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A list of all pages that have property "Has ExperimentSetup" with value "On an Intel Core 2, 2.33GHz, with 2GB of memory". Since there have been only a few results, also nearby values are displayed.

Showing below up to 10 results starting with #1.

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List of results

  • 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)
  • Zhishi.links Results for OAEI 2011  + (Tests were carried out on a Hadoop computer cluster. Each node has a quad-core Intel Core 2 processor (4M Cache, 2.66 GHz), 8GB memory. The number of reduce tasks was set to 50.)
  • Towards a Knowledge Graph Representing Research Findings by Semantifying Survey Articles  + (The evaluation started with the phase of letting researchers first read the given overview questions and letting them try in their own way to find the respective answer.)
  • A Semantic Web Middleware for Virtual Data Integration on the Web  + (The tests were performed with the followin
    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.
    D Opteron CPUs at 1.6 GHz and 2 GB memory.)
  • 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.)
  • ANAPSID: An Adaptive Query Processing Engine for SPARQL Endpoints  + (We empirically analyze the performance of
    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.
    tel Pentium Core2 Duo 3.0 GHz and 8GB RAM.)
  • SERIMI – Resource Description Similarity, RDF Instance Matching and Interlinking  + (We have loaded all these datasets into an
    We have loaded all these datasets into an open-source instance of Virtuoso Universal server 10 , where around 2GB of data were loaded. An exception was the DBPedia dataset, which we accessed online via its Sparql endpoint. The Virtuoso server was installed in a Mac OS X – version 10.5.8, with 2.4 GHz Intel Core 2 Duo processor and with 4 GB 1067 MHz DDR3 of memory. We ran the script that implements the SERIMI approach directly over the local SPARQL endpoints and DBPedia online endpoint.
    RQL endpoints and DBPedia online endpoint.)
  • Querying Distributed RDF Data Sources with SPARQL  + (we split all data over two Sun-Fire-880 ma
    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.
    ver a standard 100Mbit network connection.)
  • Querying over Federated SPARQL Endpoints : A State of the Art Survey  + ({{{ExperimentSetup}}})