A new extensible index structure, termed SP-GiST is presented that supports this class of data structures, mainly the class of space partitioning unbalanced trees. Simple method implementations are provided that demonstrate how SP-GiST can behave as a k-D tree, a trie, a quadtree, or any of their variants. Modern database applications show a growing demand for efficient and dynamic mana-gement of intervals, particularly for temporal and spatial data or for constraint handling. Common approaches require the augmentation of index structures which, however, is not supported by existing relational database systems. In the source code, the combined B+ tree and R tree index is referred to as a cirTree. One additional indexing trick is used. Because the stored data are sorted by chromosome and start position, not every item in the file must be indexed; in fact by default only every 512th item is indexed. From a practical point of view the R * -tree is very attractive because of the following two reasons 1 it efficiently supports point and spatial data at the same time and 2 its implementation cost is only slightly higher than that of other R-trees. References Points, lines, and regions are the three basic entities for constituting vector-based objects in spatial databases. Many indexing methods ( G -tree, K-D-B tree, Quad-tree, PMR -tree, Grid-file, R -tree, and so on) have been widely discussed for handling point or region data. This library provides a set of utility functions that can be used to create diagrams of the entire R-tree structure, including the root and all intermediate and leaf level nodes and entries. These features are optional, and the required dependencies are not automatically installed when installing this library. RECOMMENDED OPTIONAL READING Additional, optional citations, that may be useful for your project: Multimedia indexing. Spatial access methods: N. Beckmann, H.-P. Kriegel, R. Schneider B. Seeger The R*-Tree: an Efficient and Robust Access Method for Points and Rectangles ACM SIGMOD, May 1990, pp. 322-331 Atlantic City, NJ. (Deferred splitting in R-trees) In this paper, we propose a new method for indexing large amounts of point and spatial data in high-dimensional space. An analysis shows that index structures such as the R*-tree are not adequate for indexing high-dimensional data sets. Motivated by the increasing need to search efficiently through a huge number of service locations (e.g. restaurants, hotels, shops, and more), this paper presents an efficient spatial index QR-tree, a hybrid index structure of Quadtree and R-tree, instead of the exhaustive search to improve the performance in response to user queries. Antonin Guttman: R-Trees: A Dynamic Index Structure for Spatial Searching. SIGMOD Conference 1984: 47-57 [22] Joseph M. Hellerstein, Elias Koutsoupias, Christos H. Papadimitriou: On the Analysis of Indexing Schemes. PODS 1997: 249-256 [23] Gísli R. Hjaltason, Hanan Samet: Ranking in Spatial Databases. SSD 1995: 83-95 [24] R-Trees: A Dynamic Index Structure for Spatial Searching. Proc. ACM SIGMOD, Boston, Mass. • R-trees - main idea; file structure - algorithms: insertion/split - deletion - search: range, nn, spatial joins R-trees - nn search P2 q • what is really the worst of, say,