Ultrawrap: SPARQL execution on relational data

Sequeda, Juan F.; Miranker, Daniel P.

doi:10.1016/j.websem.2013.08.002

Cited by 108 publications

(73 citation statements)

References 29 publications

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“…Dealing with big data sets requires the data to remain in legacy databases, and that the translation to RDF be performed on demand through the xR2RML-based rewriting of SPARQL queries into the source database query language. In this regard, existing works related to RDBs should be leveraged [24,33].…”

Section: Discussionmentioning

confidence: 99%

Translation of Heterogeneous Databases into RDF, and Application to the Construction of a SKOS Taxonomical Reference

Michel

Djimenou

Faron-Zucker

et al. 2016

Lecture Notes in Business Information Processing

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Abstract. While the data deluge accelerates, most of the data produced remains locked in deep Web databases. For the linked open data to benefit from the potential represented by this huge amount of data, it is crucial to come up with solutions to expose heterogeneous databases as linked data. The xR2RML mapping language is an endeavor towards this goal: it is designed to map various types of databases to RDF, by flexibly adapting to heterogeneous query languages and data models while remaining free from any specific language. It extends R2RML, the W3C recommendation for the mapping of relational databases to RDF, and relies on RML for the handling of various data formats. In this paper we present xR2RML, we analyse data models of several modern databases as well as the format in which query results are returned, and we show how xR2RML translates any result data element into RDF, relying on existing languages such as XPath and JSONPath when necessary. We illustrate some features of xR2RML such as the generation of RDF collections and containers, and the ability to deal with mixed data formats. We also describe a real-world use case in which we applied xR2RML to build a SKOS thesaurus aimed at supporting studies on History of Zoology, Archaeozoology and Conservation Biology.

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Section: Discussionmentioning

confidence: 99%

Translation of Heterogeneous Databases into RDF, and Application to the Construction of a SKOS Taxonomical Reference

Michel

Djimenou

Faron-Zucker

et al. 2016

Lecture Notes in Business Information Processing

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show abstract

“…Therefore, ontology-based visual query formulation would not be a pragmatic solution for industry without technologies for ontology-based data access over relational databases. OBDA technologies, such as Ontop [9], Mastro [14], and Ultrawrap [43], make it possible to virtualise RDF graphs from relational databases and enable in-place access to relational data over ontologies without migrating or duplicating any data. Moreover, thanks to OBDA, while using well-established query optimisation and evaluation support available for traditional database systems, one could also (i) integrate data from multiple databases with different schemas by relating each to a common ontology, and (ii) utilise implicit information in query the answering process by relating the whole set of implied information with logical reasoning.…”

Section: Ontology-based Data Accessmentioning

confidence: 99%

Querying industrial stream-temporal data: An ontology-based visual approach1

Soylu

Giese

Schlatte

et al. 2017

AIS

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Abstract. An increasing number of sensors are being deployed in business-critical environments, systems, and equipment; and stream a vast amount of data. The operational efficiency and effectiveness of business processes rely on domain experts' agility in interpreting data into actionable business information. A domain expert has extensive domain knowledge but not necessarily skills and knowledge on databases and formal query languages. Therefore, centralised approaches are often preferred. These require IT experts to translate the information needs of domain experts into extract-transform-load (ETL) processes in order to extract and integrate data and then let domain experts apply predefined analytics. Since such a workflow is too time intensive, heavy-weight and inflexible given the high volume and velocity of data, domain experts need to extract and analyse the data of interest directly. Ontologies, i.e., semantically rich conceptual domain models, present an intelligible solution by describing the domain of interest on a higher level of abstraction closer to the reality. Moreover, recent ontology-based data access (OBDA) technologies enable end users to formulate their information needs into queries using a set of terms defined in an ontology. Ontological queries could then be translated into SQL or some other database query languages, and executed over the data in its original place and format automatically. To this end, this article reports an ontology-based visual query system (VQS), namely OptiqueVQS, how it is extended for a stream-temporal query language called STARQL, a user experiment with the domain experts at Siemens AG, and STARQL's query answering performance over a proof of concept implementation for PostgreSQL.

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“…Ultrawrap extract data from data sources that has triple-view or SQL schema format. On 2013 Ultrawrap enhanced and evaluated using two existing benchmark suites [51]. There are four main components in the Ultrawrap semantic data mapping tool.…”

Section: ) Ultrawrapmentioning

confidence: 99%

Semantic data mapping technology to solve semantic data problem on heterogeneity aspect

Yunianta

Barukab

Yusof

et al. 2017

Int. J. Adv. Intell. Informatics

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The diversity of applications developed with different programming languages, application/data architectures, database systems and representation of data/information leads to heterogeneity issues. One of the problem challenges in the problem of heterogeneity is about heterogeneity data in term of semantic aspect. The semantic aspect is about data that has the same name with different meaning or data that has a different name with the same meaning. The semantic data mapping process is the best solution in the current days to solve semantic data problem. There are many semantic data mapping technologies that have been used in recent years. This research aims to compare and analyze existing semantic data mapping technology using five criteria’s. After comparative and analytical process, this research provides recommendations of appropriate semantic data mapping technology based on several criteria’s. Furthermore, at the end of this research we apply the recommended semantic data mapping technology to be implemented with the real data in the specific application. The result of this research is the semantic data mapping file that contains all data structures in the application data source. This semantic data mapping file can be used to map, share and integrate with other semantic data mapping from other applications and can also be used to integrate with the ontology language.

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Ultrawrap: SPARQL execution on relational data

Cited by 108 publications

References 29 publications

Translation of Heterogeneous Databases into RDF, and Application to the Construction of a SKOS Taxonomical Reference

Translation of Heterogeneous Databases into RDF, and Application to the Construction of a SKOS Taxonomical Reference

Querying industrial stream-temporal data: An ontology-based visual approach1

Semantic data mapping technology to solve semantic data problem on heterogeneity aspect

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