Quadtree Index Research Articles

Geographic Elements Selection Algorithm Based on Quadtree in Variable-scale Visualization

In order to balance the demand between local and global visualization in the data acquisition, this paper adopts the variable-scale visualization technology and uses quadrangular frustum pyramid projection to show geographic information continuously on a mobile device. In addition, the geographic elements in the variable-scale transition region are crowd because of unceasingly scale changing. In order to solve this problem, this paper presents a quadtree-based geographic elements selection algorithm. That is to say when creating quadtree index, whether node segmentation is needed or not depends on the number of geographic elements which contained in the node space. If the node needs to be segmented, we introduce the selection evaluation function. This algorithm could optimize the display effect and efficiency of variable-scale visualization through abandoning geographic elements based on their evaluation function values. At last, experiments prove that this algorithm could better and faster accomplish variable-scale visualization.

A Kind of Index Technology Based on Binary Tree Efficient Space

Space index is one of the key technologies of spatial database, and also one of the biggest problems with puzzling GIS workers. So , how to build a more effective spatial index structure ,which has been the most realistic, most urgent, also to the forefront of research subject to GIS domain and graphics processing. This paper introduces several typical spatial index methods in the e GIS domain and graphics processing fields, and puts forward a binary tree index structure generated space grid step by step. The average time complexity of the index structure is the same with the quad tree index structure, but the maximum time complexity is reduced a third than the quadtree index structure, that is, the search efficiency improving a third.

Efficient SDH Computation In Molecular Simulations Data.

Analysis of large particle or molecular simulation data is integral part of the basic-science research community. It often involves computing functions such as point-to-point interactions of particles. Spatial distance histogram (SDH) is one such vital computation in scientific discovery. SDH is frequently used to compute Radial Distribution Function (RDF), and it takes quadratic time to compute using naive approach. Naive SDH computation is even more expensive as it is computed continuously over certain period of time to analyze simulation systems. Tree-based SDH computation is a popular approach. In this paper we look at different tree-based SDH computation techniques and briefly discuss about their performance. We present different strategies to improve the performance of these techniques. Specifically, we study the density map (DM) based SDH computation techniques. A DM is essentially a grid dividing simulated space into cells (3D cubes) of equal size (volume), which can be easily implemented by augmenting a Quad-tree (or Oct-tree) index. DMs are used in various configurations to compute SDH continuously over snapshots of the simulation system. The performance improvements using some of these configurations is presented in this paper. We also present the effect of utilizing computation power of Graphics Processing Units (GPUs) in computing SDH.

Methods of 3D map storage based on geo-referenced image database

In order to store and manage a large amount of ground and indoor image data with high resolution, an integrated data management system needs to be developed. Possible strategies for this purpose were discussed together with initial test on the newly defined 3D maps. The features of such 3D maps, data organization, key techniques used for the map storage, such as image compression based on wavelet transformation, quadtree index, update and retrieval, were analyzed, with the goals of bringing some profits to the storage and management of the digital data in the visual construction of digital mine, digital city and digital community.

A High-Efficient Data Model of Road Network in Vehicle Navigation System

Vehicle navigation system (VNS) is a world acknowledged efficient way of solving the urban traffic problem, and road network database is one of the core component parts in the system. In order to improve performance of VNS, this paper conducts a research on data organization technique of road network, and proposes a novel data model of road network, which includes spatial index and topology structure. Firstly, some common spatial index structures, such as regular grid index, R-tree index and quad-tree index, are studied, and a novel spatial index structure based on “hierarchical quad-tree and R-tree” index of two ranks is proposed. This structure can greatly reduce the access time of map data and raise index performance. Secondly, a topology model of road network based on arc-relation is presented, which can effectively solve turn penalty problems in the conventional topology model based on vertex-relation and represent real road network. Thirdly, a real topology storage structure using adjacency list is designed and a corresponding creating algorithm is put forward. Finally, the tests on the practical system prove that the proposed model effectively support kinds of data-processing and greatly raise the performance of the system.

Top- $\boldsymbol{k}$ query processing over uncertain data in distributed environments

Although top-k queries over uncertain data in centralized databases have been studied widely in recent years, it is still a challenging issue in distributed environments. In distributed environments, such as Peer-to-Peer (P2P) systems and sensor networks, there exists an inherent uncertainty on the data objects due to imprecise measurements and network delays. Therefore, it is necessary to study the problem of how to efficiently retrieve top-k uncertain data objects over distributed environments with minimum network overhead. In this paper, we propose a novel approach of processing uncertain top-k queries in large-scale P2P networks, where datasets are horizontally partitioned over peers. In our approach, each peer constructs an Uncertain Quad-Tree (UQ-Tree) index for its local uncertain data, while the P2P network constructs a global index by summarizing the local indexes. Based on the global index, we propose a spatial-pruning algorithm to reduce communication costs and a distributed-pruning algorithm to reduce computation costs. Extensive experiments are conducted to verify the effectiveness and efficiency of the proposed methods in terms of communication costs and response time.

Efficient Updates for OLAP Range Queries on Flash Memory

This paper explores efficient ways to use flash memory to store online analytical processing (OLAP) data. The particular type of queries considered are range queries using the aggregate functions SUM, COUNT and AVG. The asymmetric cost of reads and writes for flash memory gives higher importance to how updates are handled in a flash memory environment. A popular data structure used for answering OLAP range-sum queries is the prefix sum cube. It allows the range-sum query to be answered in constant time. However, updating the prefix sum cube is very expensive. To overcome this, the Δ-tree was proposed by Chun et al. (Dynamic update cube for range-sum queries. Proc. Int. Conf. Very Large Data Bases, San Francisco, CA, USA, 2001, pp. 521–530. Morgan Kaufmann Publisher). The Δ-tree stores all updates to the prefix sum cube in a separate r-tree. This approach worked well for the hard disk where in-place updates are relatively cheap. However, for flash memory where in-place updates are very expensive, the Δ-tree performs very poorly. We take a four-pronged approach to overcome the problem of expensive in-place updates. The first is efficient caching of updates in RAM. The second is writing out whole trees from RAM to flash memory instead of incrementally updating a disk resident tree. The third is we allow users to trade bounded amounts of accuracy for less updates via lossy compression. Finally, we use a quadtree index structure instead of the R-tree. We prove that the quadtree compression problem is NP-complete. A greedy heuristic is proposed to find near optimal solutions in polynomial time. Various experiments were conducted to compare the proposed algorithms against the existing Δ-tree. The results show that our algorithms consistently outperformed Δ-tree by factors of between 10 and 100. This demonstrates the importance of designing flash memory customized algorithms for OLAP range queries. In addition, among our algorithms, the error bound solutions with a small error bound setting significantly outperform the accurate solution in terms of performance for a variety of parameter settings. This indicates that the error bound algorithms offer users an effective trade-off between execution time and accuracy.

An Optimal Algorithm for Searching the Optimal Translation of Query Windows in Quadtree Decomposition

One of the efficient methods to build the index of continuous window queries over moving objects is by means of region quadtree index. In this paper, we present an optimal algorithm to search for the optimal position translation of query windows, where the total number of decomposed quadtree blocks for those windows in quadtree representation is minimal. We exploit the branch-and-bound concept to prune the particular paths of recursions in the search space. Evaluation proves that our optimal algorithm reduces search time greatly and the quadtree index based on optimal position translation works efficiently for continuous window queries. To the best of our knowledge, the algorithms and experiments reported in this paper are novel.

Using a distributed quadtree index in peer-to-peer networks

Peer-to-peer (P2P) networks have become a powerful means for online data exchange. Currently, users are primarily utilizing these networks to perform exact-match queries and retrieve complete files. However, future more data intensive applications, such as P2P auction networks, P2P job-search networks, P2P multiplayer games, will require the capability to respond to more complex queries such as range queries involving numerous data types including those that have a spatial component. In this paper, a distributed quadtree index that adapts the MX-CIF quadtree is described that enables more powerful accesses to data in P2P networks. This index has been implemented for various prototype P2P applications and results of experiments are presented. Our index is easy to use, scalable, and exhibits good load-balancing properties. Similar indices can be constructed for various multidimensional data types with both spatial and non-spatial components.

Data structures for fast searching of SEG-Y seismic data

An index structure for seismic data was designed using a modified PR quadtree data structure and implemented in the C++ computer language. To test the software and to evaluate the implementation of the structures, experiments were carried out with data from a marine seismic survey in the Mid-Pacific Ocean. The section of the survey used spanned 6 days, consisted of ten seismic lines (total of 1904 line kilometres) with a total of 39,720 traces. The seismic data (in SEG-Y format) required 335.8 Mbyte storage on disk. Experimental results of range searching (in two dimensions) with varying query window sizes shows that less than 0.1 s is required to determine which seismic lines intersect the query window. The index structure occupies 9.19 Mbyte (about 2.74% of the original data size) and requires 34.5 s to build using a UNIX® workstation. The modified PR quadtree index structure was compared to a standard PR quadtree index structure, and found to require less time (by up to 67%) to perform a 2D range search.