Machine Learning in Data Structures

Dear Colleagues,

Machine learning is the study of computer algorithms that allow computer programs to improve automatically through experience. Machine learning algorithms build a model based on training data to make predictions or decisions without being explicitly programmed to do so. In recent years, a recent subfield of machine learning has developped that is worth highlighting called deep learning (DL). DL addresses the use of different architectures of artificial neural networks that, through a hierarchy of layers with non-linear processing units, learn high-level abstractions for data.

Machine learning techniques and deep learning have been extensively used to explore the causal relations and correlations among big data. Moreover, in recent years, machine learning techniques have been used as a replacement for classical data structures. Specifically, the notion of smart data structures or learned indexes is appealing to what we want be achieved.

This Special Issue focuses on the latest developments in machine learning foundations on data structures, as well as on the synergy between data structures and machine learning. The aim of this Special Issue is to explore machine learning techniques and especially deep learning in order to recognize data schemas and data structures and make them interoperable.

This Special Issue is particularly interested in novel algorithms in the context of the application of machine learning to effectively design data structures in various applications. The notions related to the studies we intend to receive in this Special Issue are learned indices, multicriteria data structures and data structure alchemy. Theoretically well-founded contributions and their real-world applications in laying new foundations for machine learning and data structures are welcome. However, demonstration manuscripts that discuss successful system developments, as well as experience/use-case articles and high-quality survey papers, are also welcome. Contributions may span a wide range of algorithms for modeling, practices for building, and techniques for evaluating operations and services.

Submissions should focus on the aspects of application of machine learning techniques to the design of data structures, including (but not limited to) the following areas:

• Machine learning techniques on data structures
• Analysis of algorithms and distributed data structures
• Novel programming models
• Learned indices for big data
• Smart data structures
• Multicriteria data structures
• Dynamic data structures
• Distributed data structures
• Classic data structures
• Database applications
• Mining and analytics for scientific and business data, social networks, time series, streams, text, web, graphs, rules, patterns, logs, and spatio–temporal data
• Cloud-based applications
• Performance models
• Novel programming models
• Network management and techniques
• Schema management and design
• Machine learning, AI and databases
• Data management issues and support for machine learning and AI
• Specialized and domain-specific data management
• Learned index structures or information retrieval

Dr. Christos Makris
Dr. Andreas Kanavos
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. Algorithms 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.

• Machine learning techniques on data structures
• Analysis of algorithms and distributed data structures
• Novel programming models
• Learned indices for big data
• Smart data structures
• Multicriteria data structures
• Dynamic data structures
• Distributed data structures
• Classic data structures
• Database applications
• Mining and analytics for scientific and business data, social networks, time series, streams, text, web, graphs, rules, patterns, logs, and spatio–temporal data
• Cloud-based applications
• Performance models
• Novel programming models
• Network management and techniques
• Schema management and design
• Machine learning, AI and databases
• Data management issues and support for machine learning and AI
• Specialized and domain-specific data management
• Learned index structures or information retrieval