Parallel High-Dimensional Index Structure Using Cell-Based Filtering for Multimedia Data

2006 ◽  
pp. 781-790 ◽  
Author(s):  
Jae-Woo Chang ◽  
Yong-Ki Kim ◽  
Young-Jin Kim
Keyword(s):  
Multimedia Data ◽  
Index Structure ◽  
High Dimensional

FASTER SIMILARITY SEARCH FOR MULTIMEDIA DATA VIA QUERY TRANSFORMATIONS

2003 ◽  
Vol 03 (01) ◽  
pp. 3-29
Author(s):  
CHRISTIAN A. LANG ◽  
AMBUJ K. SINGH
Keyword(s):  
Similarity Search ◽  
Nearest Neighbor ◽  
Estimation Error ◽  
Fractal Dimensionality ◽  
Multimedia Data ◽  
Index Structure ◽  
High Dimensional ◽  
Range Queries ◽  
Query Execution ◽  
Real World Data

The performance of nearest neighbor (NN) queries degrades noticeably with increasing dimensionality of the data due to reduced selectivity of high-dimensional data and an increased number of seek operations during NN-query execution. If the NN-radii would be known in advance, the disk accesses could be reordered such that seek operations are minimized. We therefore propose a new way of estimating the NN-radius based on the fractal dimensionality and sampling. It is applicable to any page-based index structure. We show that the estimation error is considerably lower than for previous approaches. In the second part of the paper, we present two applications of this technique. We show how the radius estimations can be used to transform k-NN queries into at most two range queries, and how it can be used to reduce the number of page reads during all-NN queries. In both cases, we observe significant speedups over traditional techniques for synthetic and real-world data.

Tertiary hash tree-based index structure for high dimensional multimedia data

2010 ◽  
Vol 61 (1) ◽  
pp. 51-68 ◽  
Author(s):  
Yoon-Sik Tak ◽  
Seungmin Rho ◽  
Eenjun Hwang ◽  
Hanku Lee
Keyword(s):  
Multimedia Data ◽  
Index Structure ◽  
High Dimensional

Angle-Tree: a new index structure for high-dimensional point data

2001 ◽  
Author(s):  
Daoguo Dong ◽  
Xiangyang Xue ◽  
Hangzai Luo ◽  
Yingqiang Lin
Keyword(s):  
Index Structure ◽  
High Dimensional ◽  
Point Data

scholarly journals Learning in high-dimensional multimedia data: the state of the art

2015 ◽  
Vol 23 (3) ◽  
pp. 303-313 ◽  
Author(s):  
Lianli Gao ◽  
Jingkuan Song ◽  
Xingyi Liu ◽  
Junming Shao ◽  
Jiajun Liu ◽  
...  
Keyword(s):  
State Of The Art ◽  
The State ◽  
Multimedia Data ◽  
High Dimensional

scholarly journals HD-Tree: An Efficient High-Dimensional Virtual Index Structure Using a Half Decomposition Strategy

Algorithms ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 338
Author(s):  
Ting Huang ◽  
Zhengping Weng ◽  
Gang Liu ◽  
Zhenwen He
Keyword(s):  
Hash Table ◽  
Index Structure ◽  
High Dimensional ◽  
Sequential Search ◽  
Storage Space ◽  
Hash Tables ◽  
Time Performance ◽  
Indexing Method ◽  
Point Data ◽  
Better Than

To manage multidimensional point data more efficiently, this paper presents an improvement, called HD-tree, of a previous indexing method, called D-tree. Both structures combine quadtree-like partitioning (using integer shift operations without storing internal nodes, but only leaves) and hash tables (for searching for the nodes stored). However, the HD-tree follows a brand-new decomposition strategy, which is called half decomposition strategy. This improvement avoids the generation of nodes containing only a small amount of data and the sequential search of the hash table, so that it can save storage space while having faster I/O and better time performance when building the tree and querying data. The results demonstrate convincingly that the time and space performance of HD-tree is better than that of D-tree regardless of uniform or uneven data, which are less affected by data distribution.

CVA file: an index structure for high-dimensional datasets

2005 ◽  
Vol 7 (3) ◽  
pp. 337-357 ◽  
Author(s):  
Jiyuan An ◽  
Hanxiong Chen ◽  
Kazutaka Furuse ◽  
Nobuo Ohbo
Keyword(s):  
Index Structure ◽  
High Dimensional ◽  
High Dimensional Datasets
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