* [#Configure OwlimSE　配置]

* [#load Load performance] 
    * [#allieload Allie upload]
    * [#pdbjload PDBJ upload]
    * [#uniprotload Uniprot upload]
    * [#ddbjload DDBJ upload]
* [#Sparql  Sparql query performance] 
    * [#alliequery Allie query performance]
    * [#pdbjquery PDBJ query performance]
    * [#uniprotquery Uniprot query performance]
    * [#ddbjquery DDBJ query performance]


=== OwlimSE 配置 === #Configure


{{{
JVMSetting:
 -Xmx55G -Xms30G -XX:+UseG1GC -XX:+TieredCompilation
-Druleset=empty -Dentity-index-size=1147483647 -Dcache-memory=16645m -Dtuple-index-memory=15G -DenablePredicateList=false  -DftsIndexPolicy=never  -Dbuild-pcsot=false -Dbuild-ptsoc=false  -Djournaling=true -Drepository-type=file-repository  -Dentity-id-size=32  
}}}

More information please refer to [http://docs.openlinksw.com/virtuoso/databaseadmsrv.html] 


=== Load Performance === #load

'''Approach 1:'''

'''Approach 2:'''

The idea is from uniprot, which uses owlim as an library as follows:

Basically They have one specific loader program, where there is one java thread that reads the triples into a blocking queue. Then a different number of threads take triples from that queue and insert the data into OWLIM-se (or any other sesame API compatible triplestore). Normally one inserting thread per owlim file-repository fragment. The inserter treads use transactions that commit every half a million statements. The basic is to add statements not files.

    final org.openrdf.model.Statement sesameStatement = getSesameStatement(object);

    //Takes one from the blocking queue filled by the other thread

        connection.add(sesameStatement, graph);

and every millionth statement , do connection.commit();


=== Allie upload === #allieload 

=== PDBJ upload === #pdbjload 

=== Uniprot upload === #uniprotload 

=== DDBJ upload === #ddbjload  


=== Sparql query performance === #alliequery 

=== PDBJ query performance === #pdbjquery 

=== Uniprot query performance === #uniprotquery 

=== DDBJ query performance === #ddbjqu