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Associative Representation and Processing of Databases Using DASNG and AVB+trees for Efficient Data Access

  • Adrian HorzykEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 976)

Abstract

Today, we have to cope with a great amount of data – BIG data problems. The main issues concerned about BIG data are sparing representation, time efficiency of data access and processing, as well as data mining and knowledge discovery. When dealing with the big amount of data, time is crucial. The most of time for data processing in the contemporary computer science is lost for a various search operation to access appropriate data. This paper presents how data collected in relational databases can be transformed into the associative neuronal graph structures, and how searching operations can be accelerated thanks to the use of aggregation and association of the stored data. To achieve an extraordinary efficiency in data access, this paper introduces new AVB+trees which together with Deep Associative Semantic Neuronal Graphs which can typically allow for constant time access to the stored data. The presented solution allows representing horizontal and vertical relations between data and stored objects, expanding possibilities of relational databases and replacing various search operations by the specific graph structure. Another contribution is the expansion of the aggregation of the duplicates to all data tables which contain the same attributes. In such a way, the presented associative structures simplify and speed up all searching operations in comparison to the classic solutions.

Keywords

Deep neural network architectures AVB-trees AVB+trees Big data representation and processing Associative Graph Data Structures Deep Associative Semantic Neuronal Graphs Associative database transformation 

Notes

Acknowledgements

This work was supported by AGH 11.11.120.612 and a grant from the National Science Centre DEC-2016/21/B/ST7/02220.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.AGH University of Science and TechnologyKrakowPoland

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