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Property:Has ExperimentSetup
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Latest revision as of 14:56, 24 March 2018
This is a property of type Text.
Pages using the property "Has ExperimentSetup"
Showing 25 pages using this property.
A | |
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| A Probabilistic-Logical Framework for Ontology Matching + | All experiments were conducted on a desktop PC with AMD Athlon Dual Core Processor 5400B with 2.6GHz and 1GB RAM. + |
| A Semantic Web Middleware for Virtual Data Integration on the Web + | The tests were performed with the following setup: the mediator (and also the test client) where running on a 2.16 GHz Intel Core 2 Duo with 2 GB memory and a 2 MBit link to the remote endpoints. All endpoints were simulated on the same physical host running two AMD Opteron CPUs at 1.6 GHz and 2 GB memory. + |
| A Survey of Current Link Discovery Frameworks + | No data available now. + |
| ANAPSID: An Adaptive Query Processing Engine for SPARQL Endpoints + | We empirically analyze the performance of the proposed query processing techniques and report on the execution time of plans comprised of ANAPSID operators versus queries posed against SPARQL endpoints, and state-of-the-art RDF engines. Three sets of queries were considered (Table of Figure 5(b)); each sub-query was executed as a query against its corresponding endpoint. Benchmark 1 is a set of 10 queries against LinkedSensorData-blizzards; each query can be grouped into 4 or 5 sub-queries. Benchmark 2 is a set of 10 queries over linkedCT with 3 or 4 subqueries. Benchmark 3 is a set of 10 queries with 4 or 5 sub-queries executed against linkedCT and DBPedia endpoints. Experiments were executed on a Linux CentOS machine with an Intel Pentium Core2 Duo 3.0 GHz and 8GB RAM. + |
| Accessing and Documenting Relational Databases through OWL Ontologies + | No data available now. + |
| Adaptive Integration of Distributed Semantic Web Data + | Endpoint machines are connected to the machine on which the mediator is deployed (2GHz AMD Athlon X2, 2GB RAM) via a 100Mbs Ethernet LAN. + |
| AgreementMaker: Efficient Matching for Large Real-World Schemas and Ontologies + | No data available now. + |
| Analysing Scholarly Communication Metadata of Computer Science Events + | No data available now. + |
| Avalanche: Putting the Spirit of the Web back into Semantic Web Querying + | Test Avalanche using a five-node cluster. Each machine had 2GB RAM and an Intel Core 2 Duo E8500 @ 3.16GHz + |
B | |
| Bringing Relational Databases into the Semantic Web: A Survey + | No data available now. + |
C | |
| Cross: an OWL wrapper for teasoning on relational databases + | On an Intel Core 2, 2.33GHz, with 2GB of memory + |
D | |
| D2RQ – Treating Non-RDF Databases as Virtual RDF Graphs + | No data available now. + |
| DataMaster – a Plug-in for Importing Schemas and Data from Relational Databases into Protégé + | No data available now. + |
| Discovering and Maintaining Links on the Web of Data + | No data available now. + |
F | |
| FedX: Optimization Techniques for Federated Query Processing on Linked Data + | All experiments are carried out on an HP Proliant DL360 G6 with 2GHz 4Core CPU with 128KB L1 Cache, 1024KB L2 Cache, 4096KB L3 Cache, 32GB 1333MHz RAM, and a 160 GB SCSI hard drive. In all scenarios we assigned 20GB RAM to the process executing the query In the SPARQL federation we additionally assign 1GB RAM to each individual SPARQL endpoint process. + |
| From Relational Data to RDFS Models + | No data available now. + |
I | |
| Integration of Scholarly Communication Metadata using Knowledge Graphs + | No data available now. + |
K | |
| KnoFuss: A Comprehensive Architecture for Knowledge Fusion + | - + |
L | |
| LIMES - A Time-Efficient Approach for Large-Scale Link Discovery on the Web of Data + | No data available now. + |
| LogMap: Logic-based and Scalable Ontology Matching + | No data available now. + |
O | |
| Optimizing SPARQL Queries over Disparate RDF Data Sources through Distributed Semi-joins + | No data available now. + |
Q | |
| Querying Distributed RDF Data Sources with SPARQL + | we split all data over two Sun-Fire-880 machines (8x sparcv9 CPU, 1050Mhz, 16GB RAM) running SunOS 5.10. The SPARQL endpoints were provided using Virtuoso Server 5.0.37 with an allowed memory usage of 8GB . Note that, although we use only two physical servers, there were five logical SPARQL endpoints. DARQ was running on Sun Java 1.6.0 on a Linux system with Intel Core Duo CPUs, 2.13 GHz and 4GB RAM. The machines were connected over a standard 100Mbit network connection. + |
| Querying over Federated SPARQL Endpoints : A State of the Art Survey + | {{{ExperimentSetup}}} + |
| Querying the Web of Data with Graph Theory-based Techniques + | The three engines are run independently on a machine having an Intel Xeon W3520 processor, 12 GB memory and 1Gbps LAN. + |
| Querying the Web of Interlinked Datasets using VOID Descriptions + | - + |
Facts about "Has ExperimentSetup"
| Has type "Has type" is a predefined property that describes the datatype of a property and is provided by Semantic MediaWiki. | Text + |
