Spatial Databases

2017 ◽  
pp. 111-149
Author(s):  
Grace L. Samson ◽  
Joan Lu ◽  
Mistura M. Usman ◽  
Qiang Xu
Keyword(s):  
Data Storage ◽  
Spatial Data ◽  
Spatial Databases ◽  
Query Language ◽  
Dimensional Space ◽  
Spatial Database ◽  
Multidimensional Data ◽  
Data Types ◽  
3 Dimensional ◽  
Storage And Retrieval

Spatial databases maintain space information which is appropriate for applications where there is need to monitor the position of an object or event over space. Spatial databases describe the fundamental representation of the object of a dataset that comes from spatial or geographic entities. A spatial database supports aspects of space and offers spatial data types in its data model and query language. The spatial or geographic referencing attributes of the objects in a spatial database permits them to be positioned within a two (2) dimensional or three (3) dimensional space. This chapter looks into the fundamentals of spatial databases and describes their basic component, operations and architecture. The study focuses on the data models, query Language, query processing, indexes and query optimization of a spatial databases that approves spatial databases as a necessary tool for data storage and retrieval for multidimensional data of high dimensional spaces.

Methodical Spatial Database Design with Topological Polygon Structures

2012 ◽  
Vol 3 (1) ◽  
pp. 21-30
Author(s):  
Jean Damascène Mazimpaka
Keyword(s):  
Design Process ◽  
Spatial Data ◽  
Spatial Databases ◽  
Early Stage ◽  
Spatial Database ◽  
Database Design ◽  
Integrity Constraints ◽  
Data Types ◽  
Data Infrastructure ◽  
Database Development

Spatial databases form the foundation for a Spatial Data Infrastructure (SDI). For this, a spatial database should be methodically developed to accommodate its role in SDI. It is desirable to have an approach to spatial database development that considers maintenance from the early stage of database design and in a flexible way. Moreover, there is a lack of a mechanism to capture topological relations of spatial objects during the design process. This paper presents an approach that integrates maintenance of topological integrity constraints into the whole spatial database development cycle. The approach is based on the concept of Abstract Data Types. A number of topological classes have been identified and modelling primitives developed for them. Topological integrity constraints are embedded into maintenance functions associated with the topological classes. A semi-automatic transformation process has been developed following the principles of Model Driven Architecture to simplify the design process.

Query Processing in Spatial Databases

2009 ◽  
pp. 269-278
Author(s):  
Antonio Corral ◽  
Michael Vassilakopoulos
Keyword(s):  
Information Systems ◽  
Query Processing ◽  
Spatial Data ◽  
Spatial Databases ◽  
Spatial Database ◽  
Database System ◽  
Data Types ◽  
Spatial Data Models ◽  
Large Scale Integration ◽  
Efficient Query Processing

Spatial data management has been an active area of intensive research for more than two decades. In order to support spatial objects in a database system several important issues must be taken into account such as: spatial data models, indexing mechanisms and efficient query processing. A spatial database system (SDBS) is a database system that offers spatial data types in its data model and query language and supports spatial data types in its implementation, providing at least spatial indexing and efficient spatial query processing (Güting, 1994). The main reason that has caused the active study of spatial database management systems (SDBMS) comes from the needs of the existing applications such as geographical information systems (GIS), computer-aided design (CAD), very large scale integration design (VLSI), multimedia information systems (MIS), data warehousing, multi-criteria decision making, location-based services, etc.

Query Processing in Spatial Databases

2005 ◽  
pp. 511-516
Author(s):  
Antonio Corral ◽  
Michael Vassilakopoulos
Keyword(s):  
Query Processing ◽  
Spatial Data ◽  
Spatial Databases ◽  
Query Language ◽  
Database System ◽  
Data Types ◽  
Spatial Query Processing ◽  
Spatial Data Management ◽  
Spatial Data Models ◽  
Efficient Query Processing

Spatial data management has been an active area of intensive research for more than two decades. In order to support spatial objects in a database system, several important issues must be taken into account such as spatial data models, indexing mechanisms, and efficient query processing. A spatial database system (SDBS) is a database system that offers spatial data types in its data model and query language and supports spatial data types in its implementation, providing at least spatial indexing and efficient spatial query processing (Güting, 1994).

Spatial Network Databases

2009 ◽  
pp. 307-315 ◽  
Author(s):  
Michael Vassilakopoulos
Keyword(s):  
Information Systems ◽  
Spatial Data ◽  
Computer Aided Design ◽  
Geographical Information Systems ◽  
Large Scale ◽  
Spatial Databases ◽  
Query Language ◽  
Geographical Information ◽  
Data Types ◽  
Large Scale Integration

A Spatial Database is a database that offers spatial data types, a query language with spatial predicates, spatial indexing techniques, and efficient processing of spatial queries. All these fields have attracted the focus of researchers over the past 25 years. The main reason for studying spatial databases has been applications that emerged during this period, such as Geographical Information Systems, Computer-Aided Design, Very Large Scale Integration design, Multimedia Information Systems, and so forth. In parallel, the field of temporal databases, databases that deal with the management of timevarying data, attracted the research community since numerous database applications (i.e., Banking, Personnel Management, Transportation Scheduling) involve the notion of time.

scholarly journals A SPARK BASED COMPUTING FRAMEWORK FOR SPATIAL DATA

2017 ◽  
Vol IV-4/W2 ◽  
pp. 125-130
Author(s):  
F. Xiao
Keyword(s):  
Data Storage ◽  
Spatial Data ◽  
Query Language ◽  
Spatial Query ◽  
Data Types ◽  
Spatial Join ◽  
Large Size ◽  
Spatial Query Language ◽  
Spatial Indexes ◽  
User Friendly

In this paper, a novel Apache Spark-based framework for spatial data processing is proposed, which includes 4 layers: spatial data storage, spatial RDDs, spatial operations, and spatial query language. The spatial data storage layer uses HDFS to store large size of spatial vector/raster data in the distributed cluster. The spatial RDDs are the abstract logical dataset of spatial data types, and can be transferred to the spark cluster to conduct spark transformations and actions. The spatial operations layer is a series of processing on spatial RDDs, such as range query, k nearest neighbour and spatial join. The spatial query language is a user-friendly interface which provide people not familiar with Spark with a comfortable way to operation the spatial operation. Compared with other spatial frameworks based on Spark, it is highlighted that spatial indexes like grid, R-tree are used for data storage and query. Extensive experiments on real system prototype and real datasets show that better performance can be achieved.

scholarly journals A Big Spatial Data Processing Framework Applying to National Geographic Conditions Monitoring

2018 ◽  
Vol XLII-3 ◽  
pp. 1945-1950 ◽  
Author(s):  
F. Xiao
Keyword(s):  
Data Processing ◽  
Data Storage ◽  
Spatial Data ◽  
Query Language ◽  
Spatial Query ◽  
K Nearest Neighbor ◽  
Data Types ◽  
National Geographic ◽  
Spatial Query Language ◽  
Spatial Data Processing

In this paper, a novel framework for spatial data processing is proposed, which apply to National Geographic Conditions Monitoring project of China. It includes 4 layers: spatial data storage, spatial RDDs, spatial operations, and spatial query language. The spatial data storage layer uses HDFS to store large size of spatial vector/raster data in the distributed cluster. The spatial RDDs are the abstract logical dataset of spatial data types, and can be transferred to the spark cluster to conduct spark transformations and actions. The spatial operations layer is a series of processing on spatial RDDs, such as range query, k nearest neighbor and spatial join. The spatial query language is a user-friendly interface which provide people not familiar with Spark with a comfortable way to operation the spatial operation. Compared with other spatial frameworks, it is highlighted that comprehensive technologies are referred for big spatial data processing. Extensive experiments on real datasets show that the framework achieves better performance than traditional process methods.

Spatial and Spatiotemporal Data Types as a Foundation for Representing Space-Time Data in GIS

2017 ◽  
pp. 1-28 ◽  
Author(s):  
Markus Schneider
Keyword(s):  
Spatial Data ◽  
Geographical Information Systems ◽  
Moving Objects ◽  
Spatial Databases ◽  
Query Languages ◽  
Geographical Information ◽  
Spatiotemporal Data ◽  
Data Types ◽  
Time Data ◽  
Use Of Data

A data type comprises a set of homogeneous values together with a collection of operations defined on them. This chapter emphasizes the importance of crisp spatial data types, fuzzy spatial data types, and spatiotemporal data types for representing static, vague, and time-varying geometries in Geographical Information Systems (GIS). These data types provide a fundamental abstraction for modeling the geometric structure of crisp spatial, fuzzy spatial, and moving objects in space and time as well as their relationships, properties, and operations. The goal of this chapter is to provide an overview and description of these data types and their operations that have been proposed in research and can be found in GIS, spatial databases, moving objects databases, and other spatial software tools. The use of data types, operations, and predicates will be illustrated by their embedding into query languages.

A Taxonomy for Distance-Based Spatial Join Queries

2017 ◽  
Vol 13 (3) ◽  
pp. 1-24 ◽  
Author(s):  
Lingxiao Li ◽  
David Taniar
Keyword(s):  
Fixed Points ◽  
Spatial Data ◽  
Database Management ◽  
Spatial Databases ◽  
Family Type ◽  
Management Systems ◽  
Data Types ◽  
Full Spectrum ◽  
Spatial Join ◽  
Join Queries

Join operation is one of the most used operations in database management systems, including spatial databases. Hence, spatial join queries are very important in spatial database processing. There are many different kinds of spatial join queries, due to the richness in spatial data types and spatial operations. Therefore, it is important to understand the full spectrum of spatial join queries. The aim of this paper is to give a classification to one family type of spatial join, called the Distance-based Spatial Join. In the taxonomy, the authors divide this spatial join into three categories: (i) AllRange, (ii) All-kNN, and (iii) All-RNN. Each of these categories has its own variants. In this taxonomy, the authors confine the discussions to join queries on fixed points.

Evaluation of a Model-driven Knowledge Storage and Retrieval IDE for Interactive HRI Systems

2019 ◽  
Vol 13 (02) ◽  
pp. 207-227 ◽  
Author(s):  
Norman Köster ◽  
Sebastian Wrede ◽  
Philipp Cimiano
Keyword(s):  
Data Storage ◽  
User Study ◽  
Query Language ◽  
Storage System ◽  
Human Robot Interaction ◽  
Storage And Retrieval ◽  
Model Driven ◽  
Long Term Storage ◽  
Model Driven Software Development

Efficient storage and querying of long-term human–robot interaction data requires application developers to have an in-depth understanding of the involved domains. Creating syntactically and semantically correct queries in the development process is an error prone task which can immensely impact the interaction experience of humans with robots and artificial agents. To address this issue, we present and evaluate a model-driven software development approach to create a long-term storage system to be used in highly interactive HRI scenarios. We created multiple domain-specific languages that allow us to model the domain and seamlessly embed its concepts into a query language. Along with corresponding model-to-model and model-to-text transformations, we generate a fully integrated workbench facilitating data storage and retrieval. It supports developers in the query design process and allows in-tool query execution without the need to have prior in-depth knowledge of the domain. We evaluated our work in an extensive user study and can show that the generated tool yields multiple advantages compared to the usual query design approach.

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