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