Building an efficient RDF store over a relational database

Bornea, Mihaela A.; Dolby, Julian; Kementsietsidis, Anastasios; Srinivas, Kavitha; Dantressangle, Patrick; Udrea, Octavian; Bhattacharjee, Bishwaranjan

doi:10.1145/2463676.2463718

Cited by 170 publications

(131 citation statements)

References 16 publications

(18 reference statements)

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“…Readers can refer to W3C documents (such as RDF primer 7 and SPARQL 1.1 Recommendation 8 ) for more details.…”

Section: Preliminariesmentioning

confidence: 99%

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Graph-Based RDF Data Management

Zou

Özsu

2017

Data Sci. Eng.

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The increasing size of RDF data requires efficient systems to store and query them. There have been efforts to map RDF data to a relational representation, and a number of systems exist that follow this approach. We have been investigating an alternative approach of maintaining the native graph model to represent RDF data, and utilizing graph database techniques (such as a structureaware index and a graph matching algorithm) to address RDF data management. Since 2009, we have been developing a set of graph-based RDF data management systems that follow this approach: gStore, gStore-D and gAnswer. The first two are designed to support efficient SPARQL query evaluation in a centralized and distributed/parallel environments, respectively, while the last one aims to provide an easy-to-use interface (natural language question/answering) for users to access a RDF repository. In this paper, we give an overview of these systems and also discuss our design philosophy.

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“…Readers can refer to W3C documents (such as RDF primer 7 and SPARQL 1.1 Recommendation 8 ) for more details.…”

Section: Preliminariesmentioning

confidence: 99%

“…Consequently, it stores ''related'' properties in the same table. Example systems include Jena [31] and IBM's DB2RDF [7].…”

Section: Introductionmentioning

confidence: 99%

Graph-Based RDF Data Management

Zou

Özsu

2017

Data Sci. Eng.

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“…Multiple studies have addressed processing queries using mapping [44][45][46]. Bornea et al [44] describe novel query translation techniques as well.…”

Section: Related Workmentioning

confidence: 99%

EpiK: A Knowledge Base for Epidemiological Modeling and Analytics of Infectious Diseases

Hasan

Fox

Bisset

et al. 2017

J Healthc Inform Res

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Computational epidemiology seeks to develop computational methods to study the distribution and determinants of health-related states or events (including disease), and the application of this study to the control of diseases and other health problems. Recent advances in computing and data sciences have led to the development of innovative modeling environments to support this important goal. The datasets used to drive the dynamic models as well as the data produced by these models presents unique challenges owing to their size, heterogeneity and diversity. These datasets form the basis of effective and easy to use decision support and analytical environments. As a result, it is important to develop scalable data management systems to store, manage and integrate these datasets. In this paper, we develop EpiK-a knowledge base that facilitates the development of decision support and analytical environments to support epidemic science. An important goal is to develop a framework that links the input as well as output datasets to facilitate effective spatio-temporal and social reasoning that is critical in planning and intervention analysis before and during an epidemic. The data management framework links modeling workflow data and its metadata using a controlled vocabulary. The metadata captures information about storage, the mapping between the linked model and the physical layout, and relationships to support services. EpiK is designed to support agent-based modeling and analytics frameworks-aggregate models can be seen as special cases and are thus supported. We use semantic web technologies to create a representation of the datasets that encapsulates both the location and the schema heterogeneity. The choice of RDF as a representation language is motivated by the diversity and growth of the datasets that need to be integrated. A query bank is developed-the queries capture a broad range of questions that can be posed and answered during a typical case study pertaining to disease outbreaks. The queries are constructed using SPARQL Protocol and RDF Query Language (SPARQL) over the EpiK. EpiK can hide schema and location heterogeneity while efficiently supporting queries that span the computational epidemiology modeling pipeline: from model construction to simulation output. We show that the performance of benchmark queries varies significantly with respect to the choice of hardware underlying the database and resource description framework (RDF) engine.

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“…Similarly, the variables V are also assumed to be a disjoint union of countably infinite sets V C , V R , V I . Now, we define an OWL 2 QL BGP as a finite set of triple patterns representing OWL 2 QL axiom and assertion templates in the FSS such as: 3 Assuming that A = {ClassAssertion(:A, :a), ObjectPropertyAssertion(:T, :a, :b)}, it is not hard to see that B T ,A = {?x → :a}. Indeed, by the first assertion of A and the first two axioms of T , any model of (T , A) contains a domain element w (not necessarily among the individuals in A) such that ObjectPropertyAssertion(:R, :a, w) holds.…”

Section: Sparql Queries Under Owl Ql Entailment Regimementioning

confidence: 99%

“…Our experiments show that Ontop outperforms the reasoner-based systems for most of the queries over small datasets; over larger datasets the difference becomes dramatic, with Ontop demonstrating a solid performance even on 69 million triples in LUBM 500 . Finally, we note that, although Ontop was designed to work with existing relational databases, it is also applicable in the context of RDF triple stores, in which case approaches such as the one from [3] can be used to generate suitable relational schemas. Omitted proofs and evaluation details can be found in the full version at http://www.dcs.bbk.ac.uk/~michael/ISWC-14-v2.pdf.…”

Section: Introductionmentioning

confidence: 99%

Answering SPARQL Queries over Databases under OWL 2 QL Entailment Regime

Kontchakov

Rezk

Rodríguez-Muro

et al. 2014

The Semantic Web – ISWC 2014

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Abstract. We present an extension of the ontology-based data access platform Ontop that supports answering SPARQL queries under the OWL 2 QL direct semantics entailment regime for data instances stored in relational databases. On the theoretical side, we show how any input SPARQL query, OWL 2 QL ontology and R2RML mappings can be rewritten to an equivalent SQL query solely over the data. On the practical side, we present initial experimental results demonstrating that by applying the Ontop technologies-the tree-witness query rewriting, T -mappings compiling R2RML mappings with ontology hierarchies, and T -mapping optimisations using SQL expressivity and database integrity constraints-the system produces scalable SQL queries.

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Building an efficient RDF store over a relational database

Cited by 170 publications

References 16 publications

Graph-Based RDF Data Management

Graph-Based RDF Data Management

EpiK: A Knowledge Base for Epidemiological Modeling and Analytics of Infectious Diseases

Answering SPARQL Queries over Databases under OWL 2 QL Entailment Regime

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