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Information System Model and Big Data Analytics

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

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 6357

Special Issue Editors


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Guest Editor

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Guest Editor
Department of Mechanical and Industrial Engineering, NOVA University of Lisbon, Lisbon, Portugal
Interests: applications of AI to industrial engineering; digital platforms; business interoperability

Special Issue Information

Dear Colleagues,

Over the past few years, we have seen exponential growth in the volume of data available worldwide. With this increase, two inherent problems have arisen: data storage capacity and data processing. Big data analysis highlights analytical approaches to processing these large amounts of data, leading to the possibility of extracting insights that were not possible to identify through traditional methods, such as unknown correlations in data. Big data analytics has several advantages, such as improving decision support systems and preventing fraudulent activities.

Information systems are a set of components that aim to collect, store and process data to provide information and knowledge. The increase in big data came to affect these systems, which had to be “rebuilt” to respond to the rise in data volume. It is necessary to have a well-modelled information system, clear and objective, to take advantage of all the potential that big data can bring to our society.

Everything around us is now affected by big data. In recent years, the ability to analyze and understand a large amount of data has been improved. There are immense possibilities that arise from big data's use, sustainability being one of them. In the field of sustainability, environmental sustainability is one of the most critical factors. One example is climate change, which has reached the top of the list of global risks, affecting a diversity of countries and disturbing economies. Big data can provide very relevant information to promote sustainability to reduce risks and prevent certain situations.

This Special Issue, entitled “Information System Model and Big Data Analytics”, aims to publish research and revised articles based on the relationship between the information system model and big data analytics. The topics of interest for this issue include but are not limited to:

- Artificial intelligence for sustainability;

- Big data visualization;

- Big data preprocessing;

- Big data analytic tools;

- Search and optimization for big data;

- Forecasting models using big data analytics;

- Novel hardware and software architectures for big data;

- Big data analytics using machine learning and computational intelligence;

- Applications.

Prof. Dr. Fernando Moreira
Prof. Dr. José Machado
Prof. Dr. Antonio Grilo
Prof. Dr. Paulo Novais
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. 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 2400 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 analytics
  • Information systems
  • Machine learning
  • Intelligent systems
  • Data quality
  • Deep learning
  • Data mining
  • Decision support systems
  • Sustainability
  • Big data management
  • Data processing and analysis
  • Predictive analysis
  • Sustainable cities

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Published Papers (2 papers)

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Research

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18 pages, 554 KiB  
Article
Interpretable Success Prediction in Higher Education Institutions Using Pedagogical Surveys
by Fátima Leal, Bruno Veloso, Carla Santos Pereira, Fernando Moreira, Natércia Durão and Natacha Jesus Silva
Sustainability 2022, 14(20), 13446; https://doi.org/10.3390/su142013446 - 18 Oct 2022
Cited by 1 | Viewed by 1687
Abstract
The indicators of student success at higher education institutions are continuously analysed to increase the students’ enrolment in multiple scientific areas. Every semester, the students respond to a pedagogical survey that aims to collect the student opinion of curricular units in terms of [...] Read more.
The indicators of student success at higher education institutions are continuously analysed to increase the students’ enrolment in multiple scientific areas. Every semester, the students respond to a pedagogical survey that aims to collect the student opinion of curricular units in terms of content and teaching methodologies. Using this information, we intend to anticipate the success in higher-level courses and prevent dropouts. Specifically, this paper contributes with an interpretable student classification method. The proposed solution relies on (i) a pedagogical survey to collect student’s opinions; (ii) a statistical data analysis to validate the reliability of the survey; and (iii) machine learning algorithms to classify the success of a student. In addition, the proposed method includes an explainable mechanism to interpret the classifications and their main factors. This transparent pipeline was designed to have implications in both digital and sustainable education, impacting the three pillars of sustainability, i.e.,economic, social, and environmental, where transparency is a cornerstone. The work was assessed with a dataset from a Portuguese higher-level institution, contemplating multiple courses from different departments. The most promising results were achieved with Random Forest presenting 98% in accuracy and F-measure. Full article
(This article belongs to the Special Issue Information System Model and Big Data Analytics)
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Review

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26 pages, 8737 KiB  
Review
Agricultural Big Data Architectures in the Context of Climate Change: A Systematic Literature Review
by Ania Cravero, Ana Bustamante, Marlene Negrier and Patricio Galeas
Sustainability 2022, 14(13), 7855; https://doi.org/10.3390/su14137855 - 28 Jun 2022
Cited by 5 | Viewed by 3206
Abstract
Climate change is currently one of agriculture’s main problems in achieving sustainability. It causes drought, increased rainfall, and increased diseases, causing a decrease in food production. In order to combat these problems, Agricultural Big Data contributes with tools that improve the understanding of [...] Read more.
Climate change is currently one of agriculture’s main problems in achieving sustainability. It causes drought, increased rainfall, and increased diseases, causing a decrease in food production. In order to combat these problems, Agricultural Big Data contributes with tools that improve the understanding of complex, multivariate, and unpredictable agricultural ecosystems through the collection, storage, processing, and analysis of vast amounts of data from diverse heterogeneous sources. This research aims to discuss the advancement of technologies used in Agricultural Big Data architectures in the context of climate change. The study aims to highlight the tools used to process, analyze, and visualize the data, to discuss the use of the architectures in crop, water, climate, and soil management, and especially to analyze the context, whether it is in Resilience Mitigation or Adaptation. The PRISMA protocol guided the study, finding 33 relevant papers. However, despite advances in this line of research, few papers were found that mention architecture components, in addition to a lack of standards and the use of reference architectures that allow the proper development of Agricultural Big Data in the context of climate change. Full article
(This article belongs to the Special Issue Information System Model and Big Data Analytics)
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