Building the Right Environment to Support AI, Machine Learning and Deep Learning
Although there has been a lot of AVL tree libraries available now, nearly all of them are meant to work in the random access memory(RAM). Some of them do provide some mechanism for dumping the whole tree into a file and loading it back to the memory in order to make data in that tree persistent. It serves well when there's just small amount of data. When the tree is somewhat bigger, the dumping/loading process could take a lengthy time and makes your mission-critical program less efficient. How about an AVL tree that can directly use the disk for data storage ? If there's something like that, we won't need to read through the whole tree in order to pick up just a little bit imformation(a node), but read only the sectors that are neccssary for locating a certain node and the sectors in which that node lies. This is my initial motivation for writing a 'storage-media'independent AVL Tree. However, as you step forth, you would find that it not only works fine with disks but also fine with memorys, too.