The contribution of linked open data to augment a traditional data warehouse

Berkani, Nabila; Bellatreche, Ladjel; Khouri, Selma; Ordóñez, Carlos

doi:10.1007/s10844-020-00594-w

Cited by 9 publications

(4 citation statements)

References 25 publications

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“…In order to perfectly meet the needs of business leaders in the big data era, several researches have been made to ensure the enrichment of the decisional systems with different types of big data such as semantic web [4], social data [5,6], NoSQL data [7], data lakes [8] and LOD [9,10,11]. However, neither of these approaches has implemented a decision-making system based on a big data warehouse, which is ready at all times to support the increased integration of big data so as to constantly satisfy the functional exigencies of decision-makers.…”

Section: Related Workmentioning

confidence: 99%

Optimization of Columnar NoSQL Data Warehouse Model with Clarans Clustering Algorithm

Soussi

2023

cai

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In order to perfectly meet the needs of business leaders, decision-makers have resorted to the integration of external sources (such as Linked Open Data) in the decision-making system in order to enrich their existing data warehouses with new concepts contributing to bring added value to their organizations, enhance its productivity and retain its customers. However, the traditional data warehouse environment is not suitable to support external Big Data. To deal with this new challenge, several researches are oriented towards the direct conversion of classical relational data warehouse to a columnar NoSQL data warehouse, whereas the existing advanced works based on clustering algorithms are very limited and have several shortcomings. In this context, our paper proposes a new solution that conceives an optimized columnar data warehouse based on CLARANS clustering algorithm that has proven its effectiveness in generating optimal column families. Experimental results improve the validity of our system by performing a detailed comparative study between the existing advanced approaches and our proposed optimized method.

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Section: Related Workmentioning

confidence: 99%

Optimization of Columnar NoSQL Data Warehouse Model with Clarans Clustering Algorithm

Soussi

2023

cai

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“…In recent years, a large number of Linked Data (RDF triples) has been published on the Web and which further kept growing at an exponential rate. The advent of the LOD era presents an excellent opportunity to augment traditional data warehouses with a new V dimension: value (Berkani et al, 2020). Recently, many researchers (Dimitrakis et al, 2020;Berkani et al, 2020;Fernández et al, 2017) have been attracted by Linked Data and worked to build a platform to retrieve relevant information from Linked Data.…”

Section: Related Workmentioning

confidence: 99%

“…The advent of the LOD era presents an excellent opportunity to augment traditional data warehouses with a new V dimension: value (Berkani et al, 2020). Recently, many researchers (Dimitrakis et al, 2020;Berkani et al, 2020;Fernández et al, 2017) have been attracted by Linked Data and worked to build a platform to retrieve relevant information from Linked Data. To reach that challenge, some of the following semantic search engines have been published: Falcons (Cheng & Qu, 2009), Swoogle (Ding et al, 2004), Watson (d'Aquin et al, 2007, Sindice (Oren et al, 2008) and Semantic Web Search Engine (SWSE) (Hogan et al, 2011).…”

Section: Related Workmentioning

confidence: 99%

LOD search engine: A semantic search over linked data

2021

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In the last few years, there has been a significant growth in the amount of data published in RDF and adoption of Linked Data principles. Every day, a large number of people and communities contribute to the publication of datasets as Linked Data on Linked Open Data (LOD) cloud. Due to a large size of LOD cloud on the Web and the RDF representation of linked dataset, searching and retrieving relevant data on the Web is a major challenge. Because the data is published in RDF triple format, i.e. an interlinked structure, traditional search engines are unable to perform searches on Linked Data. This article introduces LOD search engine, a novel semantic search engine that searches on Semantic Web documents (such as Linked Data or triples) to retrieve a set of relevant information based on user queries. For searching over triples, we proposed two semantic search methods: Forward Search and Backward Search. To improve search results, two new ranking methods have also been introduced: Domain Ranking and Triple Ranking. The proposed LOD search engine produced remarkable results and outperformed other semantic search engines. In the best-case scenario, the proposed LOD search engine outperforms the swoogle and falcons by 22.35%, 43.38% and 33.18% in terms of precision, recall, and F-Measure respectively.

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“…One of the major goal of Data Science is the extraction of significant value from Big Data [ 6 ]. Data, Information and Knowledge play a crucial role in getting this added value [ 2 , 3 , 5 ].…”

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confidence: 99%