By Cui Yu
In this monograph, we research the matter of high-dimensional indexing and systematically introduce effective index constructions: one for diversity queries and the opposite for similarity queries. vast experiments and comparability experiences are performed to illustrate the prevalence of the proposed indexing methods.
Many new database functions, akin to multimedia databases or inventory fee details platforms, rework vital good points or homes of information gadgets into high-dimensional issues. trying to find gadgets in line with those gains is therefore a seek of issues during this function house. To aid effective retrieval in such high-dimensional databases, indexes are required to prune the hunt area. Indexes for low-dimensional databases are good studied, while every one of these software particular indexes usually are not scaleable with the variety of dimensions, and they're now not designed to aid similarity searches and high-dimensional joins.
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Additional info for High-Dimensional Indexing: Transformational Approaches to High-Dimensional Range and Similarity Searches
The generation of the signature of the range query is to map a ﬂoat vector to a small number of bits, where it loses in terms of accuracy. , a signature of size 20 bits (4 bits per dimension) in a 5-dimensional data space can address 100M diﬀerent data points. However, the number of bits needs careful tuning, and unfortunately, there are no guidelines for such tuning. To make the quantization more dynamic, instead of quantizing based based on quantiles, the IQ-tree , which is a three-level index structure, quantizes based on a regular decomposition of the page regions of the index.
Identiﬁcation of partitions The aim of indexing is to facilitate and speed up query retrieval on database. For point queries, iMax is easy to implement and eﬃcient computationally, since it only needs to calculate the iMax value of the query point and search the B+ -tree directly. To perform a range search using the iMax, the algorithm ﬁrst checks the partitions that overlap with the query region, which is a hyper-rectangle in high-dimensional data space. It then computes every subquery range with respect to the iMax space, and for each subquery, it traverses the tree once.
D1 (1,1) 1 111111111111 000000000000 (0,0) 1 d0 Fig. 6. 1% for range query. 5, the query hyper-cube always intersects the central line of data space. In this case, a range query will always be transformed into d subqueries, since every partition is intersected by the hyper-cube. When a range query is conducted on a d-dimensional data space, the query is divided into d subrange queries, each of which is a single-dimensional iMax value range. iMax value only presents the largest attribute on one dimension, so, when we search based on such values, the candidate data point set is large.