scholarly journals A Hierarchical Spatial Network Index for Arbitrarily Distributed Spatial Objects

2021 ◽  
Vol 10 (12) ◽  
pp. 814
Author(s):  
Xiangqiang Min ◽  
Dieter Pfoser ◽  
Andreas Züfle ◽  
Yehua Sheng
Keyword(s):  
Spatial Data ◽  
Main Idea ◽  
Range Query ◽  
Access Methods ◽  
Spatial Objects ◽  
Density Based Clustering ◽  
Node Distribution ◽  
Query Algorithm ◽  
Information Systems Use ◽  
Network Space

The range query is one of the most important query types in spatial data processing. Geographic information systems use it to find spatial objects within a user-specified range, and it supports data mining tasks, such as density-based clustering. In many applications, ranges are not computed in unrestricted Euclidean space, but on a network. While the majority of access methods cannot trivially be extended to network space, existing network index structures partition the network space without considering the data distribution. This potentially results in inefficiency due to a very skewed node distribution. To improve range query processing on networks, this paper proposes a balanced Hierarchical Network index (HN-tree) to query spatial objects on networks. The main idea is to recursively partition the data on the network such that each partition has a similar number of spatial objects. Leveraging the HN-tree, we present an efficient range query algorithm, which is empirically evaluated using three different road networks and several baselines and state-of-the-art network indices. The experimental evaluation shows that the HN-tree substantially outperforms existing methods.

A method for efficient clustering of spatial data in network space

2021 ◽  
pp. 1-18
Author(s):  
Trang T.D. Nguyen ◽  
Loan T.T. Nguyen ◽  
Anh Nguyen ◽  
Unil Yun ◽  
Bay Vo
Keyword(s):  
Spatial Data ◽  
Euclidean Distance ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Spatial Data Mining ◽  
Spatial Data Analysis ◽  
Clustering Methods ◽  
Dbscan Algorithm ◽  
Density Based Clustering ◽  
Network Space

Spatial clustering is one of the main techniques for spatial data mining and spatial data analysis. However, existing spatial clustering methods primarily focus on points distributed in planar space with the Euclidean distance measurement. Recently, NS-DBSCAN has been developed to perform clustering of spatial point events in Network Space based on a well-known clustering algorithm, named Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The NS-DBSCAN algorithm has efficiently solved the problem of clustering network constrained spatial points. When compared to the NC_DT (Network-Constraint Delaunay Triangulation) clustering algorithm, the NS-DBSCAN algorithm efficiently solves the problem of clustering network constrained spatial points by visualizing the intrinsic clustering structure of spatial data by constructing density ordering charts. However, the main drawback of this algorithm is when the data are processed, objects that are not specifically categorized into types of clusters cannot be removed, which is undeniably a waste of time, particularly when the dataset is large. In an attempt to have this algorithm work with great efficiency, we thus recommend removing edges that are longer than the threshold and eliminating low-density points from the density ordering table when forming clusters and also take other effective techniques into consideration. In this paper, we develop a theorem to determine the maximum length of an edge in a road segment. Based on this theorem, an algorithm is proposed to greatly improve the performance of the density-based clustering algorithm in network space (NS-DBSCAN). Experiments using our proposed algorithm carried out in collaboration with Ho Chi Minh City, Vietnam yield the same results but shows an advantage of it over NS-DBSCAN in execution time.

A New Quadtree-Based Skyline Query Algorithm

2014 ◽  
Vol 981 ◽  
pp. 175-178
Author(s):  
Run Tao Liu ◽  
Yuan Jing Chen ◽  
Da Yong Cao ◽  
De Yu Liu
Keyword(s):  
Detailed Analysis ◽  
Spatial Data ◽  
Index Structure ◽  
Skyline Query ◽  
Set Up ◽  
Query Algorithm

In this paper, the index structure, PR-quadtree for spatial data, is used to store data for a database. The properties of the quadtree are studied. With the properties prunning rules are set up for searching the Skyline set of the data stored in the quadtree. Through detailed analysis for the tree the method of finding some approximate skyline points is designed, by which a new skyline searching algorithm is given. The new algorithm is more effective.

scholarly journals DEVELOPMENT OF A HETEROGENIC DISTRIBUTED ENVIRONMENT FOR SPATIAL DATA PROCESSING USING CLOUD TECHNOLOGIES

2016 ◽  
Vol XLI-B4 ◽  
pp. 385-390
Author(s):  
A. S. Garov ◽  
I. P. Karachevtseva ◽  
E. V. Matveev ◽  
A. E. Zubarev ◽  
I. V. Florinsky
Keyword(s):  
Data Processing ◽  
Spatial Data ◽  
3D Visualization ◽  
New Technology ◽  
Main Idea ◽  
Remote Sensing Data ◽  
Data Access ◽  
Working Environment ◽  
Software Environment ◽  
Spatial Data Processing

We are developing a unified distributed communication environment for processing of spatial data which integrates web-, desktop- and mobile platforms and combines volunteer computing model and public cloud possibilities. The main idea is to create a flexible working environment for research groups, which may be scaled according to required data volume and computing power, while keeping infrastructure costs at minimum. It is based upon the "single window" principle, which combines data access via geoportal functionality, processing possibilities and communication between researchers. Using an innovative software environment the recently developed planetary information system (<a href="http://cartsrv.mexlab.ru/geoportal"target="_blank">http://cartsrv.mexlab.ru/geoportal</a>) will be updated. The new system will provide spatial data processing, analysis and 3D-visualization and will be tested based on freely available Earth remote sensing data as well as Solar system planetary images from various missions. Based on this approach it will be possible to organize the research and representation of results on a new technology level, which provides more possibilities for immediate and direct reuse of research materials, including data, algorithms, methodology, and components. The new software environment is targeted at remote scientific teams, and will provide access to existing spatial distributed information for which we suggest implementation of a user interface as an advanced front-end, e.g., for virtual globe system.

scholarly journals INTEGRATION BETWEEN SURFACE AND SUBSURFACE SPATIAL OBJECTS FOR DEVELOPING OMAN 3D SDI BASED ON THE CITYGML STANDARD

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 79-84
Author(s):  
K. Al Kalbani ◽  
A. Abdul Rahman
Keyword(s):  
Data Structure ◽  
Spatial Data ◽  
3D Model ◽  
Infrastructure Management ◽  
Data Infrastructure ◽  
3D City Models ◽  
Spatial Objects ◽  
The World ◽  
Geospatial Tools ◽  
City Models

Abstract. The paper investigates the capability to integrate the surface and subsurface 3D spatial objects data structure within the 3D spatial data infrastructure (3D SDI) based on the CityGML standards. In fact, a number of countries around the world have started applying the 3D city models for their planning and infrastructure management. While others are still working toward 3D SDI by using CityGML standards. Moreover, most of these initiatives focus on the surface spatial objects with less interest to model subsurface spatial objects. However, dealing with 3D SDI requires both surface and subsurface spatial objects with clear consideration on the issues and challenges in terms of the data structure. On the other hand, the study has used geospatial tools and databases such as FME, PostgreSQL-PostGIS, and 3D City Database to generate the 3D model and to test the capability for integrating the surface and subsurface 3D spatial objects data structure within the 3D SDI. This paper concludes by describing a framework that aims to integrate surface and subsurface 3D geospatial objects data structure in Oman SDI. The authors believe that there are possible solutions based on CityGML standards for surface and subsurface 3D spatial objects. Moreover, solving the issues in data structure can establish a better vision and open new avenues for the 3D SDI.

Object-Relational Spatial Indexing

2011 ◽  
pp. 49-80
Author(s):  
Hans-Peter Kriegel ◽  
Martin Pfeifle ◽  
Marco Potke ◽  
Thomas Seidl ◽  
Jost Enderle
Keyword(s):  
Spatial Data ◽  
Concurrency Control ◽  
Buffer Management ◽  
Database Systems ◽  
Data Types ◽  
Spatial Access ◽  
Access Methods ◽  
Object Relational ◽  
Relational Database Systems ◽  
Spatial Access Methods

In order to generate efficient execution plans for queries comprising spatial data types and predicates, the database system has to be equipped with appropriate index structures, query processing methods and optimization rules. Although available extensible indexing frameworks provide a gateway for seamless integration of spatial access methods into the standard process of query optimization and execution, they do not facilitate the actual implementation of the spatial access method. An internal enhancement of the database kernel is usually not an option for database developers. The embedding of a custom, block-oriented index structure into concurrency control, recovery services and buffer management would cause extensive implementation efforts and maintenance cost, at the risk of weakening the reliability of the entire system. The server stability can be preserved by delegating index operations to an external process, but this approach induces severe performance bottlenecks due to context switches and inter-process communication. Therefore, we present the paradigm of object-relational spatial access methods that perfectly fits to the common relational data model, and is highly compatible with the extensible indexing frameworks of existing object-relational database systems, allowing the user to define application-specific access methods.

Range Query Algorithm

2008 ◽  
pp. 949-949
Author(s):  
Shashi Shekhar ◽  
Hui Xiong
Keyword(s):  
Range Query ◽  
Query Algorithm

Analysis and Modelling of Spatial Objects to Implement OOP for Spatial Data Generalisation in GIS

OOIS’ 95 ◽  
1996 ◽  
pp. 189-199
Author(s):  
Changcheng Dong ◽  
Paul Luker ◽  
Philippa Berry ◽  
Hongji Yang
Keyword(s):  
Spatial Data ◽  
Spatial Objects
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