Spatial Vagueness, Uncertainty, Granularity

2017 ◽  
Keyword(s):  
Spatial Vagueness

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.

Spatial Vagueness and Second-Order

2003 ◽  
Vol 3 (2) ◽  
pp. 157-183 ◽  
Author(s):  
LarsVagueness Kulik
Keyword(s):  
Second Order ◽  
Spatial Vagueness

New Design Approach to Handle Spatial Vagueness in Spatial OLAP Datacubes

2016 ◽  
pp. 1859-1880
Author(s):  
Elodie Edoh-Alove ◽  
Sandro Bimonte ◽  
François Pinet ◽  
Yvan Bédard
Keyword(s):  
Spatial Data ◽  
End Users ◽  
Environmental Data ◽  
Decision Makers ◽  
Multidimensional Analysis ◽  
Multidimensional Data ◽  
New Approach ◽  
New Models ◽  
Different Types ◽  
Spatial Vagueness

Spatial-OLAP (SOLAP) technologies are dedicated to multidimensional analysis of large volumes of (spatial) data. Spatial data are subject to different types of uncertainty, in particular spatial vagueness. Although several researches propose new models to cope with spatial vagueness, their integration in SOLAP systems is still in an embryonic state. Also, analyzing multidimensional data with metadata brought by the exploitation of the new models can be too complex and demanding for decision-makers. To help reduce spatial vagueness consequences on the exactness of SOLAP analysis queries, the authors present a new approach for designing SOLAP datacubes based on end-users' tolerance to the risks of misinterpretation of fact data. An experimentation of the new approach on agri-environmental data is also proposed.

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.

New Design Approach to Handle Spatial Vagueness in Spatial OLAP Datacubes

2015 ◽  
Vol 6 (3) ◽  
pp. 29-49 ◽  
Author(s):  
Elodie Edoh-Alove ◽  
Sandro Bimonte ◽  
François Pinet ◽  
Yvan Bédard
Keyword(s):  
Spatial Data ◽  
End Users ◽  
Environmental Data ◽  
Decision Makers ◽  
Multidimensional Analysis ◽  
Multidimensional Data ◽  
New Approach ◽  
New Models ◽  
Different Types ◽  
Spatial Vagueness

Spatial-OLAP (SOLAP) technologies are dedicated to multidimensional analysis of large volumes of (spatial) data. Spatial data are subject to different types of uncertainty, in particular spatial vagueness. Although several researches propose new models to cope with spatial vagueness, their integration in SOLAP systems is still in an embryonic state. Also, analyzing multidimensional data with metadata brought by the exploitation of the new models can be too complex and demanding for decision-makers. To help reduce spatial vagueness consequences on the exactness of SOLAP analysis queries, the authors present a new approach for designing SOLAP datacubes based on end-users' tolerance to the risks of misinterpretation of fact data. An experimentation of the new approach on agri-environmental data is also proposed.

New Design Approach to Handle Spatial Vagueness in Spatial OLAP Datacubes

2018 ◽  
pp. 129-155
Author(s):  
Elodie Edoh-Alove ◽  
Sandro Bimonte ◽  
François Pinet ◽  
Yvan Bédard
Keyword(s):  
Spatial Data ◽  
End Users ◽  
Environmental Data ◽  
Decision Makers ◽  
Multidimensional Analysis ◽  
Multidimensional Data ◽  
New Approach ◽  
New Models ◽  
Different Types ◽  
Spatial Vagueness

Spatial OLAP (SOLAP) technologies are dedicated to multidimensional analysis of large volumes of (spatial) data. Spatal data are subject to different types of uncertainty, in particular spatial vagueness. Although several researches propose new models to cope with spatial vagueness, their integration in SOLAP systems is still in an embryonic state. Also, analyzing multidimensional data with metadata brought by the exploitation of the new models can be too complex and demanding for decision makers. To help reduce spatial vagueness consequences on the exactness of SOLAP analysis queries, the authors present a new approach for designing SOLAP datacubes based on end-users' tolerance to the risks of misinterpretation of fact data. An experimentation of the new approach on agri-environmental data is also proposed.

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