Just a short intro before diving deep into it;😉
Find the walkthrough here:- (basically our team's project presentation that's been uploaded on youtube)
https://www.youtube.com/watch?v=CTcz1ENLuRc
B-Trees are balanced tree structures optimized for disk storage and retrieval.
They maintain data in a sorted order, facilitating rapid search, insertion, and deletion operations.
B-Trees are commonly utilized in database systems and file systems due to their ability to maintain balance even with large datasets and varying access patterns.
This balance ensures consistent performance, making B-Trees well-suited for scenarios requiring frequent updates and range queries.
KD-Trees, on the other hand, are hierarchical data structures used primarily for multidimensional data representation and search.
They partition multidimensional space into smaller regions, recursively dividing it along coordinate axes.
This structure enables efficient spatial search operations, such as nearest neighbor queries and range searches, particularly in applications like computational geometry, machine learning, and computer graphics.
In summary, while B-Trees excel in managing linearly ordered data for disk-based storage systems, KD-Trees offer efficient organization and retrieval of multidimensional data, making them indispensable in various computational domains.