The linked data benchmark council

Angles, Renzo; Boncz, Peter; Larriba-Pey, Josep Lluís; Fundulaki, Irini; Neumann, Thomas; Erling, Orri; Neubauer, Peter; Martínez-Bazán, Norbert; Kotsev, Venelin; Toma, Ioan

doi:10.1145/2627692.2627697

Cited by 55 publications

(12 citation statements)

References 6 publications

(5 reference statements)

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“…In this paper, we identify and analyze choke points of the topic benchmarks and discuss the possibilities to optimize such benchmarks. Choke points can ensure that existing techniques are present in a system, but can also be used to reward future systems that improve performance on still open technical challenges [2].…”

Section: Benchmark Development Methodologymentioning

confidence: 99%

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Topic profiling benchmarks in the linked open data cloud: Issues and lessons learned

Spahiu

Maurino

Meusel

2019

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Topical profiling of the datasets contained in the Linking Open Data (LOD) cloud has been of interest since such kind of data became available within the Web. Different automatic classification approaches have been proposed in the past, in order to overcome the manual task of assigning topics for each and every individual (new) dataset. Although the quality of those automated approaches is comparably sufficient, it has been shown, that in most cases a single topical label per dataset does not capture the topics described by the content of the dataset. Therefore, within the following study, we introduce a machine-learning based approach in order to assign a single topic, as well as multiple topics for one LOD dataset and evaluate the results. As part of this work, we present the first multi-topic classification benchmark for LOD cloud datasets, which is freely accessible. In addition, the article discusses the challenges and obstacles, which need to be addressed when building such a benchmark.

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Section: Benchmark Development Methodologymentioning

confidence: 99%

“…should be published adopting W3C 1 standards in the RDF 2 format and providing a SPARQL 3 endpoint to query them. Over the last few years, the overall spread of datasets being part of the LOD cloud has increased from 12 datasets in 2007, to more than 1 000 datasets as of April 2014 [35], where the overall number is constantly increasing.…”

Section: Introductionmentioning

confidence: 99%

Topic profiling benchmarks in the linked open data cloud: Issues and lessons learned

Spahiu

Maurino

Meusel

2019

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“…Experiments were run on three different datasets, drawn from different sources and of different sizes. Two datasets, the Lehigh University Benchmark (LUBM) dataset [10] and the Linked Data Benchmark Council (LDBC) dataset [2] are synthetic datasets, while the Advogato dataset [1] is a real-world dataset. All experiments were conducted using the latest version of Neo4j available at the time, Neo4j 2.3.0-M01.…”

Section: Discussionmentioning

confidence: 99%

Investigations on Path Indexing for Graph Databases

Sumrall¹,

Fletcher

Poulovassilis

et al. 2017

Euro-Par 2016: Parallel Processing Workshops

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Abstract. Graph databases have become an increasingly popular choice for the management of the massive network data sets arising in many contemporary applications. We investigate the effectiveness of path indexing for accelerating query processing in graph database systems, using as an exemplar the widely used open-source Neo4j graph database. We present a novel path index design which supports efficient ordered access to paths in a graph dataset. Our index is fully persistent and designed for external memory storage and retrieval. We also describe a compression scheme that exploits the limited differences between consecutive keys in the index, as well as a workload-driven approach to indexing. We demonstrate empirically the speed-ups achieved by our implementation, showing that the path index yields query run-times from 2x up to 8000x faster than Neo4j. Empirical evaluation also shows that our scheme leads to smaller indexes than using general-purpose LZ4 compression. The complete stand-alone implementation of our index, as well as supporting tooling such as a bulk-loader, are provided as open source for further research and development.

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“…LDBC. The Linked Data Benchmark Council 18 (LDBC) [Angles et al 2014] had the goal of developing open source, yet industrial grade benchmarks for RDF and graph databases. In the Semantic Web domain, it released the Semantic Publishing Benchmark (SPB) [LDBC 2015] that has been inspired by the Media/Publishing industry (namely BBC 19 ).…”

Section: Semantic Webmentioning

confidence: 99%

Graph Generators

et al. 2020

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The abundance of interconnected data has fueled the design and implementation of graph generators reproducing real-world linking properties or gauging the effectiveness of graph algorithms, techniques, and applications manipulating these data. We consider graph generation across multiple subfields, such as Semantic Web, graph databases, social networks, and community detection, along with general graphs. Despite the disparate requirements of modern graph generators throughout these communities, we analyze them under a common umbrella, reaching out the functionalities, the practical usage, and their supported operations. We argue that this classification is serving the need of providing scientists, researchers, and practitioners with the right data generator at hand for their work. This survey provides a comprehensive overview of the state-of-the-art graph generators by focusing on those that are pertinent and suitable for several data-intensive tasks. Finally, we discuss open challenges and missing requirements of current graph generators along with their future extensions to new emerging fields.

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The linked data benchmark council

Cited by 55 publications

References 6 publications

Topic profiling benchmarks in the linked open data cloud: Issues and lessons learned

Topic profiling benchmarks in the linked open data cloud: Issues and lessons learned

Investigations on Path Indexing for Graph Databases

Graph Generators

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