A Bottom-Up Distance-Based Index Tree for Metric Space

2006 ◽  
pp. 442-449 ◽  
Author(s):  
Bing Liu ◽  
Zhihui Wang ◽  
Xiaoming Yang ◽  
Wei Wang ◽  
Baile Shi
Keyword(s):  
Metric Space ◽  
Bottom Up ◽  
Index Tree

scholarly journals A Study of Update Methods for BoND-Tree Index on Non-ordered Discrete Vector Data

10.29007/3zq4 ◽  
2020 ◽  
Author(s):  
Ramblin Cherniak ◽  
Qiang Zhu ◽  
Sakti Pramanik
Keyword(s):  
Original Work ◽  
Vector Data ◽  
Large Dataset ◽  
Bottom Up ◽  
Data Space ◽  
Tree Index ◽  
Increasing Demand ◽  
Index Tree ◽  
Discrete Domain ◽  
Query Algorithms

There is an increasing demand from numerous applications such as bioinformatics and cybersecurity to efficiently process various types of queries on datasets in a multidimensional Non-ordered Discrete Data Space (NDDS). An NDDS consists of vectors with values coming from a non-ordered discrete domain for each dimension. The BoND-tree index was recently developed to efficiently process box queries on a large dataset from an NDDS on disk. The original work of the BoND-tree focused on developing the index construction and query algorithms. No work has been reported on exploring efficient and effective up- date strategies for the BoND-tree. In this paper, we study two update methods based on two different strategies for updating the index tree in an NDDS. Our study shows that using the bottom-up update method can provide improved efficiency, comparing to the traditional top-down update method, especially when the number of dimensions for a vector that need to be updated is small. On the other hand, our study also shows that the two update methods have a comparable effectiveness, which indicates that the bottom-up update method is generally more advantageous.

Psychology and Culture: Top Down Versus Bottom Up?

PsycCRITIQUES ◽  
2005 ◽  
Vol 50 (19) ◽  
Author(s):  
Michael Cole
Keyword(s):  
Top Down ◽  
Bottom Up
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