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How much semantic data on small devices?<br />Mathieu d’Aquin, AndriyNikolov and Enrico Motta<br />Knowledge Media Institu...
Semantic Data on Small Devices?<br />
Benchmarking Semantic Data Tools<br />LUBM(1,0)<br />103,397 triples<br />Large Scale Benchmarks<br />
Extracting sets of small-scale ontologies<br />Clusters of ontologies having similar characteristics, except for size<br />
Extracting sets of small-scale Ontologies<br />Characteristics of ontologies<br />Size (tiples): varies from very small sc...
Results<br />
Queries<br />Using real life ontologies need domain independent Queries<br />A set of 8 generic queries of varying comple...
Running the benchmarks – Triple Stores<br />Jena with TDB persistent storage<br />R<br />As above + RDFS reasoning<br />Se...
Running the benchmarks – Device<br />Asus EEE PC 700 (2G)<br />
Running the benchmarks - Measures<br />Loading time: for each ontologies in an empty, re-initialized store.<br />Disk Spac...
Results – Loading time<br />
Results – Loading time<br />R<br />=<br />R<br />
Results – Disk Space<br />
Results – Disk Space<br />=<br /><<br /><<br />R<br />R<br />
Results – Memory consumption<br />
Results – Memory consumptions<br />R<br />R<br />=<br />
Result – Query time<br />
Result – Query time<br />=<br /><<br />R<br />R<br />
Conclusion – on tests<br />Sesame performs best in almost all aspects, even when including reasoning<br />Reasoning has bi...
Conclusion – on small-scale benchmarking<br />Validates our assumption that small-scale benchmarks give different results ...
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How much Semantic Data on Small Devices?

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Short paper presentation at the EKAW 2010 conference on benchmarking RDF triple stores on small devices.

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Transcript of "How much Semantic Data on Small Devices?"

  1. 1. How much semantic data on small devices?<br />Mathieu d’Aquin, AndriyNikolov and Enrico Motta<br />Knowledge Media Institute, The Open Univeristy, UK<br />m.daquin@open.ac.uk<br />@mdaquin<br />
  2. 2. Semantic Data on Small Devices?<br />
  3. 3. Benchmarking Semantic Data Tools<br />LUBM(1,0)<br />103,397 triples<br />Large Scale Benchmarks<br />
  4. 4. Extracting sets of small-scale ontologies<br />Clusters of ontologies having similar characteristics, except for size<br />
  5. 5. Extracting sets of small-scale Ontologies<br />Characteristics of ontologies<br />Size (tiples): varies from very small scale to medium scale<br />Ratio class/prop: allowing 50% variance<br />Ratio class/inst.: allowing 50% variance<br />DL expressivity: Complexity of the language<br />99 automatically created clusters<br />Manual selection of 10<br />
  6. 6. Results<br />
  7. 7. Queries<br />Using real life ontologies need domain independent Queries<br />A set of 8 generic queries of varying complexity, and which results might depend on inference<br />Select all instances of all classes<br />Select all comments <br />Select all labels and comments<br />Select all labels<br />Select all classes (RDFS/OWL/DAML)<br />Select all properties by their domain<br />Select all RDFS classes<br />Select all properties applied to instances of all classes<br />
  8. 8. Running the benchmarks – Triple Stores<br />Jena with TDB persistent storage<br />R<br />As above + RDFS reasoning<br />Sesame with persistent storage<br />R<br />As above + RDFS reasoning<br />Mulgara with default configuration<br />
  9. 9. Running the benchmarks – Device<br />Asus EEE PC 700 (2G)<br />
  10. 10. Running the benchmarks - Measures<br />Loading time: for each ontologies in an empty, re-initialized store.<br />Disk Space: of the persistent store right after loading.<br />Memory consumption: of the triple store process right after loading the ontology.<br />Query time: for each ontology, averaged over the 8 queries. <br />
  11. 11. Results – Loading time<br />
  12. 12. Results – Loading time<br />R<br />=<br />R<br />
  13. 13. Results – Disk Space<br />
  14. 14. Results – Disk Space<br />=<br /><<br /><<br />R<br />R<br />
  15. 15. Results – Memory consumption<br />
  16. 16. Results – Memory consumptions<br />R<br />R<br />=<br />
  17. 17. Result – Query time<br />
  18. 18. Result – Query time<br />=<br /><<br />R<br />R<br />
  19. 19. Conclusion – on tests<br />Sesame performs best in almost all aspects, even when including reasoning<br />Reasoning has big impact on Jena TDB at query time<br />Mulgara is clearly not adequate in a small-scale scenario<br />
  20. 20. Conclusion – on small-scale benchmarking<br />Validates our assumption that small-scale benchmarks give different results than large-scale benchmarks<br />Points out the need for more work to tackle the small-scale scenarios<br />Results are not always clear cut in every aspects: benchmarks as support to decide which tool to use, depending on the application constraints<br />
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