Efficient RDF Storage and Retrieval in Jena2

By K. Wilkinson, C. Sayers, Harumi A. Kuno, D. Reynolds. SWDB, 2003

564 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f199ad37ead55bcb34ef5f67cf6732098b6c45b2

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Citations in the corpus

The Internet of Things: A Survey from the Data-Centric Perspective (2013, unknown): “In these cases, giant triples tables are used in order to represent the underlying RDF data [11, 12, 18, 24, 49, 50, 85, 113, 114].”
A Native and Adaptive Approach for Unified Processing of Linked Streams and Linked Data (2011, methodology): “A fair amount of work on storage and query processing for Linked Data is available, including Sesame [13], Jena [38], RISC-3X [28], YARS2 [23], and Oracle Database Semantic Technologies [16].”
A Distributed Graph Engine for Web Scale RDF Data (2013, methodology): “We conduct comprehensive experimental studies on real life, web scale RDF data to demonstrate the effectiveness of our approach.”
gStore: Answering SPARQL Queries via Subgraph Matching (2011, background): “Existing RDF storage systems, such as Jena [23], Yars2 [11] and Sesame 2.0 [5], cannot work well in large RDF datasets (such as Yago dataset).”
Scalable SPARQL querying of large RDF graphs (2011, background): “Efficient RDF Storage and Retrieval in Jena2.”
An Efficient SQL-based RDF Querying Scheme (2005, result): “Finally, Jena2’s [12] property tables (clustering multiple properties) are in many ways similar to subjectproperty matrices.”
SW-Store: a vertically partitioned DBMS for Semantic Web data management (2009, methodology): “Their utilization of property tables is slightly different from Jena2 in that they are not used as a primary storage structure, but rather as an auxiliary data structure—a materialized view—that can be used to speed up specific types of queries.”
Binary RDF representation for publication and exchange (HDT) (2013, methodology): “Some approaches store RDF in a relational database and perform SPARQL queries through SQL, such as Jena-TDB [51], Virtuoso [52], and the column-oriented databases C-Store [53] and Monet-DB [38].”
Hexastore: sextuple indexing for semantic web data management (2008, methodology): “Besides, systems such as Jena [46] [19] [45] attempt to create relational-like property tables out of RDF data; these tables gather together information about multiple properties over a list of subjects.”
Top-k Exploration of Query Candidates for Efficient Keyword Search on Graph-Shaped (RDF) Data (2009, methodology): “Using standard rewriting techniques (see [3], [17], [4]), the engine translates the query to SQL and returns the answers as computed by the underlying database engine.”
Scalable Semantic Web Data Management Using Vertical Partitioning (2007, background): “Their utilization of property tables is slightly different from Jena2 in that they are not used as a primary storage structure, but rather as an auxiliary data structure – a materialized view – that can be used to speed up specific types of queries.”
RDF-3X: a RISC-style engine for RDF (2008, methodology): “Most publicly accessible RDF systems have mapped RDF triples onto relational tables (e.g., RDFSuite [2, 32], Sesame [8, 28], Jena [23, 46], the C-Store-based RDF engine of [1], and also Oracle’s RDF MATCH implementation [11]).”
The RDF-3X engine for scalable management of RDF data (2010, methodology): “We first tried the popular Jena2 system [64] which came out of the HP Labs Semantic Web Programme.”
Optimized index structures for querying RDF from the Web (2005, background): “Systems such as Jena2 [17], Sesame [5], rdfDB [7], Redland [2], Kowari 1 , FORTH RDF Suite [1] and others provide a storage infrastructure for RDF data.”