<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Fast discovering of various relationships in data is an important feature of modern data mining, cognitive, knowledge-based, and explainable AI systems, including deep neural networks. The ability to represent a rich set of relationships between stored data and objects is essential for fast inferences, finding associations, representing knowledge, and extracting useful patterns or other pieces of information. This paper introduces self-balancing, aggregating, and sorting ASA-graphs for efficient data representation in various data structures, databases, and data mining systems. These graphs are smaller and use more efficient algorithms for searching, inserting, and removing data than the most commonly used self-balancing trees. ASA-graphs also automatically aggregate and count all duplicates of values and represent them by the same nodes, connecting them in order, and simultaneously providing very fast data access based on a binary search tree approach. The proposed ASA-graph structure combines the advantages of sorted lists, binary search trees, B-trees, and B+trees, eliminating their weaknesses. Our experiments proved that the ASA-graphs outperform many commonly used self-balancing trees.</p></abstract>

Fast discovering of various relationships in data is an important feature of modern data mining, cognitive, knowledge-based, and explainable AI systems, including deep neural networks. The ability to represent a rich set of relationships between stored data and objects is essential for fast inferences, finding associations, representing knowledge, and extracting useful patterns or other pieces of information. This paper introduces self-balancing, aggregating, and sorting ASA-graphs for efficient data representation in various data structures, databases, and data mining systems. These graphs are smaller and use more efficient algorithms for searching, inserting, and removing data than the most commonly used self-balancing trees. ASA-graphs also automatically aggregate and count all duplicates of values and represent them by the same nodes, connecting them in order, and simultaneously providing very fast data access based on a binary search tree approach. The proposed ASA-graph structure combines the advantages of sorted lists, binary search trees, B-trees, and B+trees, eliminating their weaknesses. Our experiments proved that the ASA-graphs outperform many commonly used self-balancing trees.

ASA-graphs for efficient data representation and processing

Department of Biocybernetics and Biomedical Engineering

University of Information Technology and Management in Rzeszów

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"ASA-graphs for efficient data representation and processing"}

Fast discovering of various relationships in data is an important feature of modern data mining, cognitive, knowledge-based, and explainable AI systems,...

ASA-graphs for efficient data representation and processing

Publié en ligne: 31 déc. 2020

Pages: 717 - 731

Reçu: 03 août 2020

Accepté: 20 nov. 2020

DOI: https://doi.org/10.34768/amcs-2020-0053

Mots clés
self-balancing trees, self-sorting trees, self-aggregating data structures, associative structures, graphs, data access efficiency, representation of relationships

© 2020 Adrian Horzyk et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

ASA-graphs for efficient data representation and processing

Publié en ligne: 31 déc. 2020

Pages: 717 - 731

Reçu: 03 août 2020

Accepté: 20 nov. 2020

DOI: https://doi.org/10.34768/amcs-2020-0053

Mots clésself-balancing trees, self-sorting trees, self-aggregating data structures, associative structures, graphs, data access efficiency, representation of relationships

© 2020 Adrian Horzyk et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
self-balancing trees, self-sorting trees, self-aggregating data structures, associative structures, graphs, data access efficiency, representation of relationships