A new range query algorithm for Universal B-trees

1998

We investigate the usability and performance of the UB-Tree (universal B-Tree) for multidimensional data, as they arise in all relational databases and in particular in data- warehousing and data-mining applications. The UB-Tree is balanced and has all the guaranteed performance characteristics of B-Trees, i.e., it requires linear space for storage and logarithmic time for the basic operations of insertion, retrieval

2014

Range queries are a widely-used type of similarity queries that find all objects within a given distance from the query object. In this paper, we propose an approximate range query algorithm for the NDtree, a multi-dimensional index for vectors with nonordered discrete components. By sacrificing a little accuracy, approximate algorithms generally can greatly improve search performance. Our proposed approximate algorithm maintains a priority queue of tree nodes whose bounding rectangles (BR) intersect the query sphere. But it only accesses a user-specified portion of the queue. We propose a novel volumebased weighting scheme for the priority queue. The idea is that tree nodes whose BR has a larger intersection with the query sphere contain more result objects, thus should be accessed earlier. A closed-form formula is derived to calculate the volume of an intersection. Our experimental study using both synthetic and real data shows that the proposed algorithm can significantly improve...

2006 10th International Database Engineering and Applications Symposium (IDEAS'06), 2006

Multi-dimensional data structures are applied in many real index applications, i.e. data mining, indexing multimedia data, indexing nonstructured text documents and so on. Many index structures and algorithms have been proposed. There are two major approaches to multi-dimensional indexing. These are, data structures to indexing metric and vector spaces. The R-tree, R*-tree, and UB-tree are representatives of the vector data structures. These data structures provide efficient processing for many types of queries, i.e. point queries, range queries and so on. As far as the vector data structures are concerned the range query retrieves all points in defined hyper box in an n-dimensional space. The narrow range query is a significant type of the range query. Its processing is inefficient in the vector data structures. Moreover, the efficiency decreases from increase dimension of an indexed space. We depict an application of the signature for more efficient processing of narrow range queries. The approach puts the signature into the R-tree but native functionalities are preserved, i.e. the range query algorithm for general range query. The novel data structure is called the Signature R-tree. This data structure is more resistant to the curse of dimensionality.

Submitten at VLDB, 2004

Multi-dimensional data structures are applied in many real index applications, i.e. data mining, indexing multimedia data, indexing nonstructured text documents and so on. Many index structures and algorithms have been proposed. There are two major approaches to multi-dimensional indexing. These are, data structures to indexing metric and vector spaces. The R-tree, R*-tree, and UB-tree are representatives of the vector data structures. These data structures provide efficient processing for many types of queries, i.e. point queries, range queries and so on. As far as the vector data structures are concerned the range query retrieves all points in defined hyper box in an n-dimensional space. The narrow range query is a significant type of the range query. Its processing is inefficient in the vector data structures. Moreover, the efficiency decreases from increase dimension of an indexed space. We depict an application of the signature for more efficient processing of narrow range queries. The approach puts the signature into the R-tree but native functionalities are preserved, i.e. the range query algorithm for general range query. The novel data structure is called the Signature R-tree. This data structure is more resistant to the curse of dimensionality.

Information Systems, 1982

A new method for multiple attribute indexing, the Multidimensional B-Tree (MBDT), is developed. This method is well suited for dynamic databases, since it handles several types of associative queries efficiently and requires low-cost maintenance. Algorithms and search strategies for exact match, partial match, and range queries are presented and statistical procedures are given to estimate the average and worst case retrieval times. The applicability of our organization to practical databases is discussed and analytical tradeoffs with regard to index organizations based on k-d trees are established.

2009

Semantic query optimization consists in restricting the search space in order to reduce the set of objects of interest for a query. This paper presents an indexing method based on UB-trees and a static analysis of the constraints associated to the views of the database and to any constraint expressed on attributes. The result of the static analysis is a partitioning of the object space into disjoint blocks. Through Space Filling Curve (SFC) techniques, each fragment (block) of the partition is assigned a unique identifier, enabling the efficient indexing of fragments by UB-trees. The search space corresponding to a range query is restricted to a subset of the blocks of the partition. This approach has been developed in the context of a KB-DBMS but it can be applied to any relational system.

2006

Multi-dimensional data structures are applied in many real index applications, i.e. data mining, indexing multimedia data, indexing nonstructured text documents and so on. Many index structures and algorithms have been proposed. There are two major approaches to multi-dimensional indexing. These are, data structures to indexing metric and vector spaces. The R-tree, R*-tree, and UB-tree are representatives of the vector data structures. These data structures provide efficient processing for many types of queries, i.e. point queries, range queries and so on. As far as the vector data structures are concerned the range query retrieves all points in defined hyper box in an n-dimensional space. The narrow range query is a significant type of the range query. Its processing is inefficient in the vector data structures. Moreover, the efficiency decreases from increase dimension of an indexed space. We depict an application of the signature for more efficient processing of narrow range queries. The approach puts the signature into the R-tree but native functionalities are preserved, i.e. the range query algorithm for general range query. The novel data structure is called the Signature R-tree. This data structure is more resistant to the curse of dimensionality.

2009

Indexing methods for efficient processing of multidimensional data are very requested in many fields, like geographical information systems, drawing documentations etc. Well-known R-tree is one of the multidimensional data structures. The R-tree is based on bounding of spatial near points by multidimensional rectangles. This data structure supports various types of queries, e.g. point and range queries. The range query retrieves all tuples of a multidimensional space in the defined query box. Narrow range query is an important type of the range query includ- ing at least one narrow dimension. Despite many variants of R-trees, narrow range query processing is inefficient. In this paper, we depict a modification of Signature R-tree: data structure for the narrow range query processing. This data structure applies signatures for a descrip- tion of tuples stored in a tree's page. We present an improvement of this technique.

Proc. of the 8th Int'l Conf. on Database Systems …, 2003

2000

Multi-dimensional data structures are applied in many real index applications, i.e. data min- ing, indexing multimedia data, indexing non- structured text documents and so on. Many index structures and algorithms have been proposed. There are two major approaches to multi-dimensional indexing. These are, data structures to indexing metric and vec- tor spaces. The R-tree, R*-tree, and UB-tree are representatives of