Querying Heterogeneous Relational Database using SPARQL

Wang, Jinpeng; Zhang, Miao; Zhang, Yafei; Zhou, Bo

doi:10.1109/icis.2009.11

Cited by 9 publications

(6 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors presented the solution for heterogeneous RDB databases, but not dealing with unstructured databases where the syntactical problem occurs. The syntactical problem is addressed in section III and V. The article [54] discusses the query related problems to integrate the relational databases. The authors suggested that SPARQL graph patterns give the same principle for mapping the relational schema using RDF ontology.…”

Section: Shortcomings According To Our Approachmentioning

confidence: 99%

Semantic Integration of Heterogeneous Databases of Same Domain Using Ontology

Asfand-e-yar

Ali

2020

IEEE Access

View full text Add to dashboard Cite

Heterogeneous database integration is the study of integrating data from multiple databases. Integrating the heterogeneous database of the same domain has three main challenges that make the heterogeneity problem difficult to solve. The three problems are Semantic, Syntactic and Structural Heterogeneity. Conventional heterogeneous database integration schemes, like De-duplication Techniques, Data Warehouse, and Information Retrieval (IR) Search technique lack the capability to solve the integration of databases completely. The only reason is they cannot deal with Semantic heterogeneity problems efficiently. The semantic Web ontology model is experimented and discussed in the article, which is based on the query execution model. The ontology modeling is divided into two phases, initially to translate the database rules according to ontology rules to find an abstract ontology model. Secondly, to extend the abstract ontology model according to the databases. The method facilitates to apply similarly SPQRAL queries to search the data in the databases. Therefore, the Jena API is used to retrieve semantically similar records. The experiment is based on the two heterogeneous Universities Library Databases. The results show the effectiveness and scalability of the methodology.

show abstract

Section: Shortcomings According To Our Approachmentioning

confidence: 99%

Semantic Integration of Heterogeneous Databases of Same Domain Using Ontology

Asfand-e-yar

Ali

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The second technique is query-based integration [13]. Even though it allows the user to query and retrieve data from different sources by a single query, the query is complex and it lacks the transparency of data location and integration to users.…”

Section: Fig 1 Data Integration Classificationmentioning

confidence: 99%

“…The federated database integration provides a uniform and central access to query and retrieve data [13]. This technique is more scalable and flexible than previous techniques [16] since there is no need for a centralized repository.…”

Section: Fig 1 Data Integration Classificationmentioning

confidence: 99%

“…Web service integration provides extensibility and flexibility features for data integration. Nowadays, this technique is used by many Bioinformatics databases [13], [19], [20]. For example, the National Center for Biotechnology Information (NCBI) [21], European Bioinformatics Institute (EBI) [22], DNA Data Bank of Japan (DDJB) [23], BioMOBY [24], and PathPort [25] use web services techniques to collect and integrate data from their data-sources.…”

Section: Fig 1 Data Integration Classificationmentioning

confidence: 99%

See 1 more Smart Citation

Framework for Disease Outbreak Notification Systems with an Optimized Federation Layer

Azzedin¹,

Ghaleb²,

Mohammed³

et al. 2019

ijacsa

View full text Add to dashboard Cite

Data that is needed to detect outbreaks of known and unknown diseases is often gathered from sources that are scattered in many geographical locations. Often these scattered data exist in a wide variety of formats, structures, and models. The collection, pre-processing, and analysis of these data to detect potential disease outbreaks is very challenging, time-consuming and error-prone. To fight disease outbreaks, healthcare practitioners, epidemiologists and researchers need to access the scattered data in a secure and timely manner. They also require a uniform and logical framework or methodology to access the relevant data. In this paper, authors propose a federated framework for Disease Outbreak Notification Systems (DONSFed). Using advanced design and an XML technique patented in the US in 2016 by our team, the framework was tested and validated as part of this work. The proposed approach enables healthcare professionals to quickly and uniformly access data that is required to detect potential disease outbreaks. This research focuses on implementing a cloud-based prototype as a proof-of-concept to demonstrate the functionalities and to verify the concept of the proposed framework.

show abstract

“…[15] is another mapping effort that uses a set of well-defined rules, like [14], to map relational schema metadata into an equivalent RDFS ontology which is then written into an RDF/XML file. Several endeavors [16][17][18][19][20] are underway in the data integration arena as well that b http://www.w3.org/TR/rdf-schema/ Update-Enabled Triplification of Relational Data into Virtual RDF Stores 427 aim to present a semantically unified RDF model derived from multiple underlying heterogeneous databases.…”

Section: Related Workmentioning

confidence: 99%

Update-Enabled Triplification of Relational Data Into Virtual RDF Stores

Ramanujam

Khadilkar

Khan

et al. 2010

Int. J. Semantic Computing

View full text Add to dashboard Cite

The current buzzword in the Internet community is the Semantic Web initiative proposed by the W3C to yield a Web that is more flexible and self-adapting. However, for the Semantic Web initiative to become a reality, heterogeneous data sources need to be integrated in order to enable access to them in a homogeneous manner. Since a vast majority of data currently resides in relational databases, integrating relational data sources with semantic web technologies is at the top of the list of activities required to realize the semantic web vision. Several efforts exist that publish relational data as Resource Description Framework (RDF) triples; however almost all current work in this arena is uni-directional, presenting data from an underlying relational database into a corresponding virtual RDF store in a read-only manner. An enhancement over previous relational-to-RDF bridging work in the form of bi-directionality support is presented in this paper. The bi-directional bridge proposed here allows RDF data updates specified as triples to be propagated back into the underlying relational database as tuples. Towards this end, we present various algorithms to translate the triples to be updated/inserted/deleted into equivalent relational attributes/tuples whenever possible. Particular emphasis is laid, in this paper, on the translation and update propagation process for triples containing blank nodes and reification nodes, and a platform enhanced with our algorithms, called D2RQ++, through which bi-directional translation can be achieved, is presented.

show abstract

Querying Heterogeneous Relational Database using SPARQL

Cited by 9 publications

References 4 publications

Semantic Integration of Heterogeneous Databases of Same Domain Using Ontology

Semantic Integration of Heterogeneous Databases of Same Domain Using Ontology

Framework for Disease Outbreak Notification Systems with an Optimized Federation Layer

Update-Enabled Triplification of Relational Data Into Virtual RDF Stores

Contact Info

Product

Resources

About