Editing B+ tree (section)

==== iDistance ====
{{further|iDistance}}

B+ tree is efficiently used to construct an indexed search method called iDistance. iDistance searches for k nearest neighbors (kNN) in high-dimension metric spaces. 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. In other words, the iDistance technique can be viewed as a way of accelerating the sequential scan. Instead of scanning records from the beginning to the end of the data file, the iDistance starts the scan from spots where the nearest neighbors can be obtained early with a very high probability.<ref>{{Cite journal |last1=Jagadish |first1=H. V. |last2=Ooi |first2=Beng Chin |last3=Tan |first3=Kian-Lee |last4=Yu |first4=Cui |last5=Zhang |first5=Rui |date=June 2005 |title=iDistance: An adaptive B+-tree based indexing method for nearest neighbor search |url=https://dl.acm.org/doi/10.1145/1071610.1071612 |journal=ACM Transactions on Database Systems |language=en |volume=30 |issue=2 |pages=364–397 |doi=10.1145/1071610.1071612 |s2cid=967678 |issn=0362-5915|url-access=subscription }}</ref>