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Related Experiment Video

Updated: Jul 7, 2026

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases

Published on: March 19, 2018

A self-processing network model for relational databases.

E De-Medonsa¹, S Kraus, Y Shiftan

¹Dept. of Math. & Comput. Sci., Bar-Ilan Univ., Ramat-Gan.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|February 7, 2008

Summary

This study introduces a novel self-processing network model for very large databases, enhancing performance by processing data within the network structure itself, eliminating data transmission needs and redundancy.

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Area of Science:

Computer Science
Database Systems
Artificial Intelligence

Background:

Traditional distributed database models face challenges with very large datasets.
Existing models often require significant data transmission, leading to performance bottlenecks and redundancy.

Purpose of the Study:

To propose a novel model combining relational databases with self-processing networks.
To enhance the performance and efficiency of very large database management.

Main Methods:

A self-processing network model is introduced, distinct from conventional distributed approaches.
Network nodes (control and data) capture data and relationships, with network activity performing relational algebra operations.
An extension incorporates weighted links for neural network-like properties (fuzziness, learning).

Main Results:

The model achieves improved performance for very large databases.
Eliminates the need for data transmission between nodes.
Ensures data integrity and removes redundancy by using common data nodes across relations.

Conclusions:

The self-processing network model offers a radical new approach to database management.
It effectively addresses performance, integrity, and redundancy issues in large-scale databases.
The model's extension introduces adaptive learning capabilities, paving the way for intelligent databases.