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, including deep neural networks. The ability to represent a rich set of relationships between stored data and objects is essential for fast inferenc...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2020-12-01
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| Series: | International Journal of Applied Mathematics and Computer Science |
| Subjects: | |
| Online Access: | https://doi.org/10.34768/amcs-2020-0053 |
| _version_ | 1818597900350914560 |
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| author | Horzyk Adrian Bulanda Daniel Starzyk Janusz A. |
| author_facet | Horzyk Adrian Bulanda Daniel Starzyk Janusz A. |
| author_sort | Horzyk Adrian |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-16T11:55:09Z |
| format | Article |
| id | doaj.art-c9a2e594fd3f464d9f762c69c3fdfb53 |
| institution | Directory Open Access Journal |
| issn | 2083-8492 |
| language | English |
| last_indexed | 2024-12-16T11:55:09Z |
| publishDate | 2020-12-01 |
| publisher | Sciendo |
| record_format | Article |
| series | International Journal of Applied Mathematics and Computer Science |
| spelling | doaj.art-c9a2e594fd3f464d9f762c69c3fdfb532022-12-21T22:32:35ZengSciendoInternational Journal of Applied Mathematics and Computer Science2083-84922020-12-0130471773110.34768/amcs-2020-0053amcs-2020-0053ASA-graphs for efficient data representation and processingHorzyk Adrian0Bulanda Daniel1Starzyk Janusz A.2Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, al. Mickiewicza 30, 30-059Kraków, PolandDepartment of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, al. Mickiewicza 30, 30-059Kraków, PolandFaculty of Applied Computer Science, University of Information Technology and Management in Rzeszów, ul. Sucharskiego 2, 35-225Rzeszów, PolandFast 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.https://doi.org/10.34768/amcs-2020-0053self-balancing treesself-sorting treesself-aggregating data structuresassociative structuresgraphsdata access efficiencyrepresentation of relationships |
| spellingShingle | Horzyk Adrian Bulanda Daniel Starzyk Janusz A. ASA-graphs for efficient data representation and processing International Journal of Applied Mathematics and Computer Science self-balancing trees self-sorting trees self-aggregating data structures associative structures graphs data access efficiency representation of relationships |
| title | ASA-graphs for efficient data representation and processing |
| title_full | ASA-graphs for efficient data representation and processing |
| title_fullStr | ASA-graphs for efficient data representation and processing |
| title_full_unstemmed | ASA-graphs for efficient data representation and processing |
| title_short | ASA-graphs for efficient data representation and processing |
| title_sort | asa graphs for efficient data representation and processing |
| topic | self-balancing trees self-sorting trees self-aggregating data structures associative structures graphs data access efficiency representation of relationships |
| url | https://doi.org/10.34768/amcs-2020-0053 |
| work_keys_str_mv | AT horzykadrian asagraphsforefficientdatarepresentationandprocessing AT bulandadaniel asagraphsforefficientdatarepresentationandprocessing AT starzykjanusza asagraphsforefficientdatarepresentationandprocessing |