Special Issue "Advances in Architectures, Big Data, and Machine Learning Techniques for Complex Internet of Things Systems"

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: 18 December 2020.

Special Issue Editors

Prof. Dr. Jesús Peral
E-Mail Website
Guest Editor
Department of Software and Computing Systems, University of Alicante, Spain
Interests: multidimensional databases, business intelligence, data mining and information integration; natural language processing (NLP), specifically in syntactic analysis and solving linguistic phenomena (i.e., ellipsis and anaphora)
Special Issues and Collections in MDPI journals
Prof. Dr. Hadi Moradi
E-Mail Website
Guest Editor
School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Iran
Interests: machine learning and intelligent systems (such as video games, IoT, and intelligent toys) for cognitive screening, rehabilitation, and empowerment; machine learning methods (such as deep learning, SVM, and random forest) for detecting earthquakes for early warning to vulnerable areas
Prof. Dr. Javi Medina Quero
E-Mail Website
Guest Editor
Department of Computer Science, University of Jaén, Spain
Interests: intelligent systems for Internet of Things, which encompasses knowledge base systems with fuzzy logic, deep learning for temporal processing and fusion of sensor data and advanced architectures for ubiquitous computing and ambient intelligence in e-Health
Special Issues and Collections in MDPI journals
Dr. Jie Lian
E-Mail Website
Guest Editor
Department of Computer Science, Shanghai Normal University, China
Interests: spatio-temporal data mining; deep learning; big data
Prof. Dr. David Gil
E-Mail Website
Guest Editor
Department of Computing Technology and Data Processing, University of Alicante, Spain
Interests: artificial intelligence applications (such as artificial neural networks, support vector machines, decision trees, and so on); data mining applications; Big Data; Internet of things; diagnosis and decision support system in medical and cognitive sciences
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Massive volumes of data are already present and still rapidly growing as a result of diverse data sources, including all type of smart devices and sensors (Internet of Things) and social networks. This fact has led to an increasing interest in incorporating these huge amounts of external and unstructured data, normally referred to as "Big Data", into traditional applications. This requirement has made that traditional database systems and processing need to evolve and accommodate them.

However, there are important limitations for a large-scale achievement in this revolution. Furthermore, IoT allows developing big data architectures based on services. Of course, in IoT the information varies broadly in structure, complexity and type. This leads to a need for integration, one of the most complex as well as challenging issues of Big Data, which can be defined as a set of complex techniques used to combine data from disparate sources into meaningful and valuable information.

To effectively synthesize big data and communicate among devices using IoT, machine learning techniques are employed. Machine learning extracts meaning from big data using different kind of techniques (clustering, Bayesian methods, decision trees, SVM, deep learning, etc.).

The purpose of this special issue is to publish high-quality research papers as well as review articles addressing recent advances in handling of architectures, big data, data integration, and machine learning techniques for complex IoT systems. Theoretical studies and state-of-the-art practical applications are welcome for submission.

Prof. Dr. Jesús Peral
Prof. Dr. Hadi Moradi
Prof. Dr. Javi Medina Quero
Dr. Jie Lian
Prof. Dr. David Gil
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 papers will be 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. Sustainability is an international peer-reviewed open access semimonthly 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 1800 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

  • Complex IoT systems
  • Big Data architectures
  • Data Mining with Big Data
  • Machine learning techniques for Big Data analysis
  • Data visualization and integration
  • Deep learning
  • Cognitive systems

Published Papers (1 paper)

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Review

Open AccessReview
Modeling and Management Big Data in Databases—A Systematic Literature Review
Sustainability 2020, 12(2), 634; https://doi.org/10.3390/su12020634 - 15 Jan 2020
Abstract
The work presented in this paper is motivated by the acknowledgement that a complete and updated systematic literature review (SLR) that consolidates all the research efforts for Big Data modeling and management is missing. This study answers three research questions. The first question [...] Read more.
The work presented in this paper is motivated by the acknowledgement that a complete and updated systematic literature review (SLR) that consolidates all the research efforts for Big Data modeling and management is missing. This study answers three research questions. The first question is how the number of published papers about Big Data modeling and management has evolved over time. The second question is whether the research is focused on semi-structured and/or unstructured data and what techniques are applied. Finally, the third question determines what trends and gaps exist according to three key concepts: the data source, the modeling and the database. As result, 36 studies, collected from the most important scientific digital libraries and covering the period between 2010 and 2019, were deemed relevant. Moreover, we present a complete bibliometric analysis in order to provide detailed information about the authors and the publication data in a single document. This SLR reveal very interesting facts. For instance, Entity Relationship and document-oriented are the most researched models at the conceptual and logical abstraction level respectively and MongoDB is the most frequent implementation at the physical. Furthermore, 2.78% studies have proposed approaches oriented to hybrid databases with a real case for structured, semi-structured and unstructured data. Full article
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