An extensible index for spatial databases

Spatial databases are optimized for the management of data stored based on their geometric space. Researchers through high degree scalability have proposed several spatial indexing structures towards this effect. Among these indexing structures is the X-tree. The existing X-trees and its variants are designed for dynamic environment, with the capability for handling insertions and deletions. Notwithstanding, the X-tree degrades on retrieval performance as dimensionality increases and brings about poor worst-case performance than sequential scan. We propose a new X-tree packing techniques for static spatial databases which performs better in space utilization through cautious packing. This new improved structure yields two basic advantage: It reduces the space overhead of the index and produces a better response time, because the aX-tree has a higher fan-out and so the tree always ends up shorter. New model for super-node construction and effective method for optimal packing using an improved str bulk-loading technique is proposed. The study reveals that proposed system performs better than many existing spatial indexing structures.

International journal of geographical information systems, 1990

We present a Reacl;Lue Døta Stntcture, that ß, a spatial data structure with detail leaels. The two properties, spatial organization and detail levels, are the basis for a Geographic Information System with a multi-scale database. A reactive data structure is a novel type of data structure catering to multiple detail levels with rapid responses to spatial queries. It is presented here as a modification of the binary space partitioning tree that includes detail levels. This tree is one of the few spatial data structures that does not organize space in a rectangular manner. An application of the reactive data structure in thematic mapping is given. A prototype system is being implemented. An important result of this implementation is that it shows that binary space partitioning trees of real maps have O(n) storage space complexity in contrast to the theoretical worst case O(nz), with n the number of line segments in the map.

2005

l'l1an~' evolving datil bilse applications warrant the use of non-twditional indexing mechanisms beyond B+-trees and hash tables. SP-GiST is an extensible indexing framework that broadens the class of supported indexes to include disk-based wrsions of a \vide variety of space-partitioning trees, e.g., disk-based trie variants, quadtree vilriants, and kd-trees. This paper presents il serious attempt at implementing and realizing SP-GiST-based indexes inside PostgreSQL. Several index types are realized inside PostgreSQL facilitated by rapid SP-GiST instantiations.

In multimedia databases, the spatial index structures based on trees (like R-tree, M-tree) have been proved to be efficient and scalable for low-dimensional data retrieval. However, if the data dimensionality is too high, the hierarchy of nested regions (represented by the tree nodes) becomes spatially indistinct. Hence, the query processing deteriorates to inefficient index traversal (in terms of random-access I/O costs) and in such case the tree-based indexes are less efficient than the sequential search. This is mainly due to repeated access to many nodes at the top levels of the tree. In this paper we propose a modified storage layout of tree-based indexes, such that nodes belonging to the same tree level are stored together. Such level-ordered storage allows to prefetch several top levels of the tree to the buffer pool by only a few or even a single contiguous I/O operation (i.e. one-seek read). The experimental results show that our approach can speedup the tree-based search significantly.

Proceedings of the 2002 ACM SIGMOD international conference on Management of data - SIGMOD '02, 2002

Spatial indexing has been one of the active focus areas in recent database research. Several variants of Quadtree and R-tree indexes have been proposed in database literature. In this paper, we first describe briefly our implementation of Quadtree and R-tree index structures and related optimizations in Oracle Spatial. We then examine the relative merits of t h e two structures as implemented in Oracle Spatial and compare their performance for different types of queries and other operations. Finally, we summarize our experiences with these different structures in indexing large GIS datasets in Oracle Spatial.

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

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.

In this paper, we introduce an indexing method for accessing spatial databases. The index structure described here is multi-dimensional and is an extension of Multilevel Grid File (MLGF) combined with the z-ordering technique to efficiently handle indexing on the spatial components of the objects. The other important property of the proposed index structure is to be able to index on fuzzy information and process fuzzy querying in spatial databases. Handling spatial, aspatial data and fuzzy information in the physical database is necessary to satisfy some of the requirements of the spatial database applications, i.e., the geographic information systems (GIS) applications. With our proposed multi-dimensional index structure (we call it ExMLGF in this paper), one can create an index structure on aspatial (and fuzzy) data along with spatial data on the same index structure and process aspatial, spatial queries and fuzzy/crisp queries efficiently for the spatial database applications. The ExMLGF access structure is designed and implemented in a way that database users can have fuzzy queries on both homogenous and heterogeneous domains. In this paper we include a number of algorithms for processing different kinds of queries in spatial databases.

Lecture Notes in Computer Science, 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 existed 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.