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.

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.

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.

Null Queries with Compound Fuzzy Attributes

1999 ◽  
Vol 3 (6) ◽  
pp. 509-514
Author(s):  
Shyue-Liang Wang ◽  
◽  
Yu-Jane Tsai ◽  
Keyword(s):  
Database Management ◽  
Database Management Systems ◽  
Management Systems ◽  
Database Schema ◽  
Data Types ◽  
User Friendly

We present a generalized approach for handling null queries that contain compound fuzzy attributes. Null queries elicit a null answer from the database. Compound fuzzy attributes are ambiguous attributes not defined in the original database schema but derived from multiple rigid attributes in a schema. Compound fuzzy attributes derived from simple numbers were studied by Nomura11). We extend compound fuzzy attributes so they can be derived from numbers, interval values, scalars, and sets of all these data types. Database management systems that handle this type of ambiguous attributes in null queries both reduce occurrences of null answers and provide an improved user-friendly query environment.

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.

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).

scholarly journals Using a DBMS based on various data models in solving problems of cartography and geoinformatics

2021 ◽  
Vol 310 ◽  
pp. 06001
Author(s):  
Alexey A. Kolesnikov ◽  
Pavel M. Kikin
Keyword(s):  
Comparative Analysis ◽  
Spatial Data ◽  
Database Management ◽  
Database Management Systems ◽  
Data Models ◽  
Management Systems ◽  
Spatial Queries ◽  
Vector Data ◽  
Novosibirsk Region

An increasing number of database management systems are expanding their functionality to work with various types of spatial data. This is true for both relational and NoSQL data models. The article describes the main features of those data models for which the functions of storing and processing spatial data are implemented. A comparative analysis of the performance of typical spatial queries for database management systems based on various data models, including multi-model ones, is carried out. The dataset on which the comparison is performed is presented in the form of three blocks of OpenStreetMap vector data for the territory of the Novosibirsk region. Based on the results of the study, recommendations are made on the use of certain data models, depending on the available data and the tasks to be solved.

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.

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