Spatial Plateau Algebra: An Executable Type System for Fuzzy Spatial Data Types

2018 ◽  
Author(s):  
Anderson Chaves Carniel ◽  
Markus Schneider
Keyword(s):  
Spatial Data ◽  
Type System ◽  
Data Types

scholarly journals Soft Computing Techniques in Spatial Databases

2010 ◽  
pp. 49-71
Author(s):  
Markus Schneider
Keyword(s):  
Soft Computing ◽  
Spatial Data ◽  
Type System ◽  
Type Systems ◽  
Database Systems ◽  
Data Types ◽  
Spatial Objects ◽  
Soft Computing Techniques ◽  
Definition Of ◽  
Spatial Vagueness

Spatial database systems and geographical information systems are currently only able to support geographical applications that deal with only crisp spatial objects, that is, objects whose extent, shape, and boundary are precisely determined. Examples are land parcels, school districts, and state territories. However, many new, emerging applications are interested in modeling and processing geographic data that are inherently characterized by spatial vagueness or spatial indeterminacy. Examples are air polluted areas, temperature zones, and lakes. These applications require novel concepts due to the lack of adequate approaches and systems. In this chapter, the authors show how soft computing techniques can provide a solution to this problem. They give an overview of two type systems or algebras that can be integrated into database systems and utilized for the modeling and handling of spatial vagueness. The first type system, called Vague Spatial Algebra (VASA), is based on well known, general, and exact models of crisp spatial data types and introduces vague points, vague lines, and vague regions. This enables an exact definition of the vague spatial data model since we can build it upon an already existing theory of spatial data types. The second type system, called Fuzzy Spatial Algebra (FUSA), leverages fuzzy set theory and fuzzy topology and introduces novel fuzzy spatial data types for fuzzy points, fuzzy lines, and fuzzy regions. This enables an even more fine-grained modeling of spatial objects that do not have sharp boundaries and interiors or whose boundaries and interiors cannot be precisely determined. This chapter provides a formal definition of the structure and semantics of both type systems. Further, the authors introduce spatial set operations for both algebras and obtain vague and fuzzy versions of geometric intersection, union, and difference. Finally, they describe how these data types can be embedded into extensible databases and show some example queries.

A Method of Structure Comparison Using Spatial Topological Patterns

2005 ◽  
Vol 277-279 ◽  
pp. 272-277
Author(s):  
Sung Hee Park ◽  
Keun Ho Ryu
Keyword(s):  
Spatial Data ◽  
Similarity Search ◽  
3D Structure ◽  
Structural Similarity ◽  
Query Protein ◽  
Data Types ◽  
Structure Comparison ◽  
3D Space ◽  
Structure Databases ◽  
Computationally Expensive

The problem of comparison of structural similarity has been complex and computationally expensive. The first step to solve comparison of structural similarity in 3D structure databases is to develop fast methods for structural similarity. Therefore, we propose a new method of comparing structural similarity in protein structure databases by using topological patterns of proteins. In our approach, the geometry of secondary structure elements in 3D space is represented by spatial data types and is indexed using Rtrees. Topological patterns are discovered by spatial topology relations based on the Rtree index join. An algorithm for a similarity search compares topological patterns of a query protein with those of proteins in structure databases by the intersection frequency of SSEs. Our experimental results show that the execution time of our method is three times faster than the generally known method DALITE. Our method can generate small candidate sets for more accurate alignment tools such as DALI and SSAP.

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.

The Integral of Spatial Data Mining in the Era of Big Data

2017 ◽  
pp. 90-126
Author(s):  
Gebeyehu Belay Gebremeskel ◽  
Chai Yi ◽  
Zhongshi He
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Spatial Data ◽  
High Volume ◽  
Spatial Data Mining ◽  
Research Field ◽  
Data Sets ◽  
Data Types ◽  
Basic Principles ◽  
Gis Data

Data Mining (DM) is a rapidly expanding field in many disciplines, and it is greatly inspiring to analyze massive data types, which includes geospatial, image and other forms of data sets. Such the fast growths of data characterized as high volume, velocity, variety, variability, value and others that collected and generated from various sources that are too complex and big to capturing, storing, and analyzing and challenging to traditional tools. The SDM is, therefore, the process of searching and discovering valuable information and knowledge in large volumes of spatial data, which draws basic principles from concepts in databases, machine learning, statistics, pattern recognition and 'soft' computing. Using DM techniques enables a more efficient use of the data warehouse. It is thus becoming an emerging research field in Geosciences because of the increasing amount of data, which lead to new promising applications. The integral SDM in which we focused in this chapter is the inference to geospatial and GIS data.

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.

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.

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