Περιέχει το πλήρες κείμενοThe problem of indexing large volumes of high dimensional data is an important and popular issue in the area of database management. There are many indexing methods that behave well in low dimensional spaces, but, in high dimensionalities, the phenomenon of the curse of dimensionality renders all indexes useless. For example, when issuing range queries almost all of the index pages have to be retrieved for answering these queries. In this paper we review the state-of-the-art research regarding high dimensional spaces and we demonstrate the dimensionality curse phenomenon using the TPIE KDBtree implementation
Περιέχει το πλήρες κείμενοIn this paper, we focus on the leaf level nodes of tree-like k-dimensiona...
In this paper, we propose the Pyramid-Technique, a new indexing method for high-dimensional data spa...
abstract: Similarity search in high-dimensional spaces is popular for applications like image proce...
Περιέχει το πλήρες κείμενοThe problem of indexing large volumes of high dimensional data is an impo...
Abstract The notorious “dimensionality curse ” is a wellknown phenomenon for any multi-dimensional i...
The notorious "dimensionality curse" is a well-known phenomenon for any multi-dimensional indexes at...
More and more data are produced every day. Some clustering techniques have been developed to automat...
Abstract. Indexing high dimensional datasets has attracted extensive attention from many researchers...
The notorious iodimensionality curseln is a well-known phenomenon for any multi-dimensional indexes ...
When dimension goes high, sequential scan processing becomes more efficient than most index-based qu...
In this work we study the validity of the so-called curse of dimensionality for indexing of database...
In this work we study the validity of the so-called curse of dimensionality for indexing of database...
Nearest neighbor queries are important in many settings, including spatial databases (Find the k clo...
The emergence of novel database applications has resulted in the prevalence of a new paradigm for si...
Nearest neighbor searching of large databases in high-dimensional spaces is inherently difficult due...
Περιέχει το πλήρες κείμενοIn this paper, we focus on the leaf level nodes of tree-like k-dimensiona...
In this paper, we propose the Pyramid-Technique, a new indexing method for high-dimensional data spa...
abstract: Similarity search in high-dimensional spaces is popular for applications like image proce...
Περιέχει το πλήρες κείμενοThe problem of indexing large volumes of high dimensional data is an impo...
Abstract The notorious “dimensionality curse ” is a wellknown phenomenon for any multi-dimensional i...
The notorious "dimensionality curse" is a well-known phenomenon for any multi-dimensional indexes at...
More and more data are produced every day. Some clustering techniques have been developed to automat...
Abstract. Indexing high dimensional datasets has attracted extensive attention from many researchers...
The notorious iodimensionality curseln is a well-known phenomenon for any multi-dimensional indexes ...
When dimension goes high, sequential scan processing becomes more efficient than most index-based qu...
In this work we study the validity of the so-called curse of dimensionality for indexing of database...
In this work we study the validity of the so-called curse of dimensionality for indexing of database...
Nearest neighbor queries are important in many settings, including spatial databases (Find the k clo...
The emergence of novel database applications has resulted in the prevalence of a new paradigm for si...
Nearest neighbor searching of large databases in high-dimensional spaces is inherently difficult due...
Περιέχει το πλήρες κείμενοIn this paper, we focus on the leaf level nodes of tree-like k-dimensiona...
In this paper, we propose the Pyramid-Technique, a new indexing method for high-dimensional data spa...
abstract: Similarity search in high-dimensional spaces is popular for applications like image proce...