R-деревья: Структуры данных для пространственного индексирования

Data structures used in spatial indexing
the data structure

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 and has found significant use in both theoretical and applied contexts. A common real world usage for an R tree might be to store spatial objects such as restaurant locations or the polygons that typical maps are made of: streets, buildings, outlines of lakes, coastlines, etc. and then find answers quickly to queries such as "Find all museums within 2 km of my current location", "retrieve all road segments within 2 km of my location" (to display them in a navigation system) or "find the nearest gas station" (although not taking roads into account). The R tree can also accelerate nearest neighbor search for various distance metrics, including great circle distance.

The key idea of the data structure is to group nearby objects and represent them with their minimum bounding rectangle in the next higher level of the tree; the "R" in R tree is for rectangle. Since all objects lie within this bounding rectangle, a query that does not intersect the bounding rectangle also cannot intersect any of the contained objects. At the leaf level, each rectangle describes a single object; at higher levels the aggregation includes an increasing number of objects. This can also be seen as an increasingly coarse approximation of the data set. Similar to the B tree, the R tree is also a balanced search tree (so all leaf nodes are at the same depth), organizes the data in pages, and is designed for storage on disk (as used in databases). Each page can contain a maximum number of entries, often denoted as It also guarantees a minimum fill (except for the root node), however best performance has been experienced with a minimum fill of 30%–40% of the maximum number of entries (B trees guarantee 50% page fill, and B* trees even 66%). The reason for this is the more complex balancing required for spatial data as opposed to linear data stored in B trees. As with most trees, the searching algorithms (e. g., intersection, containment, nearest neighbor search) are rather simple. The key idea is to use the bounding boxes to decide whether or not to search inside a subtree. In this way, most of the nodes in the tree are never read during a search. Like B trees, R trees are suitable for large data sets and databases, where nodes can be paged to memory when needed, and the whole tree cannot be kept in main memory. Even if data can be fit in memory (or cached), the R trees in most practical applications will usually provide performance advantages over naive check of all objects when the number of objects is more than few hundred or so. However, for in memory applications, there are similar alternatives that can provide slightly better performance or be simpler to implement in practice. To maintain in memory computing for R tree in a computer cluster where computing nodes are connected by a network, researchers have used RDMA (Remote Direct Memory Access) to implement data intensive applications under R tree in a distributed environment. This approach is scalable for increasingly large applications and achieves high throughput and low latency performance for R tree. The key difficulty of R tree is to build an efficient tree that on one hand is balanced (so the leaf nodes are at the same height) on the other hand the rectangles do not cover too much empty space and do not overlap too much (so that during search, fewer subtrees need to be processed). For example, the original idea for inserting elements to obtain an efficient tree is to always insert into the subtree that requires least enlargement of its bounding box. Once that page is full, the data is split into two sets that should cover the minimal area each. Most of the research and improvements for R trees aims at improving the way the tree is built and can be grouped into two objectives: building an efficient tree from scratch (known as bulk loading) and performing changes on an existing tree (insertion and deletion). R trees do not guarantee good worst case performance, but generally perform well with real world data. While more of theoretical interest, the (bulk loaded) Priority R tree variant of the R tree is worst case optimal, but due to the increased complexity, has not received much attention in practical applications so far. When data is organized in an R tree, the neighbors within a given distance r and the k nearest neighbors (for any Lp Norm) of all points can efficiently be computed using a spatial join. This is beneficial for many algorithms based on such queries, for example the Local Outlier Factor. DeLi Clu, Density Link Clustering is a cluster analysis algorithm that uses the R tree structure for a similar kind of spatial join to efficiently compute an OPTICS clustering.

Data in R trees is organized in pages that can have a variable number of entries (up to some pre defined maximum, and usually above a minimum fill). Each entry within a non leaf node stores two pieces of data: a way of identifying a child node, and the bounding box of all entries within this child node. Leaf nodes store the data required for each child, often a point or bounding box representing the child and an external identifier for the child. For point data, the leaf entries can be just the points themselves. For polygon data (that often requires the storage of large polygons) the common setup is to store only the MBR (minimum bounding rectangle) of the polygon along with a unique identifier in the tree.