Special Issue "Data Science for Industry 4.0. Theory and Applications"

A special issue of Sci (ISSN 2413-4155).

Deadline for manuscript submissions: 20 November 2020.

Special Issue Editors

Prof. Marcello Trovati
Website
Guest Editor
Department of Computer Science, Edge Hill University, St Helens Road Ormskirk Lancashire L39 4QP, UK
Interests: mathematical modelling, the science of Big Data, including data and text mining, and their applications to multi-disciplinary topics
Prof. Yannis Korkontzelos
Website
Guest Editor
Department of Computer Science, Edge Hill University, St Helens Road Ormskirk Lancashire L39 4QP, UK
Interests: data science; mathematical modelling; dynamical systems
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Diverse multidisciplinary approaches are being continuously developed and advanced to address the challenges that Big Data research raises. In particular, the current academic and professional environments are working to produce algorithms, theoretical advances in big data science, to enable the full utilisation of its potential, and better applications. This Special Issue focuses on the dissemination of original contributions to discuss and explore theoretical concepts, principles, tools, techniques, and deployment models in the context of Big Data. Via the contribution of both academics and industry practitioners, the current approaches for the acquisition, interpretation, and assessment of relevant information will be addressed to advance the state-of-the-art Big Data technology.

This Special Issue aims to collect state-of-the-art breakthroughs, including but not limited to the following topics:

  • Statistical and dynamical properties of Big Data;
  • Applications of machine learning for information extraction;
  • Hadoop and Big Data;
  • Data and text mining techniques for Big Data;
  • Novel algorithms in classification, regression, clustering, and analysis;
  • Distributed systems and cloud computing for Big Data;
  • Big Data applications;
  • Big Textual/Natural Language Data;
  • Theory, applications and mining of networks associated with Big Data;
  • Large-scale network data analysis;
  • Data reduction, feature selection, and transformation algorithms;
  • Data visualisation;
  • Distributed data analysis platforms;
  • Scalable solutions for pattern recognition;
  • Stream and real-time processing of Big Data;
  • Information quality within Big Data;
  • Threat detection in Big Data.

Prof. Marcello Trovati
Prof. Yannis Korkontzelos
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. Sci 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
  • Data science
  • Mathematical modelling
  • Text mining
  • Natural language processing
  • AI
  • Data mining

Published Papers (1 paper)

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Research

Open AccessArticlePost Publication Peer ReviewVersion 1, Original
A Hybrid Approach: Dynamic Diagnostic Rules for Sensor Systems in Industry 4.0 Generated by Online Hyperparameter Tuned Random Forest
Sci 2020, 2(3), 61; https://doi.org/10.3390/sci2030061 - 06 Aug 2020
Abstract
In this work, a hybrid component Fault Detection and Diagnosis (FDD) approach for industrial sensor systems is established and analyzed, to provide a hybrid schema that combines the advantages and eliminates the drawbacks of both model-based and data-driven methods of diagnosis. Moreover, it [...] Read more.
In this work, a hybrid component Fault Detection and Diagnosis (FDD) approach for industrial sensor systems is established and analyzed, to provide a hybrid schema that combines the advantages and eliminates the drawbacks of both model-based and data-driven methods of diagnosis. Moreover, it shines the light on a new utilization of Random Forest (RF) together with model-based diagnosis, beyond its ordinary data-driven application. RF is trained and hyperparameter tuned using three-fold cross validation over a random grid of parameters using random search, to finally generate diagnostic graphs as the dynamic, data-driven part of this system. This is followed by translating those graphs into model-based rules in the form of if-else statements, SQL queries or semantic queries such as SPARQL, in order to feed the dynamic rules into a structured model essential for further diagnosis. The RF hyperparameters are consistently updated online using the newly generated sensor data to maintain the dynamicity and accuracy of the generated graphs and rules thereafter. The architecture of the proposed method is demonstrated in a comprehensive manner, and the dynamic rules extraction phase is applied using a case study on condition monitoring of a hydraulic test rig using time-series multivariate sensor readings. Full article
(This article belongs to the Special Issue Data Science for Industry 4.0. Theory and Applications)
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