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The LDBC Social Network Benchmark: Interactive Workload
This paper describes the LDBC Social Network Benchmark (SNB), and presents database benchmarking innovation in terms of graph query functionality tested, correlated graph generation techniques, as well as a scalable benchmark driver on a workload with complex graph dependencies.
High quality, scalable and parallel community detection for large real graphs
Scalable Community Detection is proposed, a novel disjoint community detection algorithm that is able to run up to two orders of magnitude faster than practical existing solutions by exploiting the parallelism of current multi-core processors, enabling us to process graphs of unprecedented size in short execution times.
Dex: high-performance exploration on large graphs for information retrieval
- Norbert Martínez-Bazan, V. Muntés-Mulero, S. Gómez-Villamor, Jordi Nin, M. Sánchez-Martínez, J. Larriba-Pey
- Computer ScienceCIKM '07
- 6 November 2007
DEX is proposed and evaluated, a high performance graph database querying system that allows for the integration of multiple data sources and makes graph querying possible in different flavors, including link analysis, social network analysis, pattern recognition and keyword search.
Survey of Graph Database Performance on the HPC Scalable Graph Analysis Benchmark
- David Dominguez-Sal, P. Urbón-Bayes, Aleix Giménez-Vañó, S. Gómez-Villamor, Norbert Martínez-Bazan, J. Larriba-Pey
- Computer ScienceWAIM Workshops
- 15 July 2010
This paper evaluates the performance of four of the most scalable native graph database projects (Neo4j, Jena, HypergraphDB and DEX), and implements the full HPC Scalable Graph Analysis Benchmark, showing that in their current development status, DEX and Neo4j are the most efficient graph databases.
The linked data benchmark council: a graph and RDF industry benchmarking effort
An overview of the LDBC project including its goals and organization is presented, and so-called "choke-point" based benchmark development through which experts identify key technical challenges, and introduce them in the benchmark workload is introduced.
Dynamic count filters
- J. Aguilar-Saborit, Pedro Trancoso, V. Muntés-Mulero, J. Larriba-Pey
- Computer ScienceSGMD
- 1 March 2006
The Dynamic Count Filters (DCF) is proposed as a new dynamic and space-time efficient representation of CBF and shows to be faster and more space efficient than any previous proposal.
Efficient graph management based on bitmap indices
- Norbert Martínez-Bazan, Miquel Angel Aguila-Lorente, V. Muntés-Mulero, David Dominguez-Sal, S. Gómez-Villamor, J. Larriba-Pey
- Computer ScienceIDEAS '12
- 8 August 2012
The internals of DEX graph database is described, which is based on a representation of the graph and its attributes as maps and bitmap structures that can be loaded and unloaded efficiently from memory.
The memory performance of DSS commercial workloads in shared-memory multiprocessors
- Pedro Trancoso, J. Larriba-Pey, Zheng Zhang, J. Torrellas
- Computer ScienceProceedings Third International Symposium on High…
- 1 February 1997
This paper analyzes in detail the memory access patterns of several queries that are representative of Decision Support System (DSS) databases and shows that both Index and Sequential queries exhibit spatial locality and, therefore, can benefit from relatively long cache lines.
On the Use of Semantic Blocking Techniques for Data Cleansing and Integration
- Jordi Nin, V. Muntés-Mulero, Norbert Martínez-Bazan, J. Larriba-Pey
- Computer Science11th International Database Engineering and…
- 6 September 2007
This paper shows that exploiting the relationships established between one or more data sources, makes it possible to find a new sort of semantic blocking method that improves the number of hits and reduces the amount of review effort.
Shaping communities out of triangles
- Arnau Prat-Pérez, David Dominguez-Sal, J. M. Brunat, J. Larriba-Pey
- Computer ScienceCIKM
- 26 July 2012
A new community metric called WCC is defined that meets a minimum set of basic properties that guarantees communities with structure and cohesion and is experimentally shown to correctly quantifies the quality of communities and community partitions using real and synthetic datasets.