LDIF - Benchmark Results

For this use case we used only KEGG GENES and UniProt datasets. There is a huge difference in dataset size between the two datasets. Converted to N-Triples the complete KEGG GENES dump is about 28GB in size whereas the UniProt dataset contains over 400GB worth of data.

For the test, we generated subsets of both data sources amounting together to 25, 100, 150 and 300 million RDF triples. The 3690 M dataset include the complete UniProt and complete KEGG GENES datasets.

Details about the benchmark datasets are summarized in the following table. It provides statistics about the data integration process for each dataset. The original number of input triples decreases in the process as LDIF discards input triples which are irrelevant for the defined mappings, and therefore can not be translated into the target vocabulary. The number decreases again after the actual translation, as the input data uses more verbose vocabularies and as multiple triples from the input data are thus combined into single triples in the target vocabulary. The size of the final dataset is the number of quads after the mapping phase plus any provided provenance data.

We defined R2R mappings for translating genes, diseases and pathways from KEGG GENES and genes from UniProt into a proprietary target vocabulary. Some more sophisticated mappings from the use case translate complex structural patterns and perform value transformations (e.g. extracting an integer value from a URI). The prevalent value transformations are extracting strings with a regular expression and modifying the target data types.

• mapping a KEGG GENE gene into a target vocabulary Gene

   mp:Gene
     a r2r:ClassMapping;
     r2r:prefixDefinitions
          "category: <http://mywiki/resource/category/> .
           property: <http://mywiki/resource/property/> .
           pathway:  <http://wiking.vulcan.com/neurobase/kegg_pathway/resource/vocab/> .
           genes:    <http://wiking.vulcan.com/neurobase/kegg_genes/resource/vocab/> .
           xsd:      <http://www.w3.org/2001/XMLSchema#> .";
     r2r:sourcePattern "?SUBJ a genes:gene";
     r2r:targetPattern "?SUBJ a category:Gene";
     .

• mapping a KEGG GENE externalLink property into a target vocabulary UniprotId property

   mp:GeneLinkUniProt
     a r2r:PropertyMapping;
     r2r:mappingRef mp:Gene;
     r2r:sourcePattern  "?SUBJ genes:externalLink ?x";
     r2r:transformation "?id = regexToList('UniProt:(.+)', ?x)";
     r2r:targetPattern  "?SUBJ property:UniprotId ?'id'^^xsd:string";
     . 

We defined the following Link Specification for the identity resolution phase:

<Silk>
	<Prefixes>
		<Prefix id="rdf" namespace="http://www.w3.org/1999/02/22-rdf-syntax-ns#" />
		<Prefix id="rdfs" namespace="http://www.w3.org/2000/01/rdf-schema#" />
		<Prefix id="owl" namespace="http://www.w3.org/2002/07/owl#" />
		<Prefix id="property" namespace="http://mywiki/resource/property/" />
		<Prefix id="category" namespace="http://mywiki/resource/category/" />
	</Prefixes>

	<Interlinks>
		<Interlink id="link">
			<LinkType>owl:sameAs</LinkType>

			<SourceDataset dataSource="Source" var="a">
				<RestrictTo>
					{ ?a rdf:type category:Gene }
					UNION { ?a rdf:type category:Disease }
					UNION { ?a rdf:type category:Pathway }
				</RestrictTo>
			</SourceDataset>

			<TargetDataset dataSource="Target" var="b">
				<RestrictTo>
					{ ?b rdf:type category:Gene }
					UNION { ?b rdf:type category:Disease }
					UNION { ?b rdf:type category:Pathway }
				</RestrictTo>
			</TargetDataset>

			<LinkageRule>
			<Aggregate type="max">
					<Compare metric="equality">
						<Input path="?a/property:UniprotId" />
						<Input path="?b/property:UniprotId" />
					</Compare>
					<Compare metric="equality">
						<Input path="?a/property:EntrezGeneId" />
						<Input path="?b/property:EntrezGeneId" />
					</Compare>
					<Compare metric="equality">
						<Input path="?a/property:MgiMarkerAccessionId" />
						<Input path="?b/property:MgiMarkerAccessionId" />
					</Compare>
					<Compare metric="equality">
						<Input path="?a/property:KeggDiseaseId" />
						<Input path="?b/property:KeggDiseaseId" />
					</Compare>
					<Compare metric="equality">
						<Input path="?a/property:KeggPathwayId" />
						<Input path="?b/property:KeggPathwayId" />
					</Compare>
			</Aggregate>
			</LinkageRule>

			<Filter threshold="1.0" />

		</Interlink>

	</Interlinks>
</Silk>

These machines have been used for In-Memory, TDB and FUB Hadoop clusters.

There machines have been used for EC2 Hadoop clusters.

In all cluster configurations, the master works as job tracker and name node, while the slaves work as data nodes and task trackers.

FUB Hadoop 2-slaves cluster

• 1 master, 2 slaves (FUB machines)
• Network: Gigabit Ethernet

EC2 Hadoop X-slaves cluster

• 1 master, X slaves (EC2 c1.medium instances)

For each single machine test, we applied the following procedure:

1. Clear Operating System caches

    echo 2 > /proc/sys/vm/drop_caches 

2. Run test

    java -server -Xmx20G -jar ldif-single-machine.jar 

For each distributed test, we applied the following procedure:

1. Launch a master instance w/ EBS containing input data attached
2. Launch slave instances
3. Init the cluster (connect via SSH, clear all temporary data, format HDFS, run start-all)
4. Upload input data into HDFS (from EBS)
5. Run integration