Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.9
2.4
Journals
Information
Special Issues
Machine Learning and Data Mining: Innovations in Big Data Analytics
share announcement

Machine Learning and Data Mining: Innovations in Big Data Analytics

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information and Communications Technology".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 4839

Special Issue Editors

Dr. Shadi Banitaan

E-Mail Website
Guest Editor
Electrical & Computer Engineering & Computer Science Department, University of Detroit Mercy, Detroit MI 48221-9900, USA
Interests: machine learning; data mining; applied artificial intelligence; intelligent systems
Dr. Mina Maleki

E-Mail Website
Guest Editor Assistant
Electrical & Computer Engineering & Computer Science Department, University of Detroit Mercy, Detroit MI 48221-9900, USA
Interests: machine learning; data analysis; applied artificial intelligence; bioinformatics

Special Issue Information

Dear Colleagues,

The Special Issue on “Machine Learning and Data Mining: Innovations in Big Data Analytics” aims to explore the latest advancements and applications of machine learning and data mining techniques in the context of big data. As the volume, variety, and velocity of data continue to grow exponentially, there is a pressing need for innovative methods to extract meaningful insights and knowledge from large datasets. This Special Issue will bring together researchers and practitioners to present cutting-edge approaches, share experiences, and discuss future trends in this rapidly evolving field.

Contributions to this Special Issue should address theoretical, methodological, and practical aspects of machine learning and data mining as they relate to big data analytics. We welcome high-quality research papers, comprehensive reviews, and insightful case studies that highlight new challenges, propose novel solutions, and demonstrate successful applications in various domains such as healthcare, finance, social media, and more.

Topics of Interest:

  • Advanced machine learning algorithms for big data;
  • Scalable data mining techniques;
  • Deep learning and its applications in big data analytics;
  • Real-time data processing and analytics;
  • Predictive modeling and forecasting with big data;
  • Anomaly detection and pattern recognition in large datasets;
  • Big data visualization and interpretation;
  • Applications of machine learning and data mining in healthcare, finance, social media, etc.;
  • Ethical and privacy considerations in big data analytics;
  • Tools and frameworks for big data processing.

This Special Issue aims to be a comprehensive resource for those looking to stay at the forefront of machine learning and data mining as applied to big data. By bringing together diverse perspectives and pioneering research, we hope to foster a deeper understanding of the challenges and opportunities in this exciting field.

Dr. Shadi Banitaan
Guest Editor

Dr. Mina Maleki
Guest Editor Assistant

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. Information 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 1600 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
  • machine learning
  • data mining
  • deep learning
  • scalable algorithms
  • predictive modeling
  • anomaly detection
  • real-time processing
  • data visualization
  • ethical considerations
  • healthcare analytics
  • social media analytics
  • data privacy

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 627 KiB  
Article
Fitness Approximation Through Machine Learning with Dynamic Adaptation to the Evolutionary State
by Itai Tzruia, Tomer Halperin, Moshe Sipper and Achiya Elyasaf
Information 2024, 15(12), 744; https://doi.org/10.3390/info15120744 - 21 Nov 2024
Viewed by 462
Abstract
We present a novel approach to performing fitness approximation in genetic algorithms (GAs) using machine learning (ML) models, focusing on dynamic adaptation to the evolutionary state. We compare different methods for (1) switching between actual and approximate fitness, (2) sampling the population, and [...] Read more.
We present a novel approach to performing fitness approximation in genetic algorithms (GAs) using machine learning (ML) models, focusing on dynamic adaptation to the evolutionary state. We compare different methods for (1) switching between actual and approximate fitness, (2) sampling the population, and (3) weighting the samples. Experimental findings demonstrate significant improvement in evolutionary runtimes, with fitness scores that are either identical or slightly lower than those of the fully run GA—depending on the ratio of approximate-to-actual-fitness computation. Although we focus on evolutionary agents in Gymnasium (game) simulators—where fitness computation is costly—our approach is generic and can be easily applied to many different domains. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining: Innovations in Big Data Analytics)
►▼ Show Figures

Graphical abstract

25 pages, 4208 KiB  
Article
Adaptive and Scalable Database Management with Machine Learning Integration: A PostgreSQL Case Study
by Maryam Abbasi, Marco V. Bernardo, Paulo Váz, José Silva and Pedro Martins
Information 2024, 15(9), 574; https://doi.org/10.3390/info15090574 - 18 Sep 2024
Viewed by 2111
Abstract
The increasing complexity of managing modern database systems, particularly in terms of optimizing query performance for large datasets, presents significant challenges that traditional methods often fail to address. This paper proposes a comprehensive framework for integrating advanced machine learning (ML) models within the [...] Read more.
The increasing complexity of managing modern database systems, particularly in terms of optimizing query performance for large datasets, presents significant challenges that traditional methods often fail to address. This paper proposes a comprehensive framework for integrating advanced machine learning (ML) models within the architecture of a database management system (DBMS), with a specific focus on PostgreSQL. Our approach leverages a combination of supervised and unsupervised learning techniques to predict query execution times, optimize performance, and dynamically manage workloads. Unlike existing solutions that address specific optimization tasks in isolation, our framework provides a unified platform that supports real-time model inference and automatic database configuration adjustments based on workload patterns. A key contribution of our work is the integration of ML capabilities directly into the DBMS engine, enabling seamless interaction between the ML models and the query optimization process. This integration allows for the automatic retraining of models and dynamic workload management, resulting in substantial improvements in both query response times and overall system throughput. Our evaluations using the Transaction Processing Performance Council Decision Support (TPC-DS) benchmark dataset at scale factors of 100 GB, 1 TB, and 10 TB demonstrate a reduction of up to 42% in query execution times and a 74% improvement in throughput compared with traditional approaches. Additionally, we address challenges such as potential conflicts in tuning recommendations and the performance overhead associated with ML integration, providing insights for future research directions. This study is motivated by the need for autonomous tuning mechanisms to manage large-scale, heterogeneous workloads while answering key research questions, such as the following: (1) How can machine learning models be integrated into a DBMS to improve query optimization and workload management? (2) What performance improvements can be achieved through dynamic configuration tuning based on real-time workload patterns? Our results suggest that the proposed framework significantly reduces the need for manual database administration while effectively adapting to evolving workloads, offering a robust solution for modern large-scale data environments. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining: Innovations in Big Data Analytics)
►▼ Show Figures

Graphical abstract

26 pages, 15092 KiB  
Article
Exploring the Depths of the Autocorrelation Function: Its Departure from Normality
by Hossein Hassani, Manuela Royer-Carenzi, Leila Marvian Mashhad, Masoud Yarmohammadi and Mohammad Reza Yeganegi
Information 2024, 15(8), 449; https://doi.org/10.3390/info15080449 - 30 Jul 2024
Viewed by 1586
Abstract
In this article, we study the autocorrelation function (ACF), which is a crucial element in time series analysis. We compare the distribution of the ACF, both from a theoretical and empirical point of view. We focus on white noise processes (WN), i.e., uncorrelated, [...] Read more.
In this article, we study the autocorrelation function (ACF), which is a crucial element in time series analysis. We compare the distribution of the ACF, both from a theoretical and empirical point of view. We focus on white noise processes (WN), i.e., uncorrelated, centered, and identically distributed variables, whose ACFs are supposed to be asymptotically independent and converge towards the same normal distribution. But, the study of the sum of the sample ACF contradicts this property. Thus, our findings reveal a deviation of the sample ACF from normality beyond a specific lag. Note that this phenomenon is observed for white noise of varying lengths, and evenforn the residuals of an ARMA(p,q) model. This discovery challenges traditional assumptions of normality in time series modeling. Indeed, when modeling a time series, the crucial step is to validate the estimated model by checking that the associated residuals form white noise. In this study, we show that the widely used portmanteau tests are not completely accurate. Box–Pierce appears to be too conservative, whereas Ljung–Box is too liberal. We suggest an alternative method based on the ACF for establishing the reliability of the portmanteau test and the validity of the estimated model. We illustrate our methodology using money stock data in the USA. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining: Innovations in Big Data Analytics)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop