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A B-tree index creates a multi-level tree structure that breaks a database down into fixed-size blocks or pages. ... Let h ≥ –1 be the height of the classic B ...
A B+tree is thus particularly useful as a database system index, where the data typically resides on disk, as it allows the B+tree to actually provide an efficient structure for housing the data itself (this is described in [11]: 238 as index structure "Alternative 1").
The B+ tree is a structure for indexing single-dimensional data. In order to adopt the B+ tree as a moving object index, the B x-tree uses a linearization technique which helps to integrate objects' location at time t into single dimensional value. Specifically, objects are first partitioned according to their update time.
A labeled binary tree of size 9 (the number of nodes in the tree) and height 3 (the height of a tree defined as the number of edges or links from the top-most or root node to the farthest leaf node), with a root node whose value is 1. The above tree is unbalanced and not sorted.
Various height-balanced binary search trees were introduced to confine the tree height, such as AVL trees, Treaps, and red–black trees. [5] The AVL tree was invented by Georgy Adelson-Velsky and Evgenii Landis in 1962 for the efficient organization of information. [6] [7] It was the first self-balancing binary search tree to be invented. [8]
A large database index would typically use B-tree algorithms. BRIN is not always a substitute for B-tree, it is an improvement on sequential scanning of an index, with particular (and potentially large) advantages when the index meets particular conditions for being ordered and for the search target to be a narrow set of these values.
The fractal tree index is a refinement of the B ε tree. Like a B ε tree, it consists of nodes with keys and buffers and realizes the optimal insertion/query tradeoff. The fractal tree index differs in including performance optimization and in extending the functionality. Examples of improved functionality include ACID semantics. B-tree ...
Overall, the index is organized as a B+tree. When the column cardinality is low, each leaf node of the B-tree would contain long list of RIDs. In this case, it requires less space to represent the RID-lists as bitmaps. Since each bitmap represents one distinct value, this is the basic bitmap index.