R-tree Data Structure Research Articles

АНАЛІЗ АЛГОРИТМІВ ВИЯВЛЕННЯ ЗІТКНЕНЬ У ТРИВИМІРНИХ ВІРТУАЛЬНИХ СЕРЕДОВИЩАХ

The problem addressed in this paper concerns the real-time detection of collisions in virtual environments, which can consume a significant portion of computational re-sources, particularly in complex simulations with numerous objects. Ensuring a minimum frame rate of 30 FPS is crucial for user-friendly simulations. The focus is on the narrow phase of collision detection, where objects in close proximity are examined for potential collisions. This paper proposes a collision detection algorithm leveraging Directed Bounding Parallels (AABB) R-tree structure and Oriented Bounding Parallelepiped Inter-section (OAABB) method to address this phase efficiently. Experimental results demon-strate the effectiveness of the algorithm in detecting intersections with high interaction rates. Various publicly available toolkits exist for narrow phase collision detection, em-ploying different bounding volume hierarchies and constraint volumes. Comparing these approaches is challenging due to performance variations influenced by factors such as object shapes, contact types, and model sizes. Nonetheless, these algorithms are crucial for accurately detecting collisions in virtual environments and gaming applications. Bounding Volume Hierarchies (BVH), commonly used for collision detection, organ-ize object geometry and improve performance by reducing test pair numbers through vol-ume constraints. The proposed algorithm utilizes R-tree hierarchies of directed bounding parallelepipeds and OAABB to enhance collision detection efficiency by minimizing up-date and intersect operations. The choice of constraint volume type impacts collision de-tection performance, with oriented parallelepipeds offering faster intersections. To expedite collision detection, each feature is represented by an R-tree data struc-ture in its local coordinate system. Hierarchical trees are constructed by grouping adja-cent surfaces, with leaves indicating surface geometry. Additionally, R-trees are em-ployed to spatially organize triangles within surfaces to quickly discard non-intersecting triangles. The algorithm leverages OAABB, a concept involving the common volume between two oriented bounding parallelepipeds, to filter out primitives that cannot intersect. Tra-versal algorithms are utilized to reduce the number of node visits, addressing inefficien-cies observed in traditional schemes. OAABB contributes significantly to reducing volume refresh operations. Experimental evaluations demonstrate the proposed algorithm's efficacy in real-world industrial applications, achieving interactive performance. Comparisons with al-ternative methods highlight the algorithm's effectiveness in collision detection. Overall, the proposed approach offers a robust solution to the narrow phase collision detection problem in virtual environments.

A PARALLEL ALGORITHM FOR LOCAL POINT DENSITY INDEX COMPUTATION OF LARGE POINT CLOUDS

Abstract. Point density is an important property that dictates the usability of a point cloud data set. This paper introduces an efficient, scalable, parallel algorithm for computing the local point density index, a sophisticated point cloud density metric. Computing the local point density index is non-trivial, because this computation involves a neighbour search that is required for each, individual point in the potentially large, input point cloud. Most existing algorithms and software are incapable of computing point density at scale. Therefore, the algorithm introduced in this paper aims to address both the needed computational efficiency and scalability for considering this factor in large, modern point clouds such as those collected in national or regional scans. The proposed algorithm is composed of two stages. In stage 1, a point-level, parallel processing step is performed to partition an unstructured input point cloud into partially overlapping, buffered tiles. A buffer is provided around each tile so that the data partitioning does not introduce spatial discontinuity into the final results. In stage 2, the buffered tiles are distributed to different processors for computing the local point density index in parallel. That tile-level parallel processing step is performed using a conventional algorithm with an R-tree data structure. While straight-forward, the proposed algorithm is efficient and particularly suitable for processing large point clouds. Experiments conducted using a 1.4 billion point data set acquired over part of Dublin, Ireland demonstrated an efficiency factor of up to 14.8/16. More specifically, the computational time was reduced by 14.8 times when the number of processes (i.e. executors) increased by 16 times. Computing the local point density index for the 1.4 billion point data set took just over 5 minutes with 16 executors and 8 cores per executor. The reduction in computational time was nearly 70 times compared to the 6 hours required without parallelism.

R-tree data structure implementation for Computer Aided Engineering (CAE) tools

Searching and handling geometric data are basic requirements of any Computer Aided Engineering application (CAE). Spatial search and local search has greater importance in CAD and CAE applications for reducing the model preparation time. There are many efficient algorithms being made to search geometrical data. Current neighbour search strategy is limited and not efficient in different CAE platforms. R-tree is tree data structure used for spatial access methods. This paper presents a review of R-tree data structure with its implementation in one of the CAE tool for neighbour search and local search. It satisfies current neighbour search requirements in CAE tools. Results shows considerable amount of time saving compared to the conventional approach. This work concludes that R-tree implementation can be helpful in identifying neighbour part and reducing model preparation time in CAD and CAE tools.

RMAP: a rectangular cuboid approximation framework for 3D environment mapping

This paper presents a rectangular cuboid approximation framework (RMAP) for 3D mapping. The goal of RMAP is to provide computational and memory efficient environment representations for 3D robotic mapping using axis aligned rectangular cuboids (RC). This paper focuses on two aspects of the RMAP framework: (i) An occupancy grid approach and (ii) A RC approximation of 3D environments based on point cloud density. The RMAP occupancy grid is based on the Rtree data structure which is composed of a hierarchy of RC. The proposed approach is capable of generating probabilistic 3D representations with multiresolution capabilities. It reduces the memory complexity in large scale 3D occupancy grids by avoiding explicit modelling of free space. In contrast to point cloud and fixed resolution cell representations based on beam end point observations, an approximation approach using point cloud density is presented. The proposed approach generates variable sized RC approximations that are memory efficient for axis aligned surfaces. Evaluation of the RMAP occupancy grid and approximation approach based on computational and memory complexity on different datasets shows the effectiveness of this framework for 3D mapping.

Event R-Tree Miner: an Efficient Approach to Mine Sequential Patterns from Spatio-Temporal Event Dataset

With the advances of technologies such as GPS, remote sensing, RFID, indoor locating devices, and geosensor networks, mining of spatio-temporal patterns seems more important to track spatio-temporal phenomena with increasingly finer spatial resolutions. Accordingly, we have presented an efficient approach to mine sequential patterns from spatio-temporal event datasets. Here, we make use of three major challenges, such as 1) definition of significance measure for finding spatio-temporal sequential pattern, 2) event tree, to reduce the running time and, 3) the definition of adaptive neighborhood to improve the accuracy. Based on these three challenges, R-tree data structure is utilized to construct the event R-tree for avoiding the accessing of database every time. Furthermore, follow ratio, a significance measure is proposed for finding the significant spatio-temporal sequential patterns and the sequence behavior of spatio-temporal events are identified with the help of adaptive neighborhood that is bounded with MBR condition. Finally, the experimentation is made with the real and synthetic dataset and we have proved the efficiency of the proposed event R-tree miner than the STS miner in terms of computation time

Research on Query Mechanism of Moving Object Database

Since the mobile radio communication in a wireless mobile environment, which inevitably subject to fading and interference effects of the various signals, multipath and time-domain signal to bring the mobile domain and frequency dispersion problems, bandwidth resources restricted and transmission delay increases and so on. In this paper, the use of Class R-Tree data structure to store the data on the basis of historical trajectory, the update query results by the beach line iterative way to solve the problem of nearest neighbor queries efficiently update existing intermediate results.

Machine Learning for Traffic Prediction

Recently, GPS car navigation became popular enough for a significant number of drivers in any large city to be able to potentially send their current GPS positions and velocities to a central traffic-managing server in real time. This data could be used to reconstruct a map of current traffic in the whole city and make predictions for the future, which would be valuable for traffic control, congestion analysis and prevention. The difficulty lying in the traffic reconstruction problem is that the number of cars equipped with a GPS transmitter is a small in comparison with the total number of cars in the road network. Information obtained from GPS transmitters is therefore rare and incomplete, raising the problem of approximating travel velocities from sparse information. In this context, the paper proposes approaches to solving two problems: (1) of reconstruction of routes of the individual cars in a street graph from a temporal stream of its coordinates and (2) of approximating the average velocity on a given street from irregular time series of instantaneous velocities of cars passing through that street. Determining the car's position in the road network is quite expensive computationally when done in a naive way, so we propose a method to speed up the car's localization based on the R-tree data structure [10]. Additionally, a resilient propagation neural network [13] is applied to approximate the average velocity on any edge of a street graph. Our methods were applied to one of the problems announced in the competition organized in June 2010 by Tom-Tom - a company producing automotive navigation systems [15]. The results of experiments show the capacity of our approach to make useful predictions.

대용량 선박위치정보 분석시스템 구축을 위한 Quad-Tree 및 R-Tree 자료구조 적용에 대한 연구

본 연구는 전국 연안을 항해하는 선박의 대량의 위치정보를 실시간으로 수신하여 선박의 운항상태를 분석하는 교통분석시스템의 성능을 높이기 위한 연구를 목적으로 하고 있다. 특히 교통분석시스템에서 분석시 필요한 정보의 빠른 검색을 위하여 Quad-Tree와 R-Tree 자료 구조를 적용하여 설계하고 기존의 교통분석시스템과 본 연구를 통해 개선된 교통분석시스템과의 비교를 통해 성능의 개선을 실험으로 보여주는데 있다. This study aims to facilitate and increase the performance of the Traffic Analysis System which receives the location information of vessels sailing along the coast all over the country in real time and analyzes the vessels' sailing situation. Especially, the research has a signification that the system is designed with the application of Quad-Tree and R-Tree data structure in order for system users to search necessary information quickly and effectively, and it verifies the improvement of the performance by showing experiment results comparing the existing Traffic Analysis System to newly upgraded Traffic Analysis System.

Semantic visualization of 3D urban environments

The purpose of this work is the semantic visualization of complex 3D city models containing numerous dynamic entities, as well as performing interactive semantic walkthroughs and flights without predefined paths. This is achieved by using a 3D multilayer scene graph that integrates geometric and semantic information as well as by the performance of efficient geometric and what we call semantic view culling. The proposed semantic-geometric scene graph is a 3D structure composed of several layers which is suitable for visualizing geometric data with semantic meaning while the user is navigating inside the 3D city model. BqR-Tree is the data structure specially developed for the geometric layer for the purpose of speeding up rendering time in urban scenes. It is an improved R-Tree data structure based on a quadtree spatial partitioning which improves the rendering speed of the usual R-trees when view culling is implemented in urban scenes. The BqR-Tree is defined by considering the city block as the basic and logical unit. The advantage of the block as opposed to the traditional unit, the building, is that it is easily identified regardless of the data source format, and allows inclusion of mobile and semantic elements in a natural way. The usefulness of the 3D scene graph has been tested with low structured data, which makes its application appropriate to almost all city data containing not only static but dynamic elements as well.

An optimized data structure for high-throughput 3D proteomics data: mzRTree

As an emerging field, MS-based proteomics still requires software tools for efficiently storing and accessing experimental data. In this work, we focus on the management of LC-MS data, which are typically made available in standard XML-based portable formats. The structures that are currently employed to manage these data can be highly inefficient, especially when dealing with high-throughput profile data. LC-MS datasets are usually accessed through 2D range queries. Optimizing this type of operation could dramatically reduce the complexity of data analysis. We propose a novel data structure for LC-MS datasets, called mzRTree, which embodies a scalable index based on the R-tree data structure. mzRTree can be efficiently created from the XML-based data formats and it is suitable for handling very large datasets. We experimentally show that, on all range queries, mzRTree outperforms other known structures used for LC-MS data, even on those queries these structures are optimized for. Besides, mzRTree is also more space efficient. As a result, mzRTree reduces data analysis computational costs for very large profile datasets.

A robust software watermarking for copyright protection

This paper advocates protecting software copyright through hiding watermarks in various data structures used by the code, e.g., B+-trees, R-trees, linked lists, etc. Prior proposals hide the watermarks in dummy data structures, e.g., linked lists and graphs that are created, solely for this reason, during the execution of the hosting software. This makes them vulnerable to subtractive attacks, because the attacker can remove the dummy data structures without altering the functionality or the semantic of the software program. We argue that hiding watermarks in one or more data structures that are used by the program would make the watermark more robust because disturbing the watermark would affect the semantic and the functionality of the underlying software. The challenge is that the insertion of the watermark should have a minimal effect on the operations and performance of the data structure. This paper proposes a novel method for watermarking R-tree data structure and its variants. The proposed watermarking technique does not change the values of the stored data objects. It takes advantage of the redundancy in the order of entries inside the R-tree nodes. Entries are arranged relative to a “secret” initial order, known only to the software owner, using a technique based on a numbering system that uses variable radix with factorial base. The addition of the watermark in the R-tree data structure does not affect the performance nor does it increase the size of the R-tree. The paper provides a detailed security analysis and performance evaluation to show that the embedded watermarks are robust and can withstand various types of attacks.

A broadband P2P MoD system: its implementation and application

This paper studies the scalable query process under Multimedia on Demand (MoD) system, and characters its data and queries its workloads. For the current system, our study shows poor performance in interaction and trans-media. The solutions we develop based on the Peer-to-Peer (P2P) MoD system and provide a new framework in which to tackle business model. The business model we present shares contents by integrated P2P technique and Sharable Content Object Reference Model (SCORM) concepts, and Cosine R-Tree data structure for system loading, content features clustering, contents redirection and user queries. Experiments validate the potential benefits of the new platform.

A Data-Driven Multidimensional Indexing Method for Data Mining in Astrophysical Databases

Large archives and digital sky surveys with dimensions of bytes currently exist, while in the near future they will reach sizes of the order of . Numerical simulations are also producing comparable volumes of information. Data mining tools are needed for information extraction from such large datasets. In this work, we propose a multidimensional indexing method, based on a static R-tree data structure, to efficiently query and mine large astrophysical datasets. We follow a top-down construction method, called VAMSplit, which recursively splits the dataset on a near median element along the dimension with maximum variance. The obtained index partitions the dataset into nonoverlapping bounding boxes, with volumes proportional to the local data density. Finally, we show an application of this method for the detection of point sources from a gamma-ray photon list.

A web-based spatial data access system using semantic R-trees

With the increasing use of geographical data in real-world applications, Geographic Information Systems (GISs) have recently emerged as a fruitful area for research. In order to provide information to a multitude of users, the World Wide Web (WWW) techniques have been integrated into GISs. A high-performance web-based GIS, called TerraFly, has been developed in order to provide web-based GIS accesses to the general public. The design of TerraFly considers two major aspects: (1) the system architecture including client, database server, proxy server and information server; and (2) the semantic R-tree data structure and semantic queries. The system architecture utilizes the existing resources to achieve maximum performance by using the “Internally Distributed Multithreading (IDMT)” technique. The spatial access method, semantic R-trees, is used to search for an object, based on both spatial and semantic information. System performance results are presented and analyzed. Reducing network traffic to achieve faster response to users is also discussed.

A study of concurrent operations on R-trees

Appropriate concurrent operations can improve R-tree efficiency. Supporting concurrency control algorithms for the original R-tree is necessary since many database systems have adopted the original R-tree data structure as the basic spatial data access method. This paper presents concurrency control algorithms that do not alter the original R-tree structure for range searches, insertions, and deletions. The algorithms not only yield correct results, but also ensure deadlock freedom. Cost analyses and lock efficiency are analyzed mathematically. Algorithm performance is evaluated by using a simulation model. Simulation results show the R-tree performs more efficiency if run in a multidisk environment with a low fanout structure.