Updated Aims and Scope of Analytics

1. Introduction

Analytics [1] (ISSN: 2813-2203) was established in 2022 [2] as an international, open-access journal dedicated to high-quality research on the theoretical, methodological, and technological aspects of systematic computational data analysis. This journal provides an interdisciplinary forum for communicating results relating to data analytics science and engineering.

To further enhance the quality of Analytics and its published research, under the guidance of the Editor-in-Chief, Prof. Dr. Carson K. Leung, the journal has updated and refined its aims and scope.

For more detailed information, please visit the Aims and Scope page for Analytics [3]. For the convenience of readers, the updated aims and scope are listed in Section 2 and Section 3.

We hope that the new aims and scope will contribute to providing a clearer pathway for authors and readers, enabling better access to relevant content and promoting interdisciplinary collaboration.

2. Updated Aims

Analytics (ISSN: 2813-2203) is an international, open-access journal dedicated to publishing high-quality research on the theoretical, methodological, and technological aspects of systematic computational data analysis. The journal provides an interdisciplinary forum allowing the communication of results concerning data analytics science and engineering.

The aim is to help consolidate data analytics from the following perspectives:

• Data analytics science: The research and development of formal techniques that contribute to the quality of data analysis.
• Data analytics engineering: The analysis, design, implementation, and deployment of data analytics projects.

3. Updated Scope

The scope of Analytics includes a broad range of topics, including the success of data analytics projects, with a focus on both theoretical and applied aspects.

Data analytics science: The theoretical, methodological and technological aspects of data analytics, including but not limited to the following:

• Information access and load—distributed, federated, edge computing, etc.;
• Information storage and architecture—database systems, high-performance computing, etc.;
• Information fusion—integration of different types of data (numerical, categorical, text, audio, image, video, etc.) and structures (cross-sectional, time series, panel, data streams, etc.);
• Data and information quality—preprocessing, cleansing, imputation, transformation, outlier detection, etc.;
• The mathematics and dynamics of machine learning;
• The statistical foundations of machine learning, analysis, modeling, and inferencing (e.g., Bayesian approaches);
• Predictive analytics and associated models—classification, regression, ensemble methods, etc.;
• Descriptive analytics and associated models—data lake, data mart, data mesh, data warehouse (DW), online analytical processing (OLAP) operations, clustering, bi-clustering, patterns, etc.;
• Associative analytics and associated models—associations and graph-based approaches;
• Prescriptive analytics and associated models—simulation and optimization methods, knowledge-based models for action recommendation, etc.;
• Big data analytics, including the utilization, adaptation, evaluation, and improvement of methods, techniques, algorithms, and heuristics specialized for handling and improving big data analytics;
• Visualization;
• Quality metrics;
• Emergent topics—generative artificial intelligence (AI), AI hardware, quantum computing, etc.;
• Innovative contributions of data analytics science.

Data analytics engineering: The theoretical, methodological and technological aspects of data analytics, including but not limited to the following:

• Project planning and estimation;
• Project analysis and design;
• Project methodology;
• Project validation;
• Project deployment;
• Frameworks, schemas, and international ISO/IEC/IEEE standards for data analytics engineering.

Data analytics projects: Successful applications of data analytics to real-world problems across a wide variety of industries, including but not limited to the following:

• Biomedicine/health;
• Pharmaceuticals;
• Manufacturing/production;
• Agriculture;
• Mining;
• Finance/business;
• Marketing;
• Insurance;
• Climate/natural hazards;
• Sociology/education;
• Internet of Things (IoT);
• Smart cities;
• Information technology (IT) services;
• Cybersecurity;
• Energy;
• Tourism;
• Sports.