Big Data and Databases

A special issue of Knowledge (ISSN 2673-9585).

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 3410

Special Issue Editors

Academic Director ESIC Business & Marketing School, Madrid, Spain
Interests: data analysis; predictive analytics; deep learning; machine learning; artificial intelligence
Facultad de Informática, Universidad Complutense de Madrid, Madrid, Spain
Interests: data analysis; predictive analytics; deep learning; machine learning; artificial intelligence

Special Issue Information

Dear Colleagues,

In recent decades, the Big Data phenomenon has produced a technological change that has been reflected in the development of new information processing tools and in new types of computer systems. In this sense, there are two areas of work that have undergone an important evolutionary change. On the one hand, information storage technologies have been affected. Thus, alternatives have emerged to the relational model that has prevailed in recent decades. These alternatives receive the generic name of NoSQL Databases, and are characterized by being databases that generally run in distributed environments and where there is no schema for the data, thus offering great flexibility. On the other hand, another area that has evolved has been data analytics. In this area, the use of artificial intelligence has been employed and more specifically machine learning techniques to perform information analysis and obtain valuable information such as patterns, information classifications or predictions.

The objective of this Special Issue is to serve as a meeting point for all those researchers who are working in these fields both theoretical and applied. The topics of interest would be (although limited to):

  • Challenges of Big Data;
  • NoSQL Databases;
  • New SQL;
  • New data warehouses;
  • Big Data and health;
  • Big Data and marketing;
  • Massive data processing;
  • Application of artificial intelligence to information processing;
  • Big Data and data analysis. Fields of application.

Both review articles on the state of the art and experimental or theoretical articles are welcome.

Dr. Antonio Sarasa-Cabezuelo
Dr. David Villaseca-Morales
Dr. Covadonga Diez Sanmartin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Knowledge is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Big Data
  • NoSQL databases
  • Machine Learning
  • new SQL
  • Artificial Intelligence
  • Business Intelligence

Published Papers (1 paper)

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Research

24 pages, 2112 KiB  
Article
GDPR-Compliant Social Network Link Prediction in a Graph DBMS: The Case of Know-How Development at Beekeeper
Knowledge 2022, 2(2), 286-309; https://doi.org/10.3390/knowledge2020017 - 19 May 2022
Cited by 1 | Viewed by 2539
Abstract
The amount of available information in the digital world contains massive amounts of data, far more than people can consume. Beekeeper AG provides a GDPR-compliant platform for frontline employees, who typically do not have permanent access to digital information. Finding relevant information to [...] Read more.
The amount of available information in the digital world contains massive amounts of data, far more than people can consume. Beekeeper AG provides a GDPR-compliant platform for frontline employees, who typically do not have permanent access to digital information. Finding relevant information to perform their job requires efficient filtering principles to reduce the time spent on searching, thus saving work hours. However, with GDPR, it is not always possible to observe user identification and content. Therefore, this paper proposes link prediction in a graph structure as an alternative to presenting the information based on GDPR data. In this study, the research of user interaction data in a graph database was compared with graph machine learning algorithms for extracting and predicting network patterns among the users. The results showed that although the accuracy of the models was below expectations, the know-how developed during the process could generate valuable technical and business insights for Beekeeper AG. Full article
(This article belongs to the Special Issue Big Data and Databases)
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