Hexastore: sextuple indexing for semantic web data management

By Cathrin Weiss, Panagiotis Karras, Abraham Bernstein. Proceedings of the VLDB Endowment, 2008

724 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/13268315bd19a32cc367fe4839f2239818c4639c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Michael Schmidt
2. M. Tamer Özsu
3. Haixun Wang
4. Bin Shao
5. Thomas Neumann
6. Katja Hose
7. Dongyan Zhao
8. Lei Chen

In Computer Networks and Communications

1. Michael Schmidt
2. M. Tamer Özsu
3. Thomas Neumann
4. Bin Shao
5. Haixun Wang
6. Dongyan Zhao
7. Lei Zou
8. Jinghui Mo

In Signal Processing

1. Michael Schmidt
2. M. Tamer Özsu
3. Bin Shao
4. Haixun Wang
5. Lei Chen
6. Dongyan Zhao
7. Lei Zou
8. Jinghui Mo

Citations in the corpus

• 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.”
• SW-Store: a vertically partitioned DBMS for Semantic Web data management (2009, background): “One example of such auxiliary data structures is presented in the recent Hexastore paper [52], which, in addition to storing one vertical partition per property, also stores vertical partitions for subjects and objects as well.”
• The Internet of Things: A Survey from the Data-Centric Perspective (2013, neutral): “It was argued in [110] that the scheme in [1] is also not particularly effective, unless the properties appear as bound variables.”
• SP^2Bench: A SPARQL Performance Benchmark (2008, background): “These approaches comprise a wide range of optimization techniques, including normal forms [ 4], graph pattern reordering based on selectivity estimations [5] (similar to relational join reordering), syntactic rewrit ing [6], specialized indices [7], [8] and storage schemes [9], [10], [11], [12], [13”
• Enabling the geospatial Semantic Web with Parliament and GeoSPARQL (2012, background): “…bases, sometimes called triple stores, are capable of better handling several types of problems at which relational databases struggle or are not intended to perform: queries with many joins across entities [32], queries with variable properties [32], and ontological inference on datasets.”
• Scalable SPARQL querying of large RDF graphs (2011, background): “RDFSuite [7], and continued through many new systems that have been proposed in the last few years [12, 4, 24, 32, 29, 25, 15].”
• gStore: Answering SPARQL Queries via Subgraph Matching (2011, methodology): “Although these existing RDF query engines, such as RDF-3x [12], Hexastore [22] and SW-store [1], are designed to address the scalability of SPARQL queries, they have some common limitations: (1) they cannot support SPARQL with wildcards in a scalable manner; and (2) it is very difficult for some…”
• FedX: Optimization Techniques for Federated Query Processing on Linked Data (2011, background): “While there are efficient solutions to query processing in the context of RDF for local, centralized repositories [5, 15, 23], research contributions and frameworks for federated query processing are still in the early stages.”
• The RDF-3X engine for scalable management of RDF data (2010, background): “In contrast to the arguments that [1] gives against the " giant-triples-table " approach, both RDF-3X [37] and HexaStore [61] recently showed how to successfully employ a triples table with excellent performance.”
• Binary RDF representation for publication and exchange (HDT) (2013, background): “Hexastore [55] and RDF-3X [56] are well-known systems which create indexes for all ordering combinations (SPO, SOP, PSO, POS, OPS, OSP).”