Using Semantic Web technologies in the development of data warehouses: A systematic mapping

Gacitúa, Ricardo; Mazón, José-Norberto; Cravero, Ania

doi:10.1002/widm.1293

Cited by 4 publications

(3 citation statements)

References 53 publications

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“…It is outdated and hence not supported by most currently available commercial products like Microsoft SQL Server and Oracle. Therefore, it is required to design an efficient query translation model that supports low-end recursion functionality for various products rather than involving only the advanced DBMS features of the most sophisticated systems [27,28]. Secondly, SQL queries through sql'99 recursions generated from the translation algorithms are usually complex in structure and large.…”

Section: 2mentioning

confidence: 99%

Schema-Based Mapping Approach for Data Transformation to Enrich Semantic Web

Natarajan

Subramaniyaswamy

Teekaraman

et al. 2021

Wireless Communications and Mobile Computing

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Modern web wants the data to be in Resource Description Framework (RDF) format, a machine-readable form that is easy to share and reuse data without human intervention. However, most of the information is still available in relational form. The existing conventional methods transform the data from RDB to RDF using instance-level mapping, which has not yielded the expected results because of poor mapping. Hence, in this paper, a novel schema-based RDB-RDF mapping method (relational database to Resource Description Framework) is proposed, which is an improvised version for transforming the relational database into the Resource Description Framework. It provides both data materialization and on-demand mapping. RDB-RDF reduces the data retrieval time for nonprimary key search by using schema-level mapping. The resultant mapped RDF graph presents the relational database in a conceptual schema and maintains the instance triples as data graph. This mechanism is known as data materialization, which suits well for the static dataset. To get the data in a dynamic environment, query translation (on-demand mapping) is best instead of whole data conversion. The proposed approach directly converts the SPARQL query into SQL query using the mapping descriptions available in the proposed system. The mapping description is the key component of this proposed system which is responsible for quick data retrieval and query translation. Join expression introduced in the proposed RDB-RDF mapping method efficiently handles all complex operations with primary and foreign keys. Experimental evaluation is done on the graphics designer database. It is observed from the result that the proposed schema-based RDB-RDF mapping method accomplishes more comprehensible mapping than conventional methods by dissolving structural and operational differences.

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Section: 2mentioning

confidence: 99%

Schema-Based Mapping Approach for Data Transformation to Enrich Semantic Web

Natarajan

Subramaniyaswamy

Teekaraman

et al. 2021

Wireless Communications and Mobile Computing

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“…In a similar context, Urdidiales, et al [Urdidiales-Nieto et al 2017] wrote a survey on the search of web services for the integration of biological databases. Gacitua et al [Gacitua et al 2019] provided a systematic review on semantic web technologies and discussed application to data warehouses or other industrial uses.…”

Section: Related Workmentioning

confidence: 99%

Semantic Search to Foster Scientific Findability: A Systematic Literature Review

Gottardi

Medeiros

Reis

2021

jidm

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One of the main goals of the Open Science movement is to leverage scientific collaboration through, among others, promoting the sharing and reuse of research outputs, such as publications, data and software. Sharing is enabled by public and accessible scientific repositories where these outputs are managed throughout their lifecycle. In this context, finding these digital artifacts has become a key problem. Semantic search mechanisms have risen as a means to solve this issue. However, implementing and integrating them into scientific repositories presents many challenges. This article presents a systematic literature review of research efforts on mechanisms for supporting search for scientific papers, data and processes. Our investigation is based on extracting and analyzing the entire contents of nine digital libraries using the associated search engines – in alphabetical order: ACM Digital Library, arXiV, Engineering Village, IEEE Xplore, SBC OpenLib, Springer Link, Scopus, Wiley Online Library and Web of Science. After retrieving a combined amount of 5012 documents, we identified 2054 unique papers that were used as a basis for our analysis. Our findings provide, among others, a new categorization of literature on search and discuss unexplored gaps, thereby contributing to advancing research on semantic search mechanisms to support Open Science.

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“…To compensate this problem, one research direction is turning to ontologies, sets of concepts and relationships from the Semantic Web. A detailed review of the research on the role of Semantic Web technologies in data warehousing is provided in [3].…”

Section: Introductionmentioning

confidence: 99%

The potential of semantic paradigm in warehousing of big data

2019

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Big data have analytical potential that was hard to realize with available technologies. After new storage paradigms intended for big data such as NoSQL databases emerged, traditional systems got pushed out of the focus. The current research is focused on their reconciliation on different levels or paradigm replacement. Similarly, the emergence of NoSQL databases has started to push traditional (relational) data warehouses out of the research and even practical focus. Data warehousing is known for the strict modelling process, capturing the essence of the business processes. For that reason, a mere integration to bridge the NoSQL gap is not enough. It is necessary to deal with this issue on a higher abstraction level during the modelling phase. NoSQL databases generally lack clear, unambiguous schema, making the comprehension of their contents difficult and their integration and analysis harder. This motivated involving semantic web technologies to enrich NoSQL database contents by additional meaning and context. This paper reviews the application of semantics in data integration and data warehousing and analyses its potential in integrating NoSQL data and traditional data warehouses with some focus on document stores. Also, it gives a proposal of the future pursuit directions for the big data warehouse modelling phases.

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Using Semantic Web technologies in the development of data warehouses: A systematic mapping

Cited by 4 publications

References 53 publications

Schema-Based Mapping Approach for Data Transformation to Enrich Semantic Web

Schema-Based Mapping Approach for Data Transformation to Enrich Semantic Web

Semantic Search to Foster Scientific Findability: A Systematic Literature Review

The potential of semantic paradigm in warehousing of big data

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