Bond-Energy-Algorithm

Distributed Database - Vertical Fragmentation (Clustering)

Efficient Distributed Database Fragmentation using Bond Energy Algorithm

Description:

This Python code implements the Bond Energy Algorithm for efficient vertical fragmentation of distributed databases based on clustering. The code uses Query Access Matrix and Query Attribute Matrix to calculate the bond energy between pairs of columns in a table, and then groups the columns based on their bond energy to create smaller tables. The code is highly efficient, using NumPy for numerical computations, and can be adapted to any problem.

Features:

• Implements the Bond Energy Algorithm for vertical fragmentation of distributed databases.
• Uses Query Access Matrix and Query Attribute Matrix to calculate bond energy between pairs of columns.
• Groups columns based on their bond energy to create smaller tables for efficient query performance.
• Bond Enegy determaining by using dot products.
• Using Query Access Matrix and Query Attribute Matrix to determines AA_MATRIX (Attribute Matrix).
• Find maximal bond and determining position of columns in CC_MATRIX.
• Highly efficient code using NumPy for numerical computations.
• Easily adaptable to any problem.
• Improves query performance by reducing the number of accessed columns.
• Simple example included for demonstration.

Benefits:

• Reduces network traffic and query response time in distributed databases.
• Improves overall system performance by reducing the number of accessed columns.
• Highly efficient code using NumPy for faster computations.
• Easily adaptable to any problem for efficient fragmentation.
• Simple example included for quick and easy implementation.

In summary, this Python code offers an efficient solution for vertical fragmentation of distributed databases using the Bond Energy Algorithm. It can help to improve query performance, reduce network traffic, and improve overall system performance in distributed database systems. With only 57 lines of code and a simple example included, this solution is highly adaptable and easy to implement.

Note:

• Optimal and minimal writting code (57 Lines Only - Thanks to NumPy).
• Hardcoded Example (But can be used for any problem).