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.

scholarly journals Fuzzy Spatial Data Types for Spatial Uncertainty Management in Databases

2011 ◽  
pp. 490-515 ◽  
Author(s):  
Markus Schneider
Keyword(s):  
Spatial Data ◽  
Database Systems ◽  
Spatial Uncertainty ◽  
Dimensional Euclidean Space ◽  
Future Research ◽  
Data Types ◽  
Spatial Objects ◽  
Fuzzy Regions ◽  
Definition Of ◽  
Spatial Vagueness

Spatial database systems and geographical information systems are currently only able to support geographical applications that deal with 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. This requires novel concepts due to the lack of adequate approaches and systems. In this chapter, we focus on an important kind of spatial vagueness called spatial fuzziness. Spatial fuzziness captures the property of many spatial objects in reality that do not have sharp boundaries and interiors or whose boundaries and interiors cannot be precisely determined. We will designate this kind of entities as fuzzy spatial objects. Examples are polluted areas, temperature zones, and lakes. We propose an abstract, formal, and conceptual model of so-called fuzzy spatial data types (that is, a fuzzy spatial algebra) introducing fuzzy points, fuzzy lines, and fuzzy regions in the two-dimensional Euclidean space. This chapter provides a definition of their structure and semantics, which is supposed to serve as a specification of their implementation. Furthermore, we introduce fuzzy spatial set operations like fuzzy union, fuzzy intersection, and fuzzy difference, as well as fuzzy topological predicates as they are useful in fuzzy spatial joins and fuzzy spatial selections. We also sketch implementation strategies for the whole type system and show their integration into databases. An outlook on future research challenges rounds out the chapter.

Generics for the masses

2006 ◽  
Vol 16 (4-5) ◽  
pp. 451-483 ◽  
Author(s):  
RALF HINZE
Keyword(s):  
Theoretical Perspective ◽  
Type System ◽  
Type Systems ◽  
Data Type ◽  
Data Types ◽  
Type Classes ◽  
Definition Of ◽  
The Masses ◽  
Language Construct

A generic function is a function that can be instantiated on many data types to obtain data type specific functionality. Examples of generic functions are the functions that can be derived in Haskell, such as show, read, and ‘==’. The recent years have seen a number of proposals that support the definition of generic functions. Some of the proposals define new languages, some define extensions to existing languages. As a common characteristic none of the proposals can be made to work within Haskell 98: they all require something extra, either a more sophisticated type system or an additional language construct. The purpose of this paper is to show that one can, in fact, program generically within Haskell 98 obviating to some extent the need for fancy type systems or separate tools. Haskell's type classes are at the heart of this approach: they ensure that generic functions can be defined succinctly and, in particular, that they can be used painlessly. We detail three different implementations of generics both from a practical and from a theoretical perspective.

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.

An UML Profile and SOLAP Datacubes Multidimensional Schemas Transformation Process for Datacubes Risk-Aware Design

2015 ◽  
Vol 11 (4) ◽  
pp. 64-83 ◽  
Author(s):  
Elodie Edoh-Alove ◽  
Sandro Bimonte ◽  
François Pinet
Keyword(s):  
Spatial Data ◽  
New Technologies ◽  
Design Method ◽  
Transformation Process ◽  
Spatial Data Analysis ◽  
Uml Profile ◽  
Previous Proposal ◽  
On Line ◽  
Definition Of ◽  
Spatial Vagueness

Spatial Data Warehouses (SDWs) and Spatial On-Line Analytical Processing (SOLAP) systems are new technologies for the integration and the analysis of huge volume of data with spatial reference. Spatial vagueness is often neglected in these types of systems and the data and analysis results are considered reliable. In a previous work, the authors provided a new design method for SOLAP datacubes that allows the handling of vague spatial data analysis issues. The method consists of tailoring SOLAP datacubes schemas to end-users tolerance levels to identified potential risks of misinterpretation they encounter when exploiting datacubes containing vague spatial data. It this paper, the authors further their previous proposal by presenting different formal tools to support their method: it is an UML profile providing stereotypes needed to add vague, risks and tolerance levels information on datacubes schemas plus the formal definition of SOLAP datacubes schemas transformation process and functions.

DATA ANALYTICS IN SPATIAL EPIDEMIOLOGY: A SURVEY

2016 ◽  
Vol 78 (10) ◽  
Author(s):  
Sharmila Banu Kather ◽  
BK Tripathy
Keyword(s):  
Soft Computing ◽  
Spatial Data ◽  
Data Science ◽  
Rough Set Theory ◽  
Spatial Epidemiology ◽  
Spatial Data Mining ◽  
Computing Methods ◽  
Spatial Data Analysis ◽  
Survey Paper ◽  
Soft Computing Techniques

Spatial data analysis is being used efficiently and the governments have realized that georeferenced data yields more insight with time and locations. Epidemiology is about the study of origin and distribution of diseases and dates back to the 1600s with the instance of cholera in London. Data Science has been evolving and when analyzed with Soft Computing techniques like Rough Set Theory (RST), Fuzzy Sets, Granulation Computing which encompasses the data in its original nature, results can be obtained with accrued accuracy. This survey paper highlights Spatial Data Mining methods used in the field of Epidemiology, identifies crucial challenges and discusses of the use of Soft Computing methods. 

scholarly journals An algorithm for matching spatial objects of different-scale maps based on topological data analysis

2019 ◽  
Vol 43 (6) ◽  
pp. 1021-1029 ◽  
Author(s):  
S.V. Eremeev ◽  
D.E. Andrianov ◽  
V.S. Titov
Keyword(s):  
Data Analysis ◽  
Spatial Data ◽  
General Structure ◽  
Persistent Homology ◽  
Topological Data Analysis ◽  
Spatial Objects ◽  
Topological Features ◽  
Definition Of ◽  
Topological Data

A problem of automatic comparison of spatial objects on maps with different scales for the same locality is considered in the article. It is proposed that this problem should be solved using methods of topological data analysis. The initial data of the algorithm are spatial objects that can be obtained from maps with different scales and subjected to deformations and distortions. Persistent homology allows us to identify the general structure of such objects in the form of topological features. The main topological features in the study are the connectivity components and holes in objects. The paper gives a mathematical description of the persistent homology method for representing spatial objects. A definition of a barcode for spatial data, which contains a description of the object in the form of topological features is given. An algorithm for comparing feature barcodes was developed. It allows us to find the general structure of objects. The algorithm is based on the analysis of data from the barcode. An index of objects similarity in terms of topological features is introduced. Results of the research of the algorithm for comparing maps of natural and municipal objects with different scales, generalization and deformation are shown. The experiments confirm the high quality of the proposed algorithm. The percentage of similarity in the comparison of natural objects, while taking into account the scale and deformation, is in the range from 85 to 92, and for municipal objects, after stretching and distortion of their parts, was from 74 to 87. Advantages of the proposed approach over analogues for the comparison of objects with significant deformation at different scales and after distortion are demonstrated.

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