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.

• 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