Introducing Elasticity for Spatial Knowledge Management

2010 ◽  
pp. 282-299
Author(s):  
David A. Gadish
Keyword(s):  
Knowledge Management ◽  
Spatial Data ◽  
Internal Validity ◽  
Spatial Knowledge ◽  
Data Sets ◽  
Multiple Objects ◽  
Spatial Objects ◽  
Spatial Consistency ◽  
Spatial Data Sets

The internal validity of a spatial database can be discovered using the data contained within one or more databases. Spatial consistency includes topological consistency, or the conformance to topological rules. Discovery of inconsistencies in spatial data is an important step for improvement of spatial data quality as part of the knowledge management initiative. An approach for detecting topo-semantic inconsistencies in spatial data is presented. Inconsistencies between pairs of neighboring spatial objects are discovered by comparing relations between spatial objects to rules. A property of spatial objects, called elasticity, has been defined to measure the contribution of each of the objects to inconsistent behavior. Grouping of multiple objects, which are inconsistent with one another, based on their elasticity is proposed. The ability to discover groups of neighboring objects that are inconsistent with one another can serve as the basis of an effort to understand and increase the quality of spatial data sets. Elasticity should therefore be incorporated into knowledge management systems that handle spatial data.

Introducing Elasticity for Spatial Knowledge Management

2009 ◽  
pp. 2685-2705
Author(s):  
David A. Gadish
Keyword(s):  
Knowledge Management ◽  
Spatial Data ◽  
Internal Validity ◽  
Spatial Knowledge ◽  
Data Sets ◽  
Multiple Objects ◽  
Spatial Objects ◽  
Spatial Consistency ◽  
Spatial Data Sets

The internal validity of a spatial database can be discovered using the data contained within one or more databases. Spatial consistency includes topological consistency, or the conformance to topological rules. Discovery of inconsistencies in spatial data is an important step for improvement of spatial data quality as part of the knowledge management initiative. An approach for detecting topo-semantic inconsistencies in spatial data is presented. Inconsistencies between pairs of neighboring spatial objects are discovered by comparing relations between spatial objects to rules. A property of spatial objects, called elasticity, has been defined to measure the contribution of each of the objects to inconsistent behavior. Grouping of multiple objects, which are inconsistent with one another, based on their elasticity is proposed. The ability to discover groups of neighboring objects that are inconsistent with one another can serve as the basis of an effort to understand and increase the quality of spatial data sets. Elasticity should therefore be incorporated into knowledge management systems that handle spatial data.

Introducing the Elasticity of Spatial Data

2010 ◽  
pp. 831-848
Author(s):  
David A. Gadish
Keyword(s):  
Data Quality ◽  
Spatial Data ◽  
Data Sets ◽  
Data Warehouses ◽  
Multiple Objects ◽  
Quality Of Results ◽  
Spatial Objects ◽  
Spatial Consistency ◽  
Spatial Data Sets

The data quality of a vector spatial data can be assessed using the data contained within one or more data warehouses. Spatial consistency includes topological consistency, or the conformance to topological rules (Hadzilacos & Tryfona, 1992, Rodríguez, 2005). Detection of inconsistencies in vector spatial data is an important step for improvement of spatial data quality (Redman, 1992; Veregin, 1991). An approach for detecting topo-semantic inconsistencies in vector spatial data is presented. Inconsistencies between pairs of neighboring vector spatial objects are detected by comparing relations between spatial objects to rules (Klein, 2007). A property of spatial objects, called elasticity, has been defined to measure the contribution of each of the objects to inconsistent behavior. Grouping of multiple objects, which are inconsistent with one another, based on their elasticity is proposed. The ability to detect groups of neighboring objects that are inconsistent with one another can later serve as the basis of an effort to increase the quality of spatial data sets stored in data warehouses, as well as increase the quality of results of data-mining processes

Introducing the Elasticity of Spatial Data

2011 ◽  
pp. 198-215
Author(s):  
A. Gadish David
Keyword(s):  
Data Quality ◽  
Spatial Data ◽  
Data Sets ◽  
Data Warehouses ◽  
Multiple Objects ◽  
Quality Of Results ◽  
Spatial Objects ◽  
Spatial Consistency ◽  
Spatial Data Sets

The data quality of a vector spatial data can be assessed using the data contained within one or more data warehouses. Spatial consistency includes topological consistency, or the conformance to topological rules (Hadzilacos & Tryfona, 1992, Rodríguez, 2005). Detection of inconsistencies in vector spatial data is an important step for improvement of spatial data quality (Redman, 1992; Veregin, 1991). An approach for detecting topo-semantic inconsistencies in vector spatial data is presented. Inconsistencies between pairs of neighboring vector spatial objects are detected by comparing relations between spatial objects to rules (Klein, 2007). A property of spatial objects, called elasticity, has been defined to measure the contribution of each of the objects to inconsistent behavior. Grouping of multiple objects, which are inconsistent with one another, based on their elasticity is proposed. The ability to detect groups of neighboring objects that are inconsistent with one another can later serve as the basis of an effort to increase the quality of spatial data sets stored in data warehouses, as well as increase the quality of results of data-mining processes.

On the MDBSCAN Algorithm in a Spatial Data Mining Context

2013 ◽  
pp. 263-273
Author(s):  
Gabriella Schoier
Keyword(s):  
Spatial Data ◽  
Spatial Clustering ◽  
Clustering Algorithms ◽  
Synthetic Data ◽  
Spatial Data Mining ◽  
Data Sets ◽  
Data Set ◽  
Spatial Variables ◽  
Spatial Objects ◽  
Spatial Data Sets

The rapid developments in the availability and access to spatially referenced information in a variety of areas, has induced the need for better analysis techniques to understand the various phenomena. In particular, spatial clustering algorithms, which group similar spatial objects into classes, can be used for the identification of areas sharing common characteristics. The aim of this chapter is to present a density based algorithm for the discovery of clusters of units in large spatial data sets (MDBSCAN). This algorithm is a modification of the DBSCAN algorithm (see Ester (1996)). The modifications regard the consideration of spatial and non spatial variables and the use of a Lagrange-Chebychev metrics instead of the usual Euclidean one. The applications concern a synthetic data set and a data set of satellite images

On the MDBSCAN Algorithm in a Spatial Data Mining Context

Data Mining ◽  
2013 ◽  
pp. 435-444
Author(s):  
Gabriella Schoier
Keyword(s):  
Spatial Data ◽  
Spatial Clustering ◽  
Clustering Algorithms ◽  
Synthetic Data ◽  
Spatial Data Mining ◽  
Data Sets ◽  
Data Set ◽  
Spatial Variables ◽  
Spatial Objects ◽  
Spatial Data Sets

The rapid developments in the availability and access to spatially referenced information in a variety of areas, has induced the need for better analysis techniques to understand the various phenomena. In particular, spatial clustering algorithms, which group similar spatial objects into classes, can be used for the identification of areas sharing common characteristics. The aim of this chapter is to present a density based algorithm for the discovery of clusters of units in large spatial data sets (MDBSCAN). This algorithm is a modification of the DBSCAN algorithm (see Ester (1996)). The modifications regard the consideration of spatial and non spatial variables and the use of a Lagrange-Chebychev metrics instead of the usual Euclidean one. The applications concern a synthetic data set and a data set of satellite images

scholarly journals An ABC-optimized fuzzy ELECTRE approach for assessing petroleum potential at the petroleum system level

2020 ◽  
Vol 12 (1) ◽  
pp. 580-597
Author(s):  
Mohamad Hamzeh ◽  
Farid Karimipour
Keyword(s):  
Spatial Data ◽  
Spatial Models ◽  
Model Performance ◽  
Essential Elements ◽  
Petroleum System ◽  
Petroleum Exploration ◽  
System Level ◽  
Data Sets ◽  
Petroleum Potential ◽  
Spatial Data Sets

AbstractAn inevitable aspect of modern petroleum exploration is the simultaneous consideration of large, complex, and disparate spatial data sets. In this context, the present article proposes the optimized fuzzy ELECTRE (OFE) approach based on combining the artificial bee colony (ABC) optimization algorithm, fuzzy logic, and an outranking method to assess petroleum potential at the petroleum system level in a spatial framework using experts’ knowledge and the information available in the discovered petroleum accumulations simultaneously. It uses the characteristics of the essential elements of a petroleum system as key criteria. To demonstrate the approach, a case study was conducted on the Red River petroleum system of the Williston Basin. Having completed the assorted preprocessing steps, eight spatial data sets associated with the criteria were integrated using the OFE to produce a map that makes it possible to delineate the areas with the highest petroleum potential and the lowest risk for further exploratory investigations. The success and prediction rate curves were used to measure the performance of the model. Both success and prediction accuracies lie in the range of 80–90%, indicating an excellent model performance. Considering the five-class petroleum potential, the proposed approach outperforms the spatial models used in the previous studies. In addition, comparing the results of the FE and OFE indicated that the optimization of the weights by the ABC algorithm has improved accuracy by approximately 15%, namely, a relatively higher success rate and lower risk in petroleum exploration.

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