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Resources of "Declarative RDF Graph Construction: Materialization or Virtualization?"

<p>Resources for the paper &quot;Declarative RDF Graph Construction: Materialization or Virtualization?&quot;, currently under double-blind review, thus all information about the authors is redacted.</p> <ul> <li><code>GTFS-MADRID-BENCH.tar.xz</code>:&nbsp;GTFS Madrid Bench with scales 1, 25, 50</li> <li><code>BSBM.tar.xz</code>: BSBM benchmark with scales 5000, 10000, 15000</li> <li>SANTA: <ul> <li><code>PARALLEL-LOADING.tar.xz</code>: parallel loading parameter data with 1, 8, 16, 24, 32 cores.</li> <li><code>DUPLICATES.tar.xz</code>: duplicate values&nbsp;parameter data with 0%, 25%, 50%, 75% and 100% of the data containing duplicates.</li> <li><code>EMPTY-VALUES.tar.xz</code>: empty values parameter data&nbsp;with 0%, 25%, 50%, 75% and 100% of the data containing empty values.</li> <li><code>RAW-DATA.tar.xz</code>: scaling among data records &amp; properties parameter data with 1000, 5000, 25000,&nbsp;125000 records and 1, 5, 10, 15 properties.</li> <li><code>JOIN-1_1.tar.xz</code>: Join 1-1 data.</li> <li><code>JOIN-1-N.tar.xz</code>: Join 1-N data with relationship N: 3, 5, 10, 15.</li> <li><code>JOIN-N-1.tar.xz</code>: Join N-1&nbsp;data&nbsp;with relationship N: 3, 5, 10, 15.</li> <li><code>JOIN-N-M.tar.xz</code>: Join N-M data with&nbsp;relationships N-M: 3-3, 3-5, 5-3, 10-5, 5-10.</li> <li><code>JOIN-DUPLICATES.tar.xz</code>: Join duplicates data with 0%, 25%, 50%, 75%, 100% of the joins generating duplicates.</li> <li><code>MAPPINGS.tar.xz</code>: Triples Maps &amp; Predicate Object Maps mapping rules impact parameter data with 1 TM + 15 POMs, 15 TMs + 1 POM, 3 TMs + 5 POMs, 5 TMs + 3 POMs.</li> </ul> </li> <li>Graphs <ul> <li>GTFS-graphs.pdf: GTFS-Madrid-Bench query execution time graph in full resolution</li> <li>BSBM-graphs.pdf: BSBM query execution time graph in full resolution</li> </ul> </li> </ul> <p><strong>Note</strong>: These archives are heavily compressed, make sure you have enough disk space to unpack them.</p> <p>Our <a href="https://github.com/blindreviewing/eswc2023-materialization-virtualization">bench-executor tool</a>&nbsp;can execute these automatically for you, you need to set the <code>--root</code> parameter to the root directory of the unpacked archive and execute them with the <code>run</code> command or list them all with the <code>list</code> command. Extensive instructions on how to use the tool are available in the README of the linked repository.</p>

ShareScore

36/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
4
Harmonization
4
Access
16
Reuse readiness
8
Engagement
4