{ "types" : { "Bookmark" : { "pluralLabel" : "Bookmarks" }, "Publication" : { "pluralLabel" : "Publications" }, "GoldStandardPublication" : { "pluralLabel" : "GoldStandardPublications" }, "GoldStandardBookmark" : { "pluralLabel" : "GoldStandardBookmarks" }, "Tag" : { "pluralLabel" : "Tags" }, "User" : { "pluralLabel" : "Users" }, "Group" : { "pluralLabel" : "Groups" }, "Sphere" : { "pluralLabel" : "Spheres" } }, "properties" : { "count" : { "valueType" : "number" }, "date" : { "valueType" : "date" }, "changeDate" : { "valueType" : "date" }, "url" : { "valueType" : "url" }, "id" : { "valueType" : "url" }, "tags" : { "valueType" : "item" }, "user" : { "valueType" : "item" } }, "items" : [ { "type" : "Bookmark", "id" : "https://puma.ub.uni-stuttgart.de/url/262cddae8fe6f5d70ee99c2c8ba8ec87/diglezakis", "tags" : [ "provenance","metadata","visualization" ], "intraHash" : "262cddae8fe6f5d70ee99c2c8ba8ec87", "label" : "PROV Provenance Visualizer", "user" : "diglezakis", "description" : "erzeugt ein Sankey-Diagramm aus PROV-Graphen", "date" : "2018-07-12 10:07:11", "changeDate" : "2018-07-12 08:07:11", "count" : 1, "url" : "http://provoviz.org/#" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2d14efae20c64ccfa62ac860648e79734/diglezakis", "tags" : [ "provenance","metadata","visualization" ], "intraHash" : "d14efae20c64ccfa62ac860648e79734", "interHash" : "ae056def168c423453394fdd098ea1c8", "label" : "DATA PROVENANCE VISUALIZATION METHODOLOGIES", "user" : "diglezakis", "description" : "", "date" : "2023-09-22 15:44:04", "changeDate" : "2023-09-22 15:44:04", "count" : 2, "pub-type": "phdthesis", "year": "2023", "url": "", "author": [ "Ilkay Melek Yazıcı" ], "authors": [ {"first" : "Ilkay Melek", "last" : "Yazıcı"} ], "bibtexKey": "yazici2023provenance" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2263489bb008879e8aee40546ffe85a18/diglezakis", "tags" : [ "provenance","metadata","visualization","tools" ], "intraHash" : "263489bb008879e8aee40546ffe85a18", "interHash" : "942c30c84f70ae3ff687864a277e15cd", "label" : "Provenance map orbiter: Interactive exploration of large provenance graphs.", "user" : "diglezakis", "description" : "", "date" : "2018-11-27 11:50:43", "changeDate" : "2018-11-27 10:50:43", "count" : 2, "pub-type": "inproceedings", "booktitle": "TaPP", "year": "2011", "url": "", "author": [ "Peter Macko","Margo Seltzer" ], "authors": [ {"first" : "Peter", "last" : "Macko"}, {"first" : "Margo", "last" : "Seltzer"} ], "eventtitle" : "TaPP", "eventdate" : "Jun 2011", "bibtexKey": "macko2011provenance" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2bbc844f2ad063748b8959a4e3b0740ff/diglezakis", "tags" : [ "provenance","metadata","visualization","comics" ], "intraHash" : "bbc844f2ad063748b8959a4e3b0740ff", "interHash" : "e7fe72028e0738dde630a67748dd23e9", "label" : "Visualizing Provenance using Comics", "user" : "diglezakis", "description" : "", "date" : "2018-11-26 15:17:23", "changeDate" : "2018-11-26 14:17:23", "count" : 2, "pub-type": "inproceedings", "journal": "Proceedings of the 9th USENIX Workshop on the Theory and Practice of Provenance (TaPP 2017)","booktitle": "9th USENIX Workshop on the Theory and Practice of Provenance (TaPP 2017)","publisher":"USENIX Association", "year": "2017", "url": "https://elib.dlr.de/113536/", "author": [ "Andreas Schreiber","Regina Struminski" ], "authors": [ {"first" : "Andreas", "last" : "Schreiber"}, {"first" : "Regina", "last" : "Struminski"} ], "editor": [ "Adam Bates","Bill Howe" ], "editors": [ {"first" : "Adam", "last" : "Bates"}, {"first" : "Bill", "last" : "Howe"} ], "abstract": "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.", "bibtexKey": "dlr113536" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2c0c0706face2cb6fbf52b8816c5b20ef/diglezakis", "tags" : [ "provenance","metadata","visualization","comics" ], "intraHash" : "c0c0706face2cb6fbf52b8816c5b20ef", "interHash" : "f13e70c0f4c79fbae5c07c808fb50b66", "label" : "Visualizing the Provenance of Personal Data Using Comics", "user" : "diglezakis", "description" : "", "date" : "2018-11-26 15:13:30", "changeDate" : "2018-11-26 14:13:30", "count" : 1, "pub-type": "article", "journal": "Computers","series": "Quantified Self and Personal Informatics","publisher":"MDPI", "year": "2018", "url": "https://elib.dlr.de/118681/", "author": [ "Andreas Schreiber","Regina Struminski" ], "authors": [ {"first" : "Andreas", "last" : "Schreiber"}, {"first" : "Regina", "last" : "Struminski"} ], "volume": "7","number": "1","abstract": "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.", "bibtexKey": "dlr118681" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2137cddb38ddebf08ad6e10a6a5995d1d/diglezakis", "tags" : [ "forschungsdaten","provenance","metadata","visualization" ], "intraHash" : "137cddb38ddebf08ad6e10a6a5995d1d", "interHash" : "486df7ee5e4e6d9b3c1566d4dfbbf0f2", "label" : "Prov Viewer: A Graph-Based Visualization Tool for Interactive Exploration of Provenance Data", "user" : "diglezakis", "description" : "Prov Viewer: A Graph-Based Visualization Tool for Interactive Exploration of Provenance Data | SpringerLink", "date" : "2018-05-11 11:08:59", "changeDate" : "2018-05-11 09:08:59", "count" : 2, "pub-type": "inproceedings", "booktitle": "Provenance and Annotation of Data and Processes","publisher":"Springer International Publishing","address":"Cham", "year": "2016", "url": "", "author": [ "Troy Kohwalter","Thiago Oliveira","Juliana Freire","Esteban Clua","Leonardo Murta" ], "authors": [ {"first" : "Troy", "last" : "Kohwalter"}, {"first" : "Thiago", "last" : "Oliveira"}, {"first" : "Juliana", "last" : "Freire"}, {"first" : "Esteban", "last" : "Clua"}, {"first" : "Leonardo", "last" : "Murta"} ], "editor": [ "Marta Mattoso","Boris Glavic" ], "editors": [ {"first" : "Marta", "last" : "Mattoso"}, {"first" : "Boris", "last" : "Glavic"} ], "pages": "71--82","abstract": "The analysis of provenance data for an experiment is often crucial to understand the achieved results. For long-running experiments or when provenance is captured at a low granularity, this analysis process can be overwhelming to the user due to the large volume of provenance data. In this paper we introduce, Prov Viewer, a provenance visualization tool that enables users to interactively explore provenance data. Among the visualization and exploratory features, we can cite zooming, filtering, and coloring. Moreover, we use of other properties such as shape and size to distinguish visual elements. These exploratory features are linked to the provenance semantics to ease the comprehension process. We also introduce collapsing and filtering strategies, allowing different levels of granularity exploration and analysis. We describe case studies that show how Prov Viewer has been successfully used to explore provenance in different domains, including games and urban data.", "isbn" : "978-3-319-40593-3", "bibtexKey": "10.1007/978-3-319-40593-3_6" } ] }