Recent Developments in Data Science and Knowledge Discovery

Dear Colleagues,

The Special Issue aims to highlight the latest advancements and emerging trends in the fields of data science and knowledge discovery. This issue brings together a collection of high-quality research papers, reviews, and case studies that explore innovative methodologies, tools, and applications driving the field forward.

Methodologies: We are inviting examinations of techniques for managing, processing, and analyzing large-scale datasets, which includes advancements in distributed computing, cloud-based analytics, and real-time data processing frameworks; explorations of new methods for extracting meaningful patterns and insights from complex datasets, including clustering, association rule mining, and sequential pattern mining; and papers that explore advancements in supervised, unsupervised, and reinforcement learning, with applications ranging from natural language processing to image recognition and beyond.

Topics include improvements in algorithm efficiency, scalability, and interpretability, as well as the development of hybrid models that combine different machine-learning approaches for enhanced performance.

Tools: We welcome introductions to new tools, platforms, and frameworks that facilitate data science workflows; an emphasis on open-source solutions and platforms that enhance collaboration, reproducibility, and scalability in data science projects; and reviews of integrated development environments, data visualization tools, and data management systems that support efficient data analysis and knowledge discovery.

Applications: We are interested in showcasing how data science and knowledge discovery techniques are being applied across various disciplines; papers that illustrate the impact of data-driven approaches in fields such as environmental science, education, and public policy; and innovative applications that demonstrate the transformative potential of data science in solving real-world problems and improving societal outcomes.

Prof. Dr. Wenbing Zhao
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at mdpi.longhoe.net 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. Applied System Innovation 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 1400 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
• data mining
• knowledge discovery
• natural language processing
• image recognition
• predictive analytics
• anomaly detection
• decision-making