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Big Data and Information Science Technology
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Big Data and Information Science Technology

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: 30 September 2024 | Viewed by 4491

Special Issue Editors

Dr. Enrico Corradini

E-Mail Website
Guest Editor
Department of Information Engineering (DII), Polytechnic University of Marche, 60121 Ancona, Italy
Interests: big data analytics; social network analysis; network theory and practice; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Francesco Cauteruccio

E-Mail Website
Guest Editor
Department of Information Engineering, Polytechnic University of Marche, 60121 Ancona, Italy
Interests: social and complex network analysis; hypernetwork and network science; Internet of Things; advanced algorithms for sequences comparison; pattern mining; logic programming; data science
Special Issues, Collections and Topics in MDPI journals
Dr. Luca Virgili

E-Mail Website
Guest Editor
Department of Information Engineering, Polytechnic University of Marche, 60121 Ancona, Italy
Interests: big data analytics; social network analysis; deep learning; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The digital age has brought about an unprecedented surge in data, leading to the emergence of big data and information science technology. The integration and use of big data technologies is of utmost importance within information science. This can be furthered by highlighting innovative research and applications in data science across various fields such as business, healthcare, education, and more. In doing so, we aim to revolutionize decision-making processes and offer unprecedented insights.

This Special Issue seeks to showcase original research articles and reviews on themes including big data analytics, machine learning, artificial intelligence for data management, real-time data processing, and data security in big data. By exploring these topics, we aim to contribute to the ongoing discourse on the potential of big data and information science technology to transform various sectors.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  • Predictive analytics and machine learning algorithms for big data;
  • Data mining and knowledge discovery in large-scale datasets;
  • Natural language processing and text mining in big data;
  • Visualization techniques for big data analysis and exploration;
  • Big data-driven decision support systems and applications;
  • Scalable and distributed computing frameworks for big data processing;
  • Privacy-preserving techniques in big data analytics;
  • Real-time stream processing and analytics for big data;
  • Big data integration, fusion, and interoperability;
  • Ethical and legal considerations in the era of big data.

We look forward to receiving your contributions.

Dr. Enrico Corradini
Dr. Francesco Cauteruccio
Dr. Luca Virgili
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. Big Data and Cognitive Computing is an international peer-reviewed open access monthly 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

  • big data
  • information science
  • data analytics
  • data security
  • machine learning
  • data mining
  • real-time data processing

Published Papers (3 papers)

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Research

24 pages, 429 KiB  
Article
Cancer Detection Using a New Hybrid Method Based on Pattern Recognition in MicroRNAs Combining Particle Swarm Optimization Algorithm and Artificial Neural Network
by Sepideh Molaei, Stefano Cirillo and Giandomenico Solimando
Big Data Cogn. Comput. 2024, 8(3), 33; https://doi.org/10.3390/bdcc8030033 - 19 Mar 2024
Viewed by 956
Abstract
MicroRNAs (miRNAs) play a crucial role in cancer development, but not all miRNAs are equally significant in cancer detection. Traditional methods face challenges in effectively identifying cancer-associated miRNAs due to data complexity and volume. This study introduces a novel, feature-based technique for detecting [...] Read more.
MicroRNAs (miRNAs) play a crucial role in cancer development, but not all miRNAs are equally significant in cancer detection. Traditional methods face challenges in effectively identifying cancer-associated miRNAs due to data complexity and volume. This study introduces a novel, feature-based technique for detecting attributes related to cancer-affecting microRNAs. It aims to enhance cancer diagnosis accuracy by identifying the most relevant miRNAs for various cancer types using a hybrid approach. In particular, we used a combination of particle swarm optimization (PSO) and artificial neural networks (ANNs) for this purpose. PSO was employed for feature selection, focusing on identifying the most informative miRNAs, while ANNs were used for recognizing patterns within the miRNA data. This hybrid method aims to overcome limitations in traditional miRNA analysis by reducing data redundancy and focusing on key genetic markers. The application of this method showed a significant improvement in the detection accuracy for various cancers, including breast and lung cancer and melanoma. Our approach demonstrated a higher precision in identifying relevant miRNAs compared to existing methods, as evidenced by the analysis of different datasets. The study concludes that the integration of PSO and ANNs provides a more efficient, cost-effective, and accurate method for cancer detection via miRNA analysis. This method can serve as a supplementary tool for cancer diagnosis and potentially aid in developing personalized cancer treatments. Full article
(This article belongs to the Special Issue Big Data and Information Science Technology)
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22 pages, 9109 KiB  
Article
Temporal Dynamics of Citizen-Reported Urban Challenges: A Comprehensive Time Series Analysis
by Andreas F. Gkontzis, Sotiris Kotsiantis, Georgios Feretzakis and Vassilios S. Verykios
Big Data Cogn. Comput. 2024, 8(3), 27; https://doi.org/10.3390/bdcc8030027 - 04 Mar 2024
Viewed by 1106
Abstract
In an epoch characterized by the swift pace of digitalization and urbanization, the essence of community well-being hinges on the efficacy of urban management. As cities burgeon and transform, the need for astute strategies to navigate the complexities of urban life becomes increasingly [...] Read more.
In an epoch characterized by the swift pace of digitalization and urbanization, the essence of community well-being hinges on the efficacy of urban management. As cities burgeon and transform, the need for astute strategies to navigate the complexities of urban life becomes increasingly paramount. This study employs time series analysis to scrutinize citizen interactions with the coordinate-based problem mapping platform in the Municipality of Patras in Greece. The research explores the temporal dynamics of reported urban issues, with a specific focus on identifying recurring patterns through the lens of seasonality. The analysis, employing the seasonal decomposition technique, dissects time series data to expose trends in reported issues and areas of the city that might be obscured in raw big data. It accentuates a distinct seasonal pattern, with concentrations peaking during the summer months. The study extends its approach to forecasting, providing insights into the anticipated evolution of urban issues over time. Projections for the coming years show a consistent upward trend in both overall city issues and those reported in specific areas, with distinct seasonal variations. This comprehensive exploration of time series analysis and seasonality provides valuable insights for city stakeholders, enabling informed decision-making and predictions regarding future urban challenges. Full article
(This article belongs to the Special Issue Big Data and Information Science Technology)
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26 pages, 4290 KiB  
Article
A Model for Enhancing Unstructured Big Data Warehouse Execution Time
by Marwa Salah Farhan, Amira Youssef and Laila Abdelhamid
Big Data Cogn. Comput. 2024, 8(2), 17; https://doi.org/10.3390/bdcc8020017 - 06 Feb 2024
Viewed by 1758
Abstract
Traditional data warehouses (DWs) have played a key role in business intelligence and decision support systems. However, the rapid growth of the data generated by the current applications requires new data warehousing systems. In big data, it is important to adapt the existing [...] Read more.
Traditional data warehouses (DWs) have played a key role in business intelligence and decision support systems. However, the rapid growth of the data generated by the current applications requires new data warehousing systems. In big data, it is important to adapt the existing warehouse systems to overcome new issues and limitations. The main drawbacks of traditional Extract–Transform–Load (ETL) are that a huge amount of data cannot be processed over ETL and that the execution time is very high when the data are unstructured. This paper focuses on a new model consisting of four layers: Extract–Clean–Load–Transform (ECLT), designed for processing unstructured big data, with specific emphasis on text. The model aims to reduce execution time through experimental procedures. ECLT is applied and tested using Spark, which is a framework employed in Python. Finally, this paper compares the execution time of ECLT with different models by applying two datasets. Experimental results showed that for a data size of 1 TB, the execution time of ECLT is 41.8 s. When the data size increases to 1 million articles, the execution time is 119.6 s. These findings demonstrate that ECLT outperforms ETL, ELT, DELT, ELTL, and ELTA in terms of execution time. Full article
(This article belongs to the Special Issue Big Data and Information Science Technology)
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