Add to ORKGΠεριέχει 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
Abstract. Indexing high dimensional datasets has attracted extensive attention from many researchers...
Nearest neighbor searching of large databases in high-dimensional spaces is inherently difficult due...
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 impor...
Abstract The notorious “dimensionality curse ” is a wellknown phenomenon for any multi-dimensional i...
In this work we study the validity of the so-called curse of dimensionality for indexing of database...
More and more data are produced every day. Some clustering techniques have been developed to automat...
Nearest neighbor queries are important in many settings, including spatial databases (Find the k clo...
In this work we study the validity of the so-called curse of dimensionality for indexing of database...
The notorious "dimensionality curse" is a well-known phenomenon for any multi-dimensional indexes at...
In this work, we revisit the curse of dimensionality, especially the concentration of the norm pheno...
Περιέχει το πλήρες κείμενοIn this paper, we focus on the leaf level nodes of tree-like k-dimensiona...
When dimension goes high, sequential scan processing becomes more efficient than most index-based qu...
The notorious iodimensionality curseln is a well-known phenomenon for any multi-dimensional indexes ...
The emergence of novel database applications has resulted in the prevalence of a new paradigm for si...
Abstract. Indexing high dimensional datasets has attracted extensive attention from many researchers...
Nearest neighbor searching of large databases in high-dimensional spaces is inherently difficult due...
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 impor...
Abstract The notorious “dimensionality curse ” is a wellknown phenomenon for any multi-dimensional i...
In this work we study the validity of the so-called curse of dimensionality for indexing of database...
More and more data are produced every day. Some clustering techniques have been developed to automat...
Nearest neighbor queries are important in many settings, including spatial databases (Find the k clo...
In this work we study the validity of the so-called curse of dimensionality for indexing of database...
The notorious "dimensionality curse" is a well-known phenomenon for any multi-dimensional indexes at...
In this work, we revisit the curse of dimensionality, especially the concentration of the norm pheno...
Περιέχει το πλήρες κείμενοIn this paper, we focus on the leaf level nodes of tree-like k-dimensiona...
When dimension goes high, sequential scan processing becomes more efficient than most index-based qu...
The notorious iodimensionality curseln is a well-known phenomenon for any multi-dimensional indexes ...
The emergence of novel database applications has resulted in the prevalence of a new paradigm for si...
Abstract. Indexing high dimensional datasets has attracted extensive attention from many researchers...
Nearest neighbor searching of large databases in high-dimensional spaces is inherently difficult due...
abstract: Similarity search in high-dimensional spaces is popular for applications like image proce...