Titlebook: High-Dimensional Indexing; Transformational App Cui Yu Book 2002 Springer-Verlag Berlin Heidelberg 2002 B-Trees.Data Mining.Database Queryi

Tonometry

Introduction,e located by sequential scanning, they are the primary means of reducing the volume of data that must be fetched and examined in response to a query. In practice, large database files must be indexed to meet performance requirements. In fact, it has been noted [13] that indexes are the primary and most direct means in reducing redundant disk I/O.

聲明

High-Dimensional Indexing,ds or structures known as indexes to locate data of interest quickly and efficiently. Many indexes have been proposed for multi-dimensional databases and, in particular, for spatial databases. These indexes have been designed primarily for low-dimensional databases, and hence most of them suffer from the ‘dimensionality curse’.

mydriatic

Conclusions,he iMinMax(.) was specifically designed for range queries, it could be extended to efficiently support approximate .-nearest neighbor (KNN) queries. The iDistance [105] was designed for supporting KNN queries directly. The basic principle of both indexes is their simple extension of existing B.-trees, while achieving efficient query retrieval.