Abstract. The M-tree is a dynamic data structure designed to index metric datasets. In this paper we introduce two dynamic techniques of building the M-tree. The first one incorporates a multi-way object inser-tion while the second one exploits the generalized slim-down algorithm. Usage of these techniques or even combination of them significantly increases the querying performance of the M-tree. We also present comparative experimental results on large datasets showing that the new techniques outperform by far even the static bulk loading algorithm
Searching in a dataset for elements that are similar to a given query element is a core problem in a...
Metric Access Methods (MAM) are employed to accelerate the processing of similarity queries, such as...
Title: Tree-based Indexing Methods for Similarity Search in Metric and Nonmetric Spaces Author: Mgr....
The M-tree is a dynamic data structure designed to index metric datasets. In this paper we introduce...
Since its introduction in 1997, the M-tree became a respected metric access method (MAM), while rema...
M-tree is a dynamic access method suitable to index generic "metric spaces", where the fun...
In this paper, we propose a new metric index, called M+-tree, which is a tree dynamically organized ...
This paper presents a new technique and two algorithms to bulk-load data into multi-way dynamic metr...
The M-tree is a well-known indexing method enabling efficient similarity search in metric spaces. Al...
This paper presents a new technique and two algorithms to bulk-load data into multi-way dynamic metr...
In this paper we present the Slim-tree, a dynamic tree for organizing metric datasets in pages of fi...
Abstract—Although metric access methods (MAMs) proved their capabilities when performing efficient s...
The M-tree is a well-known indexing method enabling efficient similarity search in metric spaces. Al...
The emergence of complex data objects that must to be indexed and accessed in databases has created ...
Searching in a dataset for elements that are similar to a given query element is a core problem in a...
Searching in a dataset for elements that are similar to a given query element is a core problem in a...
Metric Access Methods (MAM) are employed to accelerate the processing of similarity queries, such as...
Title: Tree-based Indexing Methods for Similarity Search in Metric and Nonmetric Spaces Author: Mgr....
The M-tree is a dynamic data structure designed to index metric datasets. In this paper we introduce...
Since its introduction in 1997, the M-tree became a respected metric access method (MAM), while rema...
M-tree is a dynamic access method suitable to index generic "metric spaces", where the fun...
In this paper, we propose a new metric index, called M+-tree, which is a tree dynamically organized ...
This paper presents a new technique and two algorithms to bulk-load data into multi-way dynamic metr...
The M-tree is a well-known indexing method enabling efficient similarity search in metric spaces. Al...
This paper presents a new technique and two algorithms to bulk-load data into multi-way dynamic metr...
In this paper we present the Slim-tree, a dynamic tree for organizing metric datasets in pages of fi...
Abstract—Although metric access methods (MAMs) proved their capabilities when performing efficient s...
The M-tree is a well-known indexing method enabling efficient similarity search in metric spaces. Al...
The emergence of complex data objects that must to be indexed and accessed in databases has created ...
Searching in a dataset for elements that are similar to a given query element is a core problem in a...
Searching in a dataset for elements that are similar to a given query element is a core problem in a...
Metric Access Methods (MAM) are employed to accelerate the processing of similarity queries, such as...
Title: Tree-based Indexing Methods for Similarity Search in Metric and Nonmetric Spaces Author: Mgr....