Triple Store Survey for Life Science Data

• Overview
• Platform
• Data
• Approach
• Database
  • Bigdata => Bigdata
  • 4store => 4store
  • Virtuoso => Virtuoso
  • Owlim-se => OwlimSe
  • Mulgara => Mulgara

• Summary =>Summarize

Overview

Platform

* Machine:

• OS: GNU/linux
• CPU: GenuineIntel? 6; model name : Intel(R) Xeon(R) CPU E5649 @ 2.53GHz; 12 cores 24 hyper-threading
• Mem: 65996128 kB
• Harddisk: SCSI Raid 0 (three hard disks of 1 Tera bytes, two of them are used to store data)

* Software:

• JDK:1.6.0_26
• Virtuoso: 6.3 commercial
• OwlimSE: 4.3.4238
• Mulgara: 2.1.12
• 4store: 1.1.4
• Bigdata: RWSTORE_1_1_0

Data

Allie: .n3 format, 94,420,989 tripples, sparql query attachment:allie.txt .

PDBJ: .rdf.gz format ,589,987,335 triples, 77878 files, from  ftp://ftp.pdbj.org/XML/rdf/. sparql query attachment:pdbj.txt .

The queries in PDBJ are point queries which retrieve the relative characteristics of certain EntryID, such as 107L. Therefore their result set is small but the number of query joins is big.

Uniprot: .rdf.gz format , about 4 billion triples, the 3 largest files are uniprot.rdf.gz,uniparc.rdf.gz,uniref.rdf.gz, from  ftp://ftp.uniprot.org/pub/databases/uniprot/ (the experiment used data was 2011.Nov version). sparql query attachment:uniprot.txt or  http://beta.sparql.uniprot.org/.

DDBJ: .rdf.gz format, about 8 billion triples, 330 files, from  ftp://ftp.ddbj.nig.ac.jp/ddbj_database/ddbj/. sparql query attachment:ddbj.txt .

Approach

We evaluated the data in every Sparql end point at least twice to make it sure that there is no much difference between two test values.

We did the query evaluation by executing the whole query mix (composed of the query sequence) five times in every Sparql endpoint, remove the highest one and then get the average time cost of other four queries. We report the five detailed time cost in every database section and the average cost in the summary section.