A demonstrator tool to provide the network operator with microservices based on big data and semantic web technologies

2017 ◽  
Vol 1 (15) ◽  
pp. 137-142 ◽  
Author(s):  
E. Bionda ◽  
F. Belloni ◽  
R. Chiumeo ◽  
D. Della Giustina ◽  
D. Pala ◽  
...  
Keyword(s):  
Big Data ◽  
Semantic Web ◽  
Network Operator ◽  
Semantic Web Technologies ◽  
Web Technologies

scholarly journals Semantic Web technologies for the big data in life sciences

2014 ◽  
Vol 8 (4) ◽  
pp. 192-201 ◽  
Author(s):  
Hongyan Wu ◽  
Atsuko Yamaguchi
Keyword(s):  
Big Data ◽  
Semantic Web ◽  
Life Sciences ◽  
Semantic Web Technologies ◽  
Web Technologies

A State of the Art on Big Data With Semantic Web Technologies

2020 ◽  
pp. 195-216
Author(s):  
Satyaveer Singh ◽  
Mahendra Singh Aswal
Keyword(s):  
Big Data ◽  
Semantic Web ◽  
Database Management ◽  
High Volume ◽  
Semantic Technologies ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
The Past ◽  
Digital World ◽  
Data Heterogeneity

We live in a digital world where a large amount of data is being generated rapidly by various diverse sources with an unprecedented rate. The term Big Data has been coined to represent a large amount of data. But Big Data could not be processed and analysed by traditional database management systems. A number of challenges such as data heterogeneity and diversity are being faced by enterprises due to high volume, variety, and velocity of Big Data. Since the past few years, some research efforts have been attempted to integrate semantic web technologies such as ontologies with Big Data. This integration is paving the way to deal with various issues that are related to the processing of Big Data. This chapter firstly uncovers the fundamentals of Big Data, its characteristics and opportunities, challenges, related current tools, and technologies. Secondly, it tries to highlight the integration of Big Data with semantic web technologies. The promising research is going on to tackle volume and velocity of Big Data by using semantic technologies.

scholarly journals A Semantic-Based Approach for Managing Healthcare Big Data: A Survey

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Rafat Hammad ◽  
Malek Barhoush ◽  
Bilal H. Abed-alguni
Keyword(s):  
Big Data ◽  
Information Systems ◽  
Semantic Web ◽  
Semantic Web Technologies ◽  
Healthcare Information ◽  
Healthcare Information Systems ◽  
Web Technologies ◽  
New Methods ◽  
Points Of View ◽  
Different Sources

Healthcare information systems can reduce the expenses of treatment, foresee episodes of pestilences, help stay away from preventable illnesses, and improve personal life satisfaction. As of late, considerable volumes of heterogeneous and differing medicinal services data are being produced from different sources covering clinic records of patients, lab results, and wearable devices, making it hard for conventional data processing to handle and manage this amount of data. Confronted with the difficulties and challenges facing the process of managing healthcare big data such as volume, velocity, and variety, healthcare information systems need to use new methods and techniques for managing and processing such data to extract useful information and knowledge. In the recent few years, a large number of organizations and companies have shown enthusiasm for using semantic web technologies with healthcare big data to convert data into knowledge and intelligence. In this paper, we review the state of the art on the semantic web for the healthcare industry. Based on our literature review, we will discuss how different techniques, standards, and points of view created by the semantic web community can participate in addressing the challenges related to healthcare big data.

scholarly journals Semantics for Big Data

AI Magazine ◽  
2015 ◽  
Vol 36 (1) ◽  
pp. 3-4 ◽  
Author(s):  
Frank Van Harmelen ◽  
James A. Hendler ◽  
Pascal Hitzler ◽  
Krzysztof Janowicz
Keyword(s):  
Big Data ◽  
Semantic Web ◽  
Editorial Introduction ◽  
Semantic Web Technologies ◽  
Web Technologies

This editorial introduction summarizes the seven guest-edited contributions to AI Magazine that explore opportunities and challenges arising from transferring and adapting semantic web technologies to the big data quest.

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