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The nested set model is a technique for representing nested set collections (also known as trees or hierarchies) in relational databases.. It is based on Nested Intervals, that "are immune to hierarchy reorganization problem, and allow answering ancestor path hierarchical queries algorithmically — without accessing the stored hierarchy relation".
A hierarchical database model is a data model in which the data is organized into a tree-like structure. The data are stored as records which is a collection of one or more fields . Each field contains a single value, and the collection of fields in a record defines its type .
In computer science, a tree is a widely used abstract data type that represents a hierarchical tree structure with a set of connected nodes. Each node in the tree can be connected to many children (depending on the type of tree), but must be connected to exactly one parent, [ 1 ] [ 2 ] except for the root node, which has no parent (i.e., the ...
Relational database management systems such as IBM Db2, [11] Informix, [11] Microsoft SQL Server, [11] Oracle 8, [11] Sybase ASE, [11] and SQLite [14] support this type of tree for table indices, though each such system implements the basic B+ tree structure with variations and extensions.
Using a 2-D-B-tree (2-dimensional K-D-B-tree) as an example, space is subdivided in the same manner as a k-d tree: using a point in just one of the domains, or axes in this case, all other values are either less than or greater than the current value, and fall to the left and right of the splitting plane respectively.
In computer science, a B-tree is a self-balancing tree data structure that maintains sorted data and allows searches, sequential access, insertions, and deletions in logarithmic time. The B-tree generalizes the binary search tree , allowing for nodes with more than two children. [ 2 ]
In computer science, a 2–3–4 tree (also called a 2–4 tree) is a self-balancing data structure that can be used to implement dictionaries. The numbers mean a tree where every node with children (internal node) has either two, three, or four child nodes: a 2-node has one data element, and if internal has two child nodes;
The time cost to build a vantage-point tree is approximately O(n log n). For each element, the tree is descended by log n levels to find its placement. However there is a constant factor k where k is the number of vantage points per tree node. [3] The time cost to search a vantage-point tree to find a single nearest neighbor is O(log n).