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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. Each level of this tree can be used to link those pages via an address location, allowing one page (known as a node, or internal page) to refer to another with leaf pages at the lowest level.
The index is similar to the term document matrices employed by latent semantic analysis. The inverted index can be considered a form of a hash table. In some cases the index is a form of a binary tree, which requires additional storage but may reduce the lookup time.
A "Fenwick tree" is actually three implicit trees over the same array: the interrogation tree used for translating indexes to prefix sums, the update tree used for updating elements, and the search tree for translating prefix sums to indexes (rank queries). [4] The first two are normally walked upwards, while the third is usually walked downwards.
Accordingly, the set bit is used to index the first item, or child node, in the 32- or 64-entry based bitwise tree. Search then proceeds by testing each subsequent bit in the key. [19] This procedure is also cache-local and highly parallelizable due to register independency, and thus performant on out-of-order execution CPUs. [19]
R-trees are tree data structures used for spatial access methods, i.e., for indexing multi-dimensional information such as geographical coordinates, rectangles or polygons. The R-tree was proposed by Antonin Guttman in 1984 [ 2 ] and has found significant use in both theoretical and applied contexts. [ 3 ]
In data processing R*-trees are a variant of R-trees used for indexing spatial information. R*-trees have slightly higher construction cost than standard R-trees, as the data may need to be reinserted; but the resulting tree will usually have a better query performance. Like the standard R-tree, it can store both point and spatial data.
The data in those high-dimension spaces is divided based on space or partition strategies, and each partition has an index value that is close with the respect to the partition. From here, those points can be efficiently implemented using B+ tree, thus, the queries are mapped to single dimensions ranged search.
Diagram illustrating compaction of data in a log-structured merge tree. Most LSM trees used in practice employ multiple levels. Level 0 is kept in main memory, and might be represented using a tree. The on-disk data is organized into sorted runs of data. Each run contains data sorted by the index key.