Telecommunication Networks as Knowledge Graph Research Datasets

<p><strong>Telecommunication Networks as Knowledge Graph Research Datasets.</strong></p> <p>All the datasets were created using the PC with following parameters:&nbsp;Intel(R) Core(TM) &nbsp;i7-9750H CPU @ 2.60GHz/16.00 GB RAM 1TB SSD</p> <p>All the software which is used for the datasets creation is available here:&nbsp;https://github.com/kulikovia/TN_KG_research<strong>&nbsp;</strong></p> <p>The datasets and supplementary files description of follow:</p> <table> <tbody> <tr> <td> <p><strong>#</strong></p> </td> <td> <p><strong>File</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>1.</p> </td> <td> <p>Computational_complexity_v5_ (ENG).pdf</p> </td> <td> <p>SPARQL performance tests report</p> </td> </tr> <tr> <td> <p>2.</p> </td> <td> <p>Synthesis_performance_tests_results_v1.pdf</p> </td> <td> <p>Inductive and deductive synthesis performance tests report</p> </td> </tr> <tr> <td>3.</td> <td>Computational_complexity_Parallel_v2_ (ENG).pdf</td> <td>Comparision of SPARQL performanse using multi-level KG structure approach and execution using distributed RDF storege&nbsp;</td> </tr> <tr> <td>4.</td> <td>Synthesis_additional_experiments_v4.pdf</td> <td>Additional&nbsp;Inductive and deductive synthesis performance tests report (with different elements distribution by levels)</td> </tr> <tr> <td> <p>5.</p> </td> <td> <p>Dataset_10k_hierarchical.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 10k hierarchical model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>6.</p> </td> <td> <p>Dataset_10k_one-level.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 10k one-level model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>7.</p> </td> <td> <p>Dataset_1k_hierarchical.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 1k hierarchical model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>8.</p> </td> <td> <p>Dataset_1k_one-level.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 1k one-level model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>9.</p> </td> <td> <p>Dataset_200k_hierarchical.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 200k hierarchical model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>10.</p> </td> <td> <p>Dataset_200k_one-level.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 200k one-level model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>11.</p> </td> <td> <p>Dataset_500k_hierarchical.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 500k hierarchical model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>12.</p> </td> <td> <p>Dataset_500k_one-level.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 500k one-level model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>13.</p> </td> <td> <p>Synthesis_Hierarchical_RDF-XML.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: RDF/XML datasets for hierarchical models (1k, 10k, 200k, 500k)</p> </td> </tr> <tr> <td> <p>14.</p> </td> <td> <p>Synthesis_Linear_RDF-XML.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: RDF/XML datasets for hierarchical models (1k, 10k, 200k, 500k)</p> </td> </tr> <tr> <td> <p>15.</p> </td> <td> <p>Hierarchy_model_results_Exp_10M.zip</p> </td> <td> <p>SPARQL performance tests: Datasets for 10M,&nbsp;3-5-levels, exponential distributed&nbsp;model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> <tr> <td>16.</td> <td>Hierarchy_model_results_Exp_15M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 15M,&nbsp;3-5-levels, exponential distributed&nbsp;model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>17.</td> <td>Hierarchy_model_results_Exp_200k.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 200k,&nbsp;3-5-levels, exponential distributed&nbsp;model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>18.</td> <td>Hierarchy_model_results_Linear_10M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 10M, 3-5-levels, linear&nbsp;distributed&nbsp;model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>19.</td> <td>Hierarchy_model_results_Linear_15M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 15M,&nbsp;3-5-levels, linear&nbsp;distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>20.</td> <td>Hierarchy_model_results_Linear_200k.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 200k,&nbsp;3-5-levels, linear&nbsp;distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>21.</td> <td>Hierarchy_model_results_Quadro_10M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 10M,&nbsp;3-5-levels, quadratic distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>22.</td> <td>Hierarchy_model_results_Quadro_15M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 15M,&nbsp;3-5-levels, quadratic distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>23.</td> <td>Hierarchy_model_results_Quadro_200k.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 200k, 3-5-levels, quadratic distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>24.</td> <td>Hierarchy_model_results_Uniform_10M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 10M, 3-5-levels, uniform distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>25.</td> <td>Hierarchy_model_results_Uniform_15M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 15M,&nbsp;3-5-levels, uniform distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>26.</td> <td>Hierarchy_model_results_Uniform_200k.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 200k, 3-5-levels, uniform distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td> <p>27.</p> </td> <td> <p>Linear_model_resuts.zip</p> </td> <td> <p>SPARQL performance tests: Datasets for 200k, 10M, 15M, one-level&nbsp;model in RDF/XML format</p> </td> </tr> </tbody> </table>