Matches in ScholarlyData for { ?s ?p ?o. }
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- item-3 hasContent philippe-cudre-mauroux.
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- 28 creator dilshod-ibragimov.
- 28 creator esteban-zimanyi.
- 28 creator katja-hose.
- 28 creator torben-bach-pedersen.
- 28 type InProceedings.
- 28 label "Processing Aggregate Queries in a Federation of SPARQL Endpoints".
- 28 sameAs 28.
- 28 abstract "More and more RDF data is exposed on the Web via SPARQL endpoints. With the recent SPARQL 1.1 standard, these datasets can be queried in novel and more powerful ways, e.g., complex analysis tasks involving grouping and aggregation, and even data from multiple SPARQL endpoints, can now be formulated in a single query. This enables Business Intelligence applications that access data from federated web sources and can combine it with local data. However, as both aggregate and federated queries have become available only recently, state-of-the-art systems lack sophisticated optimization techniques that facilitate efficient execution of such queries over large datasets. To overcome these shortcomings, we propose a set of query processing strategies and the associated Cost-based Optimizer for Distributed Aggregate queries (CoDA) for executing aggregate SPARQL queries over federations of SPARQL endpoints. Our comprehensive experiments show that CoDA significantly improves performance over current state-of-the-art systems.".
- 28 hasAuthorList authorList.
- 28 isPartOf proceedings.
- 28 keyword "RDF".
- 28 keyword "SPARQL 1.1".
- 28 keyword "aggregate queries".
- 28 keyword "federated query processing".
- 28 title "Processing Aggregate Queries in a Federation of SPARQL Endpoints".
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- item-1 hasContent dilshod-ibragimov.
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- item-2 hasContent katja-hose.
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- item-3 hasContent torben-bach-pedersen.
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- item-4 hasContent esteban-zimanyi.
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- 32 creator javier-d-fernandez.
- 32 creator jose-m-gimenez-garcia.
- 32 creator miguel-a-martinez-prieto.
- 32 type InProceedings.
- 32 label "HDT-MR: A Scalable Solution for RDF Compression with HDT and MapReduce".
- 32 sameAs 32.
- 32 abstract "HDT a is binary RDF serialization aiming at minimizing the space overheads of traditional RDF formats, while providing retrieval features in compressed space. Several HDT-based applications, such as the recent Linked Data Fragments proposal, leverage these features for diverse publication, interchange and consumption purposes. However, scalability issues emerge in HDT construction because the whole RDF dataset must be processed in a memory-consuming task. This is hindering the evolution of novel applications and techniques at Web scale. This paper introduces HDT-MR, a MapReduce-based technique to process huge RDF and build the HDT serialization. HDT-MR performs in linear time with the dataset size and has proven able to serialize datasets up to 4.42 billion triples, preserving HDT compression and retrieval features. These results enable a new generation of HDT-based applications.".
- 32 hasAuthorList authorList.
- 32 isPartOf proceedings.
- 32 keyword "Big Semantic Data".
- 32 keyword "HDT".
- 32 keyword "MapReduce".
- 32 keyword "RDF compression".
- 32 keyword "Scalability".
- 32 title "HDT-MR: A Scalable Solution for RDF Compression with HDT and MapReduce".
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- item-1 hasContent jose-m-gimenez-garcia.
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- item-2 hasContent javier-d-fernandez.
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- item-3 hasContent miguel-a-martinez-prieto.
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- 35 creator albert-zuendorf.
- 35 creator martin-peters.
- 35 creator sabine-sachweh.
- 35 type InProceedings.
- 35 label "Large scale rule-based Reasoning using a Laptop".
- 35 sameAs 35.
- 35 abstract "Although recent developments have shown that it is possible to reason over large RDF datasets with billions of triples in a scalable way, the reasoning process can still be a challenging task with respect to the growing amount of available semantic data. By now, reasoner implementation that are able to process large scale datasets usually use a MapReduce based implementation that runs on a cluster of computing nodes. In this paper we address this circumstance by identifying the resource consuming parts of a reasoner process and providing a solution for a more efficient implementation in terms of memory consumption. As a basis we use a rule-based reasoner concept from our previous work. In detail, we are going to introduce an approach for a memory efficient RETE algorithm implementation. Furthermore, we introduce a compressed triple-index structure that can be used to identify duplicate triples and only needs a few bytes to represent a triple. Based on these concepts we show that it is possible to apply all RDFS rules to more than 1 billion triples on a single laptop reaching a throughput, that is comparable or even higher than state of the art MapReduce based reasoner. Thus, we show that the resources needed for large scale lightweight reasoning can massively be reduced.".
- 35 hasAuthorList authorList.
- 35 isPartOf proceedings.
- 35 keyword "GPU".
- 35 keyword "Large scale reasoning".
- 35 keyword "RETE algorithm".
- 35 keyword "memory efficient".
- 35 keyword "rule-based reasoning".
- 35 keyword "triple compression".
- 35 title "Large scale rule-based Reasoning using a Laptop".
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- item-1 hasContent martin-peters.
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- item-2 hasContent sabine-sachweh.
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