Updating relational data via SPARQL/update

Hert, Matthias; Reif, Gerald; Gall, Harald C.

doi:10.1145/1754239.1754266

Cited by 19 publications

(18 citation statements)

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“…Bertails et al demonstrates the power of the mapping to treat all of the important features of SQL tables, like cardinality and NULLs, and to yield an RDF graph which preserves the relational information [37]. Hert et al illustrate ontology-based access to relational databases as discussed in [41]. It describes a mapping language, the translation algorithms, and a prototype implementation.…”

Section: Related Workmentioning

confidence: 99%

“…Numerous relational to RDF data conversion approaches are developed on top of tuple-based mapping or a modified version of it. Examples include the following: Relational.OWL [76], DataMaster [77], ROSEX [78], Automapper [79], FDR2 [80], CROSS [81], D2RQ [82], Tether [83], and OntoAccess [41]. Value-based mapping is the simplest form of the relational to RDF mapping approach that preserves primary key and foreign key relationships [75].…”

Section: Tuple-based and Value-based Mappingsmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Tuple-based and Value-based Mappingsmentioning

confidence: 99%

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

Hasan

Fox

Bisset

et al. 2017

J Healthc Inform Res

View full text Add to dashboard Cite

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“…R2RML (Das et al, 2012) a mapping language made a recommendation by W3C to make a standardized approach for RDB to RDF transformation. OntoAccess mediation platform based transformation language known as R3M (Hert, Reif, & Gall, 2010). As an update, attentive transformation language, it enables providing partial bidirectional query oriented RDF oriented contact to the RDB.…”

Section: Literature Reviewmentioning

confidence: 99%

E-Assessment Data Compatibility Resolution Methodology with Bidirectional Data Transformation

Malik

Ahmad

2017

EURASIA J MATH SCI T

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Electronic Assessment (E-Assessment) also known as computer aided assessment for the purposes involving diagnostic, formative or summative examining using data analysis. Digital assessments come commonly from social, academic, and adaptive learning in machine readable forms to deliver the machine scoring function. To achieve real-time and smart e-assessment, data modeling needs dramatic improvements at the level of representation which will improve examinees to gain prompt response instantly after attempting exams. Whereas, computer based inference to gain intelligence in assessing results through computations is becoming a useful feature in todays' testing systems. Induction of rule base linked data is desired to be reformed from the old tradition data model found either in spread sheet or relational database used for data storage. These data forms are essential to be converted into semantical annotated form to support Artificial Intelligence. This can be done with the use of Semantic Web data model Resource Description Framework (RDF) built-up using hierarchal and linked data representation. Updating assessment source data later for results is one of the hardest problem of all viabilities in traditional and semantically augmented systems when combined for evaluating. This study purposes a methodology of bidirectional data transformation back and forth from Relational Database (RDB) and RDF. A case study representing qualitative analysis of transforming student's results information into RDF store reforming data as ready to be analyzed. At the end of this study outcomes show how data updating becomes feasible by following proposed data transformation procedure.

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“…R3M: R3M [24] is the mapping language of the OntoAccess 4 mediation platform [22]. As an update-aware mapping language it enables bidirectional RDF-based access to the RDB, i.e., read and write access is supported.…”

Section: Mapping Languagesmentioning

confidence: 99%

A comparison of RDB-to-RDF mapping languages

Hert

Reif

Gall

2011

Proceedings of the 7th International Conference on Semantic Systems

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Mapping Relational Databases (RDB) to RDF is an active field of research. The majority of data on the current Web is stored in RDBs. Therefore, bridging the conceptual gap between the relational model and RDF is needed to make the data available on the Semantic Web. In addition, recent research has shown that Semantic Web technologies are useful beyond the Web, especially if data from different sources has to be exchanged or integrated. Many mapping languages and approaches were explored leading to the ongoing standardization effort of the World Wide Web Consortium (W3C) carried out in the RDB2RDF Working Group (WG). The goal and contribution of this paper is to provide a featurebased comparison of the state-of-the-art RDB-to-RDF mapping languages. It should act as a guide in selecting a RDB-to-RDF mapping language for a given application scenario and its requirements w.r.t. mapping features. Our comparison framework is based on use cases and requirements for mapping RDBs to RDF as identified by the RDB2RDF WG. We apply this comparison framework to the state-of-the-art RDB-to-RDF mapping languages and report the findings in this paper. As a result, our classification proposes four categories of mapping languages: direct mapping, read-only general-purpose mapping, readwrite general-purpose mapping, and special-purpose mapping. We further provide recommendations for selecting a mapping language. WG). The goal and contribution of this paper is to provide a feature-based comparison of the state-of-the-art RDB-to-RDF mapping languages. It should act as a guide in selecting a RDB-to-RDF mapping language for a given application scenario and its requirements w.r.t. mapping features. Our comparison framework is based on use cases and requirements for mapping RDBs to RDF as identified by the RDB2RDF WG. We apply this comparison framework to the state-of-the-art RDB-to-RDF mapping languages and report the findings in this paper. As a result, our classification proposes four categories of mapping languages: direct mapping, read-only general-purpose mapping, readwrite general-purpose mapping, and special-purpose mapping. We further provide recommendations for selecting a mapping language. A Comparison of RDB-to-RDF Mapping Languages

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Updating relational data via SPARQL/update

Cited by 19 publications

References 10 publications

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

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

E-Assessment Data Compatibility Resolution Methodology with Bidirectional Data Transformation

A comparison of RDB-to-RDF mapping languages

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