53,027 research outputs found
Large Spatial Database Indexing with aX-tree
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 structure
Two-layer Space-oriented Partitioning for Non-point Data
Non-point spatial objects (e.g., polygons, linestrings, etc.) are ubiquitous.
We study the problem of indexing non-point objects in memory for range queries
and spatial intersection joins. We propose a secondary partitioning technique
for space-oriented partitioning indices (e.g., grids), which improves their
performance significantly, by avoiding the generation and elimination of
duplicate results. Our approach is easy to implement and can be used by any
space-partitioning index to significantly reduce the cost of range queries and
intersection joins. In addition, the secondary partitions can be processed
independently, which makes our method appropriate for distributed and parallel
indexing. Experiments on real datasets confirm the advantage of our approach
against alternative duplicate elimination techniques and data-oriented
state-of-the-art spatial indices. We also show that our partitioning technique,
paired with optimized partition-to-partition join algorithms, typically reduces
the cost of spatial joins by around 50%.Comment: To appear in the IEEE Transactions on Knowledge and Data Engineerin
An information-driven framework for image mining
[Abstract]: Image mining systems that can automatically extract semantically meaningful information (knowledge) from image data are increasingly in demand. The fundamental challenge in image mining is to determine how low-level, pixel representation contained in a raw image or
image sequence can be processed to identify high-level spatial objects and relationships. To meet
this challenge, we propose an efficient information-driven framework for image mining. We distinguish four levels of information: the Pixel Level, the Object Level, the Semantic Concept Level, and the Pattern and Knowledge Level. High-dimensional indexing schemes and retrieval
techniques are also included in the framework to support the flow of information among the levels. We believe this framework represents the first step towards capturing the different levels of information present in image data and addressing the issues and challenges of discovering useful
patterns/knowledge from each level
A Survey on Spatial Indexing
Spatial information processing has been a centre of attention of research in the previous decade. In spatial databases, data related with spatial coordinates and extents are retrieved based on spatial proximity. A large number of spatial indexes have been proposed to make ease of efficient indexing of spatial objects in large databases and spatial data retrieval. The goal of this paper is to review the advance techniques of the access methods. This paper tries to classify the existing multidimensional access methods, according to the types of indexing, and their performance over spatial queries. K-d trees out performs quad tress without requiring additional memory usage
Using Images to create a Hierarchical Grid Spatial Index
This paper presents a hybrid approach to spatial indexing of two dimensional
data. It sheds new light on the age old problem by thinking of the traditional
algorithms as working with images. Inspiration is drawn from an analogous
situation that is found in machine and human vision. Image processing
techniques are used to assist in the spatial indexing of the data. A fixed grid
approach is used and bins with too many records are sub-divided hierarchically.
Search queries are pre-computed for bins that do not contain any data records.
This has the effect of dividing the search space up into non rectangular
regions which are based on the spatial properties of the data. The bucketing
quad tree can be considered as an image with a resolution of two by two for
each layer. The results show that this method performs better than the quad
tree if there are more divisions per layer. This confirms our suspicions that
the algorithm works better if it gets to look at the data with higher
resolution images. An elegant class structure is developed where the
implementation of concrete spatial indexes for a particular data type merely
relies on rendering the data onto an image.Comment: In Proceedings of the IEEE International Conference on Systems, Man
and Cybernetics, Taiwan, 2006, pp. 1974-197
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