SpiderStore: Exploiting Main Memory for Efficient RDF Graph Representation and Fast Querying

The constant growth of available RDF data requires fast and ecient querying facilities of graph data. So far, such data sets have been stored by using mapping techniques from graph structures to relational models, secondary memory structures or even complex main memory based models. We present the main memory database SpiderStore which is capable of eciently managing large RDF data sets and providing powerful and fast SPARQL processing facilities. The SpiderStore storage concept aims at storing the graph structure in main memory without performing any complex mappings. Therefore it exploits the natural web-structure of RDF by using fast and random access to main memory. The abandonment of additional mappings or meta-information therefore leads to a signicant performance gain compared to other common RDF stores.

[1]  V. S. Subrahmanian,et al.  GRIN: A Graph Based RDF Index , 2007, AAAI.

[2]  ZhaoHui Tang,et al.  Cost-based Selection of Path Expression Processing Algorithms in Object-Oriented Databases , 1996, VLDB.

[3]  James A. Hendler,et al.  Matrix "Bit" loaded: a scalable lightweight join query processor for RDF data , 2010, WWW '10.

[4]  Gerhard Weikum,et al.  WWW 2007 / Track: Semantic Web Session: Ontologies ABSTRACT YAGO: A Core of Semantic Knowledge , 2022 .

[5]  Krys J. Kochut,et al.  BRAHMS: A WorkBench RDF Store and High Performance Memory System for Semantic Association Discovery , 2005, SEMWEB.

[6]  Gerhard Weikum,et al.  RDF-3X: a RISC-style engine for RDF , 2008, Proc. VLDB Endow..

[7]  Abraham Bernstein,et al.  OptARQ: A SPARQL Optimization Approach based on Triple Pattern Selectivity Estimation , 2007 .

[8]  Gerhard Weikum,et al.  Scalable join processing on very large RDF graphs , 2009, SIGMOD Conference.

[9]  Jürgen Umbrich,et al.  YARS2: A Federated Repository for Querying Graph Structured Data from the Web , 2007, ISWC/ASWC.

[10]  Alfons Kemper,et al.  HyPer: HYbrid OLTP&OLAP High PERformance Database System , 2010 .

[11]  Dave Reynolds,et al.  Efficient RDF Storage and Retrieval in Jena2 , 2003, SWDB.

[12]  Christian Bizer,et al.  The Berlin SPARQL Benchmark , 2009, Int. J. Semantic Web Inf. Syst..

[13]  Frank van Harmelen,et al.  Sesame: A Generic Architecture for Storing and Querying RDF and RDF Schema , 2002, SEMWEB.

[14]  Dave Reynolds,et al.  SPARQL basic graph pattern optimization using selectivity estimation , 2008, WWW.

[15]  Orri Erling,et al.  RDF Support in the Virtuoso DBMS , 2007, CSSW.

[16]  B. E. Eckbo,et al.  Appendix , 1826, Epilepsy Research.

[17]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[18]  Martin L. Kersten,et al.  Column-store support for RDF data management: not all swans are white , 2008, Proc. VLDB Endow..

[19]  Vassilis Christophides,et al.  Benchmarking Database Representations of RDF/S Stores , 2005, SEMWEB.

[20]  Daniel J. Abadi,et al.  Scalable Semantic Web Data Management Using Vertical Partitioning , 2007, VLDB.

[21]  Jens Lehmann,et al.  DBpedia: A Nucleus for a Web of Open Data , 2007, ISWC/ASWC.