LAZY R-tree: The R-tree with lazy splitting algorithm

2019 ◽  
Vol 46 (2) ◽  
pp. 243-257
Author(s):  
Yang Yang ◽  
Pengwei Bai ◽  
Ningling Ge ◽  
Zhipeng Gao ◽  
Xuesong Qiu
Keyword(s):  
Data Structure ◽  
Spatial Data ◽  
Splitting Method ◽  
Hash Table ◽  
Spatial Index ◽  
Index Structure ◽  
Splitting Algorithm ◽  
Tree Construction ◽  
Tree Index

The spatial index is a data structure formed according to the position and shape of the spatial object or the relationship between the spatial objects according to certain rules, and the spatial data is managed by an effective spatial data structure. The quality of a spatial index directly affects the performance of spatial queries. The R-tree index structure is a highly efficient spatial index. According to the R-tree query rule, when performing spatial query, most data that is not related to the query condition can be filtered out, and finally, a few leaf nodes can be accessed to query the data satisfying the condition. Its query performance is affected by factors such as non-leaf node overlap and node space utilisation. This article proposes a lazy splitting method to improve the R-tree construction process. The scheme works as follows: (1) When a node overflows, it creates an overflow node for that node and all overflow nodes are saved in a hash table. (2) If the node continues to insert data, the data are added to its overflow node. (3) When an overflow node is saturated, the node and its overflow node are split into two saturated nodes. We use both simulated and actual data to perform experiments. The experimental results show that an R-tree constructed by the lazy algorithm is superior to an R-tree constructed using the original R-tree PM algorithm or the corner-based splitting (CBS) algorithm based on the number of splits created, the node space used and the efficiency of region queries and k-nearest neighbour (kNN) queries.

scholarly journals PROVIDING R-TREE SUPPORT FOR MONGODB

2016 ◽  
Vol XLI-B4 ◽  
pp. 545-549 ◽  
Author(s):  
Longgang Xiang ◽  
Xiaotian Shao ◽  
Dehao Wang
Keyword(s):  
Spatial Data ◽  
Relational Databases ◽  
Spatial Index ◽  
Geospatial Information ◽  
Nosql Databases ◽  
Document Structure ◽  
Depth Analysis ◽  
Novel Method ◽  
Tree Index ◽  
Management Issues

Supporting large amounts of spatial data is a significant characteristic of modern databases. However, unlike some mature relational databases, such as Oracle and PostgreSQL, most of current burgeoning NoSQL databases are not well designed for storing geospatial data, which is becoming increasingly important in various fields. In this paper, we propose a novel method to provide R-tree index, as well as corresponding spatial range query and nearest neighbour query functions, for MongoDB, one of the most prevalent NoSQL databases. First, after in-depth analysis of MongoDB’s features, we devise an efficient tabular document structure which flattens R-tree index into MongoDB collections. Further, relevant mechanisms of R-tree operations are issued, and then we discuss in detail how to integrate R-tree into MongoDB. Finally, we present the experimental results which show that our proposed method out-performs the built-in spatial index of MongoDB. Our research will greatly facilitate big data management issues with MongoDB in a variety of geospatial information applications.

scholarly journals PROVIDING R-TREE SUPPORT FOR MONGODB

2016 ◽  
Vol XLI-B4 ◽  
pp. 545-549
Author(s):  
Longgang Xiang ◽  
Xiaotian Shao ◽  
Dehao Wang
Keyword(s):  
Spatial Data ◽  
Relational Databases ◽  
Spatial Index ◽  
Geospatial Information ◽  
Nosql Databases ◽  
Document Structure ◽  
Depth Analysis ◽  
Novel Method ◽  
Tree Index ◽  
Management Issues

Supporting large amounts of spatial data is a significant characteristic of modern databases. However, unlike some mature relational databases, such as Oracle and PostgreSQL, most of current burgeoning NoSQL databases are not well designed for storing geospatial data, which is becoming increasingly important in various fields. In this paper, we propose a novel method to provide R-tree index, as well as corresponding spatial range query and nearest neighbour query functions, for MongoDB, one of the most prevalent NoSQL databases. First, after in-depth analysis of MongoDB’s features, we devise an efficient tabular document structure which flattens R-tree index into MongoDB collections. Further, relevant mechanisms of R-tree operations are issued, and then we discuss in detail how to integrate R-tree into MongoDB. Finally, we present the experimental results which show that our proposed method out-performs the built-in spatial index of MongoDB. Our research will greatly facilitate big data management issues with MongoDB in a variety of geospatial information applications.

A Mobile Storage System for Massive Spatial Data

2014 ◽  
Vol 962-965 ◽  
pp. 2730-2734
Author(s):  
Zhen Song ◽  
Jian Chen ◽  
Jiu Yan Ye
Keyword(s):  
Mobile Devices ◽  
Data Storage ◽  
Spatial Data ◽  
Storage System ◽  
Hierarchical Classification ◽  
Spatial Index ◽  
Index Structure ◽  
Real Time Visualization ◽  
Spatial Data Organization ◽  
Low Efficiency

This paper aims at the solution to problems of large amounts of data storage, low efficiency calculate and poor user experience which actually exists in GIS application on mobile devices. In order to solve these issues we use GIS technologies including spatial data organization, map browser, and spatial index. We focused on the research of how to effectively utilize system resources and rational and efficient out organize the spatial data. What’s more, we improved the R-tree indexing algorithm to establish a rapid spatial index structure and used hierarchical classification techniques to optimize the efficiency of the real-time visualization of spatial data for mobile devices.

A SEMANTIC INDEX STRUCTURE FOR MULTIMEDIA RETRIEVAL

2012 ◽  
Vol 06 (02) ◽  
pp. 155-178 ◽  
Author(s):  
FAUSTO C. FLEITES ◽  
SHU-CHING CHEN ◽  
KASTURI CHATTERJEE
Keyword(s):  
Nearest Neighbor ◽  
Multimedia Retrieval ◽  
Index Structure ◽  
Similarity Queries ◽  
Tree Construction ◽  
Efficient Access ◽  
Balanced Tree ◽  
High Level ◽  
Detrimental Impact

To be effective multimedia retrieval mechanisms, index methods must provide not only efficient access but also meaningful retrieval by addressing challenges in multimedia retrieval. This article presents the AH+-tree, a height-balanced, tree-based index structure that efficiently incorporates high-level affinity information to support Content-Based Image Retrieval (CBIR) through similarity queries. The incorporation of affinity information allows the AH+-tree to address the problems of semantic gap and user perception subjectivity inherent to multimedia retrieval. Based on the Affinity-Hybrid Tree (AH-Tree), the AH+-tree utilizes affinity information in a novel way to eliminate the I/O overhead of the AH-Tree while maintaining the same functionality and quality of results. We explain the structure of the AH+-tree and implement and analyze algorithms for tree construction and similarity queries (range and nearest neighbor). Experimental results demonstrate the superior I/O efficiency of the AH+-tree over that of the AH-Tree and the M-tree without a detrimental impact on real-time costs of the retrieval process.

A Kind of Index Technology Based on Binary Tree Efficient Space

2012 ◽  
Vol 239-240 ◽  
pp. 1537-1540
Author(s):  
Hong Ming Chen ◽  
Hai Yan Zhou
Keyword(s):  
Binary Tree ◽  
Time Complexity ◽  
Spatial Index ◽  
Index Structure ◽  
Search Efficiency ◽  
Research Subject ◽  
Quad Tree ◽  
Key Technologies ◽  
Tree Index ◽  
Graphics Processing

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.

Research on Cache Quadtree: A Cache Index for Spatial Data Based on Intelligent Material System

2011 ◽  
Vol 63-64 ◽  
pp. 795-799
Author(s):  
Zhong Jie Zhang ◽  
De Peng Zhao ◽  
De Qiang Wang
Keyword(s):  
Spatial Data ◽  
Spatial Index ◽  
Index Structure ◽  
Material System ◽  
Spatial Indexing ◽  
Data Query ◽  
Intelligent Material

This paper presents a new spatial index structure - cache quadtree. Cache quadtree combined with the advantages of spatial indexing and caching. According to the characteristics of spatial data, query uses the previous query results as much as possible, only the necessary queries are performed on the server. Meanwhile, full use of query results in the cache tree; reduce the server's query and its query range. Our paper gives the cache quadtree structure and the key algorithm in detail.

scholarly journals A QUADTREE SPATIAL INDEX METHOD WITH INCLUSION RELATIONS FOR THE INCREMENTAL UPDATING OF VECTOR LANDCOVER DATABASE

2018 ◽  
Vol XLII-4 ◽  
pp. 757-761
Author(s):  
X. G. Zhou ◽  
H. S. Wang
Keyword(s):  
Land Cover ◽  
Spatial Data ◽  
Spatial Index ◽  
Index Structure ◽  
Index Method ◽  
Incremental Updating ◽  
Indexing Methods ◽  
Data Indexing ◽  
Inclusion Relations ◽  
Complex Polygon

<p><strong>Abstract.</strong> In vector landcover database, there are a lot of complex polygons with many holes, even nesting holes. In the incremental updating (i.e., using the change-only information to update the land cover database), a new changed parcel usually has 2-dimensional intersections (e.g., overlap, cover, equal and inside, etc.) with several existing regions, automatic updating operations need to identify the affected objects for the new changes at first. If the existing parcels include complex polygons (i.e., the polygon with holes), it is still needed to determine if there are 2-dimensional intersections between the new changed polygon and each holes of the involved complex polygons. The relation between the complex polygon and its holes has not been presented in the current spatial data indexing methods, only the MBB (Minimum Bounding Box) of the exterior ring of the complex polygon has been stored, the non-involved holes can not be filtered at the first step of spatial access methods. As the refinement geometric operation is costly, therefore the updating process for the complex polygons is very complicated and low efficient using the current spatial data indexing methods. In order to solve this problem, an improved quadtree spatial index method is presented in this paper. In this method, the polygons is divided to two categories according to the relations with the quadrant axes, i.e., disjoint to the axes and intersect with the axes. The intersect polygons are still divided to 5 cases according to the intersection position among the polygons and the different level quadrant axes. The intersection polygons are stored in the different level root nodes in our index tree, and five buckets denoted as <i>XpB, XnB, YpB, YnB, XYB</i> are used to store the polygons intersecting the different level quadrant axes respectively. The polygons disjoint to all quadrant axes are stored in the leaf nodes in this method. The authors developed the spatial index structure with inclusion relations and the algorithms of the corresponding index operations (e.g., insert, delete and query) for the complex polygons. The effectiveness of the improved index is verified by an experiment of land cover data incremental updating. Experimental results show that the proposed index method is significantly more efficient than the traditional quadtree index in terms of spatial query efficiency, and the time efficiency of the incremental updating is increased about 3 times using the proposed index method than that using the traditional quadtree index.</p>

R-Tree Index Structure for Multi-Scale Representation of Spatial Data

2009 ◽  
Vol 32 (1) ◽  
pp. 177-184 ◽  
Author(s):  
Hong-Yan DENG ◽  
Fang WU ◽  
Ren-Jian ZAI ◽  
Qian ZHAO
Keyword(s):  
Spatial Data ◽  
Index Structure ◽  
Multi Scale ◽  
Tree Index

Study on the Implementation of Quadtree Index on E-Chart System

2011 ◽  
Vol 63-64 ◽  
pp. 627-632
Author(s):  
Zhong Jie Zhang ◽  
De Peng Zhao ◽  
De Qiang Wang
Keyword(s):  
Experimental Data ◽  
Mobile Devices ◽  
Spatial Data ◽  
Spatial Index ◽  
Index Structure ◽  
Index Structures ◽  
Application Development ◽  
Electronic Chart ◽  
Comparison And Analysis

As embedded and mobile devices application development and many micro-computing devices need to run the e-chart system. The tree spatial index structures can efficiently organize and retrieve spatial data. This paper discusses the index to organize and retrieve e-chart data through the quadtree. We discussed the electronic chart in the two typical quadtree on the index structure, and give the comparison and analysis of experimental data.

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