Merge branch 'main' of https://github.com/athenarc/acmsigmod.gr-site

hdms/2024/program.html

@@ -757,15 +757,38 @@ <h4> Download the program
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                                     <td class="program-sessions">12:50 - 1:00pm</td>
                                     <td class="program-sessions"> <i> Proportionality on Spatial Data with Context </i> 
-                                        <br> Fakas, George; Kalamatianos, Georgios; 
+                                        <br> Fakas, George; Kalamatianos, Georgios
                                         <br> <a class="toggle-abstract" onclick="toggleAbstract('abstract-30')">Click to display the abstract</a>
                                         <span class="paper-abstract" id="abstract-30">
                                             More often than not, spatial objects are associated with some context, in the form of text, descriptive tags (e.g., points of interest, flickr photos), or linked entities in semantic graphs (e.g., Yago2, DBpedia). Hence, location-based retrieval should be extended to consider not only the locations but also the context of the objects, especially when the retrieved objects are too many and the query result is overwhelming. In this article, we study the problem of selecting a subset of the query result, which is the most representative. We argue that objects with similar context and nearby locations should proportionally be represented in the selection. Proportionality dictates the pairwise comparison of all retrieved objects and hence bears a high cost. We propose novel algorithms which greatly reduce the cost of proportional object selection in practice. In addition, we propose pre-processing, pruning, and approximate computation techniques that their combination reduces the computational cost of the algorithms even further. We theoretically analyze the approximation quality of our approaches. Extensive empirical studies on real datasets show that our algorithms are effective and efficient. A user evaluation verifies that proportional selection is more preferable than random selection and selection based on object diversification.
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-                                    <td class="program-sessions"> 1:15-3:00pm </td>
+                                    <td class="program-sessions">1:00 - 1:10pm</td>
+                                    <td class="program-sessions"> <i> Graph Theory for Consent Management: A New Approach for Complex Data Flows </i> 
+                                        <br> Filipczuk, Dorota; Gerding, Enrico H.; Konstantinidis, George
+                                        <br> <a class="toggle-abstract" onclick="toggleAbstract('abstract-31')">Click to display the abstract</a>
+                                        <span class="paper-abstract" id="abstract-31">
+                                            Through legislation and technical advances users gain more
+                                            control over how their data is processed, and they expect
+                                            online services to respect their privacy choices and preferences. However, data may be processed for many di↵erent
+                                            purposes by several layers of algorithms that create complex data workflows. To date, there is no existing approach
+                                            to automatically satisfy fine-grained privacy constraints of
+                                            a user in a way which optimises the service provider’s gains
+                                            from processing. In this article, we propose a solution to
+                                            this problem by modelling a data flow as a graph. User constraints and processing purposes are pairs of vertices which
+                                            need to be disconnected in this graph. We show that, in
+                                            general, this problem is NP-hard and we propose several
+                                            heuristics and algorithms. We discuss the optimality versus
+                                            eciency of our algorithms and evaluate them using synthetically generated data. On the practical side, our algorithms
+                                            can provide nearly optimal solutions for tens of constraints
+                                            and graphs of thousands of nodes, in a few seconds.
+                                        </span>
+                                    </td>
+                                </tr>
+                                <tr>
+                                    <td class="program-sessions"> 1:20-3:00pm </td>
                                     <td class="program-sessions"> <b>Lunch break & Mentoring Event</b></td>
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