how much semantic data on small devices?
DESCRIPTION
Short paper presentation at the EKAW 2010 conference on benchmarking RDF triple stores on small devices.TRANSCRIPT
![Page 1: How much Semantic Data on Small Devices?](https://reader034.vdocuments.us/reader034/viewer/2022052623/559b84ae1a28ab2f458b46ec/html5/thumbnails/1.jpg)
How much semantic data on
small devices?
Mathieu d’Aquin, AndriyNikolov and Enrico MottaKnowledge Media Institute, The Open Univeristy, UK
@mdaquin
![Page 2: How much Semantic Data on Small Devices?](https://reader034.vdocuments.us/reader034/viewer/2022052623/559b84ae1a28ab2f458b46ec/html5/thumbnails/2.jpg)
Semantic Data on Small Devices?
![Page 3: How much Semantic Data on Small Devices?](https://reader034.vdocuments.us/reader034/viewer/2022052623/559b84ae1a28ab2f458b46ec/html5/thumbnails/3.jpg)
Benchmarking Semantic Data Tools
Large Scale Benchmarks
LUBM(1,0)103,397 triples
![Page 4: How much Semantic Data on Small Devices?](https://reader034.vdocuments.us/reader034/viewer/2022052623/559b84ae1a28ab2f458b46ec/html5/thumbnails/4.jpg)
Extracting sets of small-scale
ontologies
Clusters of ontologies having similar characteristics, except for size
![Page 5: How much Semantic Data on Small Devices?](https://reader034.vdocuments.us/reader034/viewer/2022052623/559b84ae1a28ab2f458b46ec/html5/thumbnails/5.jpg)
Extracting sets of small-scale
Ontologies
• Characteristics of ontologies
– Size (tiples): varies from very small scale to
medium scale
– Ratio class/prop: allowing 50% variance
– Ratio class/inst.: allowing 50% variance
– DL expressivity: Complexity of the
language
• 99 automatically created clusters
• Manual selection of 10
![Page 6: How much Semantic Data on Small Devices?](https://reader034.vdocuments.us/reader034/viewer/2022052623/559b84ae1a28ab2f458b46ec/html5/thumbnails/6.jpg)
Results
Size (triples) Prop/class Ind/class DL
9-2742 0.65-1.0 1.0-2.0 ALO
27-3688 0.21-0.48 0.07-0.14 ALH
2-8502 N/A N/A -
17-3696 0.66-2.0 4.5-20.5 -
3208-658808 N/A N/A EL
1514-153298 N/A N/A ELR+
8-3657 N/A N/A -
7-4959 1.41-4.0 N/A AL
1-2759 N/A N/A -
43-5132 1.0-2.0 13.0-22.09 -
![Page 7: How much Semantic Data on Small Devices?](https://reader034.vdocuments.us/reader034/viewer/2022052623/559b84ae1a28ab2f458b46ec/html5/thumbnails/7.jpg)
Queries
• Using real life ontologies need domain independent Queries
• A set of 8 generic queries of varying complexity, and which results might depend on inference
Select all labels
Select all comments
Select all labels and comments
Select all RDFS classes
Select all classes (RDFS/OWL/DAML)
Select all instances of all classes
Select all properties applied to instances of all classes
Select all properties by their domain
![Page 8: How much Semantic Data on Small Devices?](https://reader034.vdocuments.us/reader034/viewer/2022052623/559b84ae1a28ab2f458b46ec/html5/thumbnails/8.jpg)
Running the benchmarks – Triple
Stores
Jena with TDB persistent storage
R As above + RDFS reasoning
R
Sesame with persistent storage
As above + RDFS reasoning
Mulgara with default configuration
![Page 9: How much Semantic Data on Small Devices?](https://reader034.vdocuments.us/reader034/viewer/2022052623/559b84ae1a28ab2f458b46ec/html5/thumbnails/9.jpg)
Running the benchmarks – Device
Asus EEE PC 700 (2G)
![Page 10: How much Semantic Data on Small Devices?](https://reader034.vdocuments.us/reader034/viewer/2022052623/559b84ae1a28ab2f458b46ec/html5/thumbnails/10.jpg)
Running the benchmarks - Measures
• Loading time: for each ontologies in an
empty, re-initialized store.
• Disk Space: of the persistent store right
after loading.
• Memory consumption: of the triple store
process right after loading the ontology.
• Query time: for each ontology, averaged
over the 8 queries.
![Page 11: How much Semantic Data on Small Devices?](https://reader034.vdocuments.us/reader034/viewer/2022052623/559b84ae1a28ab2f458b46ec/html5/thumbnails/11.jpg)
Results – Loading time
![Page 12: How much Semantic Data on Small Devices?](https://reader034.vdocuments.us/reader034/viewer/2022052623/559b84ae1a28ab2f458b46ec/html5/thumbnails/12.jpg)
Results – Loading time
R
R
=
![Page 13: How much Semantic Data on Small Devices?](https://reader034.vdocuments.us/reader034/viewer/2022052623/559b84ae1a28ab2f458b46ec/html5/thumbnails/13.jpg)
Results – Disk Space
![Page 14: How much Semantic Data on Small Devices?](https://reader034.vdocuments.us/reader034/viewer/2022052623/559b84ae1a28ab2f458b46ec/html5/thumbnails/14.jpg)
Results – Disk Space
RR=< <
![Page 15: How much Semantic Data on Small Devices?](https://reader034.vdocuments.us/reader034/viewer/2022052623/559b84ae1a28ab2f458b46ec/html5/thumbnails/15.jpg)
Results – Memory consumption
![Page 16: How much Semantic Data on Small Devices?](https://reader034.vdocuments.us/reader034/viewer/2022052623/559b84ae1a28ab2f458b46ec/html5/thumbnails/16.jpg)
Results – Memory
consumptions
R
R
=
![Page 17: How much Semantic Data on Small Devices?](https://reader034.vdocuments.us/reader034/viewer/2022052623/559b84ae1a28ab2f458b46ec/html5/thumbnails/17.jpg)
Result – Query time
![Page 18: How much Semantic Data on Small Devices?](https://reader034.vdocuments.us/reader034/viewer/2022052623/559b84ae1a28ab2f458b46ec/html5/thumbnails/18.jpg)
Result – Query time
R=
R
<
![Page 19: How much Semantic Data on Small Devices?](https://reader034.vdocuments.us/reader034/viewer/2022052623/559b84ae1a28ab2f458b46ec/html5/thumbnails/19.jpg)
Conclusion – on tests
• Sesame performs best in almost all
aspects, even when including reasoning
• Reasoning has big impact on Jena TDB at
query time
• Mulgara is clearly not adequate in a small-
scale scenario
![Page 20: How much Semantic Data on Small Devices?](https://reader034.vdocuments.us/reader034/viewer/2022052623/559b84ae1a28ab2f458b46ec/html5/thumbnails/20.jpg)
Conclusion – on small-scale benchmarking
• Validates our assumption that small-scale benchmarks give different results than large-scale benchmarks
• Points out the need for more work to tackle the small-scale scenarios
• Results are not always clear cut in every aspects: benchmarks as support to decide which tool to use, depending on the application constraints