8f588fd08288815f625d45/author/3"><span itemprop="name">D. Koop</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Juliana Freire" itemprop="url" href="/person/109dffb0e6c8f588fd08288815f625d45/author/4"><span itemprop="name">J. Freire</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Provenance and Annotation of Data and Processes</span>, </em></span><em>page <span itemprop="pagination">71--83</span>. </em><em>Cham, </em><em><span itemprop="publisher">Springer International Publishing</span>, </em>(<em><span>2015<meta content="2015" itemprop="datePublished"/></span></em>)</span>Tue Nov 27 10:55:53 CET 2018ChamProvenance and Annotation of Data and Processes71--83noWorkflow: Capturing and Analyzing Provenance of Scripts2015metadata provenance tools We propose noWorkflow, a tool that transparently captures provenance of scripts and enables reproducibility. Unlike existing approaches, noWorkflow is non-intrusive and does not require users to change the way they work -- users need not wrap their experiments in scientific workflow systems, install version control systems, or instrument their scripts. The tool leverages Software Engineering techniques, such as abstract syntax tree analysis, reflection, and profiling, to collect different types of provenance, including detailed information about the underlying libraries. We describe how noWorkflow captures multiple kinds of provenance and the different classes of analyses it supports: graph-based visualization; differencing over provenance trails; and inference queries.Provenance for the People: An HCI Perspective on the W3C PROV Standard Through an Online Gamehttps://puma.ub.uni-stuttgart.de/bibtex/29a146547cc27b3e14b56a66ecd61b118/diglezakisdiglezakis2018-11-27T10:10:51+01:00game human-computer-interaction provenance visualization <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Khaled Bachour" itemprop="url" href="/person/1fd24a786e4e4f7c1e831bbdb40afeed4/author/0"><span itemprop="name">K. Bachour</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Richard Wetzel" itemprop="url" href="/person/1fd24a786e4e4f7c1e831bbdb40afeed4/author/1"><span itemprop="name">R. Wetzel</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Martin Flintham" itemprop="url" href="/person/1fd24a786e4e4f7c1e831bbdb40afeed4/author/2"><span itemprop="name">M. Flintham</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Trung Dong Huynh" itemprop="url" href="/person/1fd24a786e4e4f7c1e831bbdb40afeed4/author/3"><span itemprop="name">T. Huynh</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Tom Rodden" itemprop="url" href="/person/1fd24a786e4e4f7c1e831bbdb40afeed4/author/4"><span itemprop="name">T. Rodden</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Luc Moreau" itemprop="url" href="/person/1fd24a786e4e4f7c1e831bbdb40afeed4/author/5"><span itemprop="name">L. Moreau</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems</span>, </em></span><em>page <span itemprop="pagination">2437--2446</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2015<meta content="2015" itemprop="datePublished"/></span></em>)</span>Tue Nov 27 10:10:51 CET 2018New York, NY, USAProceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems2437--2446CHI '15Provenance for the People: An HCI Perspective on the W3C PROV Standard Through an Online Game2015game human-computer-interaction provenance visualization Visualizing Provenance using Comicshttps://puma.ub.uni-stuttgart.de/bibtex/2bbc844f2ad063748b8959a4e3b0740ff/diglezakisdiglezakis2018-11-26T15:17:23+01:00comics metadata provenance visualization <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Schreiber" itemprop="url" href="/person/1e7fe72028e0738dde630a67748dd23e9/author/0"><span itemprop="name">A. Schreiber</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Regina Struminski" itemprop="url" href="/person/1e7fe72028e0738dde630a67748dd23e9/author/1"><span itemprop="name">R. Struminski</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">9th USENIX Workshop on the Theory and Practice of Provenance (TaPP 2017)</span>, </em></span><em><span itemprop="publisher">USENIX Association</span>, </em>(<em><span>June 2017<meta content="June 2017" itemprop="datePublished"/></span></em>)</span>Mon Nov 26 15:17:23 CET 20189th USENIX Workshop on the Theory and Practice of Provenance (TaPP 2017)Proceedings of the 9th USENIX Workshop on the Theory and Practice of Provenance (TaPP 2017)JuniVisualizing Provenance using Comics2017comics metadata provenance visualization Understanding how a piece of data was produced, where it was stored, and by whom it was accessed, is crucial information in many processes. To understand the trace of data, the provenance of that data can be recorded and analyzed. But it is sometimes hard to understand this provenance information, especially for people who are not familiar with software or computer science. To close this gap, we present a visualization technique for data provenance using comics strips. Each strip of the comic represents an activity of the provenance graph, for example, using an app, storing or retrieving data on a cloud service, or generating a diagram. The comic strips are generated automatically using recorded provenance graphs. These provenance comics are intended to enable people to understand the provenance of their data and realize crucial points more easily.Visualizing the Provenance of Personal Data Using Comicshttps://puma.ub.uni-stuttgart.de/bibtex/2c0c0706face2cb6fbf52b8816c5b20ef/diglezakisdiglezakis2018-11-26T15:13:30+01:00comics metadata provenance visualization <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Schreiber" itemprop="url" href="/person/1f13e70c0f4c79fbae5c07c808fb50b66/author/0"><span itemprop="name">A. Schreiber</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Regina Struminski" itemprop="url" href="/person/1f13e70c0f4c79fbae5c07c808fb50b66/author/1"><span itemprop="name">R. Struminski</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="journal">Computers</span>, </em> </span>(<em><span>February 2018<meta content="February 2018" itemprop="datePublished"/></span></em>)</span>Mon Nov 26 15:13:30 CET 2018ComputersFebruar1Quantified Self and Personal InformaticsVisualizing the Provenance of Personal Data Using Comics72018comics metadata provenance visualization Personal health data is acquired, processed, stored, and accessed using a variety of different devices, applications, and services. These are often complex and highly connected. Therefore, use or misuse of the data is hard to detect for people, if they are not capable to understand the trace (i.e., the provenance) of that data. We present a visualization technique for personal health data provenance using comic strips. Each strip of the comic represents a certain activity, such as entering data using a smartphone application, storing or retrieving data on a cloud service, or generating a diagram from the data. The comic strips are generated automatically using recorded provenance graphs. The easy-to-understand comics enable all people to notice crucial points regarding their data such as, for example, privacy violations.Standardisierung eines erweiterbaren Modells für Provenance-Daten (PROV-SPEC)https://puma.ub.uni-stuttgart.de/bibtex/2a55ac24a5b95275ab4ac8b6d53699ca6/diglezakisdiglezakis2018-11-26T15:04:31+01:00forschungsdaten metadata provenance standard <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Schreiber" itemprop="url" href="/person/1c68894da195a6f27342aac8a46e7e826/author/0"><span itemprop="name">A. Schreiber</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"> </span>(<em><span>2016<meta content="2016" itemprop="datePublished"/></span></em>)</span>Mon Nov 26 15:04:31 CET 20182016-04Standardisierung eines erweiterbaren Modells für Provenance-Daten (PROV-SPEC)research report2016forschungsdaten metadata provenance standard A survey on provenance: What for? What form? What from?https://puma.ub.uni-stuttgart.de/bibtex/26110b597ad09046f07385fc109750d0d/diglezakisdiglezakis2018-08-21T17:21:35+02:00litQSaFE provenance <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Melanie Herschel" itemprop="url" href="/person/1a8acf1ec91c81df66b5c3a657f0dbd4b/author/0"><span itemprop="name">M. Herschel</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ralf Diestelkämper" itemprop="url" href="/person/1a8acf1ec91c81df66b5c3a657f0dbd4b/author/1"><span itemprop="name">R. Diestelkämper</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Houssem Ben Lahmar" itemprop="url" href="/person/1a8acf1ec91c81df66b5c3a657f0dbd4b/author/2"><span itemprop="name">H. Ben Lahmar</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="journal">VLDB JOURNAL</span>, </em> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">26 </span></span>(<span itemprop="issueNumber">6</span>):
<span itemprop="pagination">881-906</span></em> </span>(<em><span>December 2017<meta content="December 2017" itemprop="datePublished"/></span></em>)</span>Tue Aug 21 17:21:35 CEST 2018VLDB JOURNALdec6881-906A survey on provenance: What for? What form? What from?Article262017litQSaFE provenance