Open Access

ARTICLE

Using Semantic Web Technologies to Improve the Extract Transform Load Model

Amena Mahmoud^1,*, Mahmoud Y. Shams², O. M. Elzeki³, Nancy Awadallah Awad⁴

1 Department of Computer Science, Kafrelshiekh University, Kafrelshiekh, Egypt
2 Department of Machine Learning, Kafrelsheikh University, Kafrelshiekh, Egypt
3 Department of Computer Science, Mansoura University, Mansoura, Egypt
4 Department of Computer and Information Systems, Sadat Academy for Management Sciences, Cairo, Egypt

* Corresponding Author: Amena Mahmoud. Email:

Computers, Materials & Continua 2021, 68(2), 2711-2726. https://doi.org/10.32604/cmc.2021.015293

Received 14 November 2020; Accepted 18 February 2021; Issue published 13 April 2021

Abstract

Semantic Web (SW) provides new opportunities for the study and application of big data, massive ranges of data sets in varied formats from multiple sources. Related studies focus on potential SW technologies for resolving big data problems, such as structurally and semantically heterogeneous data that result from the variety of data formats (structured, semi-structured, numeric, unstructured text data, email, video, audio, stock ticker). SW offers information semantically both for people and machines to retain the vast volume of data and provide a meaningful output of unstructured data. In the current research, we implement a new semantic Extract Transform Load (ETL) model that uses SW technologies for aggregating, integrating, and representing data as linked data. First, geospatial data resources are aggregated from the internet, and then a semantic ETL model is used to store the aggregated data in a semantic model after converting it to Resource Description Framework (RDF) format for successful integration and representation. The principal contribution of this research is the synthesis, aggregation, and semantic representation of geospatial data to solve problems. A case study of city data is used to illustrate the semantic ETL model’s functionalities. The results show that the proposed model solves the structural and semantic heterogeneity problems in diverse data sources for successful data aggregation, integration, and representation.

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Keywords

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