A Proposal Of Index For High-Dimensional Static Databases

Distributed and Parallel Databases, 2005

Multidimensional indexing is concerned with the indexing of multi-attributed records, where queries can be applied on some or all of the attributes. Indexing multi-attributed records is referred to by the term multidimensional indexing because each record is viewed as a point in a multidimensional space with a number of dimensions that is equal to the number of attributes. The values of the point coordinates along each dimension are equivalent to the values of the corresponding attributes. In this paper, the PN-tree, a new index structure for multidimensional spaces, is presented. This index structure is an efficient structure for indexing multidimensional points and is parallel by nature. Moreover, the proposed index structure does not lose its efficiency if it is serially processed or if it is processed using a small number of processors. The PN-tree can take advantage of as many processors as the dimensionality of the space. The PN-tree makes use of B + -trees that have been developed and tested over years in many DBMSs. The PN-tree is compared to the Hybrid tree that is known for its superiority among various index structures. Experimental results show that parallel processing of the PN-tree reduces significantly the number of disk accesses involved in the search operation. Even in its serial case, the PN-tree outperforms the Hybrid tree for large database sizes.

2007

Over the last two decades, much research effort has been spent on nearest neighbor search in high-dimensional data sets. Most of the approaches published thus far have, however, only been tested on rather small collections. When large collections have been considered, high-performance environments have been used, in particular systems with a large main memory. Accessing data on disk has largely been avoided because disk operations are considered to be too slow. It has been shown, however, that using large amounts of memory is generally not an economic choice. Therefore, we propose the NV-tree, which is a very efficient disk-based data structure that can give good approximate answers to nearest neighbor queries with a single disk operation, even for very large collections of high-dimensional data. Using a single NV-tree, the returned results have high recall but contain a number of false positives. By combining two or three NV-trees, most of those false positives can be avoided while retaining the high recall. Finally, we compare the NV-tree to Locality Sensitive Hashing, a popular method for-distance search. We show that they return results of similar quality, but the NV-tree uses many fewer disk reads.

Technical Indexing of spatial database is discussed in this paper. Several methods of indexing moving objects will be presented taking into account the faults and strengths of each one, the two main types of applications that manage moving objects will be discussed. "2-level index" is one of the recent variant indexing relied on R-tree, thereafter this variant will be presented and located between types of applications, its principle performance will be explained, the main defect which is redundancy of nodes after each update will be shown, and finally our contribution to optimize it will be presented.

2001

Nowadays feature vector based similarity search is increasingly emerging in database systems. Consequently, many multidimensional data index techniques have been widely introduced to database researcher community. These index techniques are categorized into two main classes: SP (space partitioning)/KD-tree-based and DP (data partitioning)/R-tree-based. Recently, a hybrid index structure has been proposed. It combines both SP/KDtree-based and DP/R-tree-based techniques to form a new, more efficient index structure. However, weaknesses are still existing in techniques above. In this paper, we introduce a novel and flexible index structure for multidimensional data, the SH-tree (Super Hybrid tree). Theoretical analyses show that the SHtree is a good combination of both techniques with respect to both presentation and search algorithms. It overcomes the shortcomings and makes use of their positive aspects to facilitate efficient similarity searches.

International Journal of Database Management Systems

Tracing moving objects have turned out to be essential in our life and have a lot of uses like: GPS guide, traffic monitor based administrations and location-based services. Tracking the changing places of objects has turned into important issues. The moving entities send their positions to the server through a system and large amount of data is generated from these objects with high frequent updates so we need an index structure to retrieve information as fast as possible. The index structure should be adaptive, dynamic to monitor the locations of objects and quick to give responses to the inquiries efficiently. The most wellknown kinds of queries strategies in moving objects databases are Rang, Point and K-Nearest Neighbour and inquiries. This study uses R-tree method to get detailed range query results efficiently. But using R-tree only will generate much overlapping and coverage between MBR. So R-tree by combining with Gridpartition index is used because grid-index can reduce the overlap and coverage between MBR. The query performance will be efficient by using these methods. We perform an extensive experimental study to compare the two approaches on modern hardware.

The main aim of this paper is to develop a new dynamic indexing structure to support very large datasets and high dimensionality. This new structure is tree based used to facilitate efficient access. It is highly adaptable to any type of applications. The newly developed structure is based on nearest neighbors' method with exception of linearly scan the very large datasets. The NewTree surely minimizes adverse effect of the curse of dimensionality. It means that the most existing indexing techniques degrade rapidly when dimensionality goes higher. The major drawback here is the retrieval of subsets from the huge storage system. The NewTree structure can handle very efficiently and effectively during adding new data. When the new data are added and the shape of the structure does not change. The performance of the newly developed structure can be evaluated with SR Tree, existing indexing structure. The results clearly show that the efficiency of the newly developed structure is superior in both time complexity and memory complexity than SR Tree.

2010

Similarity search in high-dimensional metric spaces is a key operation in many applications, such as multimedia databases, image retrieval, object recognition, and others. The high dimensionality of the data requires special index structures to facilitate the search. A problem regarding the creation of suitable index structures for highdimensional data is the relationship between the geometry of the data and the organization of an index structure. In this paper, we study the performance of a new index structure, called Divisive-Agglomerative Hierarchical Clustering tree (DAHC-tree), which reduces the effects imposed by the above liability. DAHC-tree is constructed by dividing and grouping the data set into compact clusters. We perform a rigorous experimental design and analyze the trade-offs involved in building such an index structure. Additionally, we present extensive experiments comparing our method against state-of-the-art of exact and approximate solutions. The conducted analysis and the reported comparative test results demonstrate that our technique significantly improves the performance of similarity queries.

In order to handle spatial data efficiently, as required in computer aided design and geo-data applications, a database system needs an mdex mechanism that ti help it retrieve data items quickly accordmg to their spatial locations However, traditional mdexmg methods are not well suited to data oblects of non-zero size located m multidimensional spaces In this paper we describe a dynarmc mdex structure called an R-tree winch meets this need, and give algorithms for searching and updatmg it. We present the results of a series of tests which indicate that the structure performs well, and conclude that it is useful for current database systems m spatial applications

2001

Emerging database applications require the use of new indexing structures beyond B-trees and R-trees. Examples are the k-D tree, the trie, the quadtree, and their variants. They are often proposed as supporting structures in data mining, GIS, and CAD/CAM applications. A common feature of all these indexes is that they recursively divide the space into partitions. A new extensible index structure, termed SP-GiST, is presented that supports this class of data structures, mainly the class of space partitioning unbalanced trees. Simple method implementations are provided that demonstrate how SP-GiST can behave as a k-D tree, a trie, a quadtree, or any of their variants. Issues related to clustering tree nodes into pages as well as concurrency control for SP-GiST are addressed. A dynamic minimum-height clustering technique is applied to minimize disk accesses and to make using such trees in database systems possible and efficient. A prototype implementation of SP-GiST is presented as well as performance studies of the various SP-GiST's tuning parameters.

2009

Mobile query processing is, currently, a very active research field. Range and nearest neighbor queries are commonly used in spatiotemporal databases and location based services (LBS). In this paper, we focus on finding nearest neighbors of a query point within a certain distance range. We propose a new indexing structure CN-tree, Compact N-tree, based on a recent indexing technique called N-tree. CN-tree joins efficiency of N-tree's data partitioning scheme to pertinent objects' approximation with minimal bounding rectangles of R-trees which are reported to be the best performing for range search. We show how we use the approximation in constructing CN-tree and, then, how this index can support range queries efficiently by minimizing computation of distances and avoiding overlapping of minimal bounding rectangles. The experimental results through the comparison with the well know R*-tree, show that the proposed CN-tree widely outperforms R*-tree as an in-memory index and it presents competitive performances when used as an in-disk index.