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Eventos “OntoQuad”
A native RDF Store Server for Semantic Web
Eventos Semantic Web Toolset
•  One of them is “OntoQuad”:
a native RDF Database Management System Server
for Semantic Web...
OntoQuad: native RDF Store Server for Semantic Web
OntoQuad is cross-platform and can be deployed
on different devices:
• ...
OntoQuad RDF Store - benchmarking
Berlin SPARQL Benchmark (BSBM): Electronic commerce scenarios
For comparison, we tested ...
BSBM tests. QMpH for 10 and 100 millions triples
Query Mix per Hour for 10 millions triples dataset
28,847
50,846
79,700
1...
BSBM tests. QMpH for 10 millions datasets for Android
Query Mix per Hour for 10 millions triples dataset, Android vs. Linu...
RIA LOD Datasets on basis of “OntoQuad”
RIA http://opendata.ria.ru/sparql, SPARQL examples
•  Object types with instances ...
San Francisco Open Data Examples
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Summit2013 eventos onto quad

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Transcript of "Summit2013 eventos onto quad"

  1. 1. Eventos “OntoQuad” A native RDF Store Server for Semantic Web
  2. 2. Eventos Semantic Web Toolset •  One of them is “OntoQuad”: a native RDF Database Management System Server for Semantic Web •  We develop a wide range of semantic technology instruments, a whole toolset, including: –  Documents Natural Language Processing –  Documents clusterization and classification –  Semantic Storages –  link discovery framework for the Web of Data
  3. 3. OntoQuad: native RDF Store Server for Semantic Web OntoQuad is cross-platform and can be deployed on different devices: •  MS Windows x64 (developed on Windows 7) •  Unix/Linux x64 (tested on Linux CentOS 6.3) •  Mobile Android (Samsung Galaxy Note II, Google Nexus 7 etc.) •  Raspberry Pi Model B rev 2 •  column-oriented storage, key-value index files implemented using B-trees •  developed with the latest C++ Standard (C++11) •  Supports triple (SPO) or quad (SPOC) configurations •  SPARQL 1.1 Query Language and Protocol, and Java (Jena) API
  4. 4. OntoQuad RDF Store - benchmarking Berlin SPARQL Benchmark (BSBM): Electronic commerce scenarios For comparison, we tested BSBM “Explore” Use case for: –  Virtuoso 6.1.6, –  Jena TDB (Fuseki 0.2.7) and –  BigData (Release 1.2.2). All systems were configured to use 22 GB of main memory. The benchmark machine: –  quad-core Intel i7-3770 CPU with 32 GB of RAM. –  storage is 2x2 TB 7200rpm SATA hard drives, configured as software RAID 1.
  5. 5. BSBM tests. QMpH for 10 and 100 millions triples Query Mix per Hour for 10 millions triples dataset 28,847 50,846 79,700 103,212 6,315 12,253 22,407 28,175 12,963 22,954 34,599 14,132 5,866 10,972 19,153 30,857 0 20,000 40,000 60,000 80,000 100,000 120,000 10m, 1 concurrent user 10m, 2 concurrent users 10m, 4 concurrent users 10m, 16 concurrent users OntoQuad Virtuoso Jena TDB BigData 8,605 15,814 27,009 31,454 5,270 10,270 18,983 22,163 2,466 3,578 5,839 2,8552,432 4,046 5,430 6,151 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 100m mt1 100m mt2 100m mt4 100m mt16 OntoQuad Virtuoso Jena TDB BigData Query Mix per Hour for 100 millions triples dataset
  6. 6. BSBM tests. QMpH for 10 millions datasets for Android Query Mix per Hour for 10 millions triples dataset, Android vs. Linux Server 3177 Query Mix per Hour Database size – 1,72 GB Executable module size - 61 MB Android Samsung Galaxy Note II: •  16 GB storage, 2 GB RAM •  Quad-core 1.6 GHz Cortex-A9 The benchmark Server (for Virtuoso, Jena, BigData): •  quad-core Intel i7-3770 CPU with 32 GB of RAM •  storage is 2x2 TB 7200rpm SATA hard drives, configured as software RAID 1 •  All systems were configured to use 22 GB of main memory 3,177 6,315 12,963 5,866 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 10 millions, 1 concurrent user OntoQuad Android Virtuoso Jena TDB BigData
  7. 7. RIA LOD Datasets on basis of “OntoQuad” RIA http://opendata.ria.ru/sparql, SPARQL examples •  Object types with instances numbers select ?t ?o (count(?s) as ?number) WHERE { ?s a ?t. ?t ?p ?o. FILTER(lang(?o) = "ru") } group by ?t order by desc(?number) •  Object types with sameAs in LOD datasets select ?l ?o WHERE { ?s <http://www.w3.org/2000/01/rdf-schema#label> ?l. ?s <http://www.w3.org/2002/07/owl#sameAs> ?o.} order by ?o •  Persons who live in the same city prefix ria: <http://data.ria.ru#> prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> select ?city ?name {?loc a ria:Location. ?loc rdfs:label ?city. ?s a ria:Person. ?s ria:birthPlace ?loc. ?s rdfs:label ?name. } order by ?city
  8. 8. San Francisco Open Data Examples
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