A Meteorological Conceptual Modeling Approach Based on Spatial Data Mining and Knowledge Discovery

2005 ◽  
pp. 490-499 ◽  
Author(s):  
Yubin Yang ◽  
Hui Lin ◽  
Zhongyang Guo ◽  
Jixi Jiang
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Spatial Data ◽  
Conceptual Modeling ◽  
Spatial Data Mining ◽  
Modeling Approach

Geographic Knowledge Discovery in Multiple Spatial Databases

2017 ◽  
pp. 344-366 ◽  
Author(s):  
Tahar Mehenni
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Spatial Data ◽  
Spatial Databases ◽  
Spatial Association ◽  
Spatial Data Mining ◽  
Research Field ◽  
Geographic Knowledge ◽  
Geographic Knowledge Discovery ◽  
Active Research

Voluminous geographic data have been, and continue to be, collected from various Geographic Information Systems (GIS) applications such as Global Positioning Systems (GPS) and high-resolution remote sensing. For these applications, huge amount of data is maintained in multiple disparate databases and different in spatial data type, file formats, data schema, access mechanism, etc. Spatial data mining and knowledge discovery has emerged as an active research field that focuses on the development of theory, methodology, and practice for the extraction of useful information and knowledge from massive and complex spatial databases. This chapter highlights recent theoretical and applied research in geographic knowledge discovery and spatial data mining in a distributed environment where spatial data are dispersed in multiple sites. The author will present in this chapter, an overall picture of how spatial multi-database mining is achieved through several common spatial data-mining tasks, including spatial cluster analysis, spatial association rule and spatial classification.

Geographic Knowledge Discovery in Multiple Spatial Databases

2018 ◽  
pp. 121-143
Author(s):  
Tahar Mehenni
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Spatial Data ◽  
Spatial Databases ◽  
Spatial Association ◽  
Spatial Data Mining ◽  
Research Field ◽  
Geographic Knowledge ◽  
Geographic Knowledge Discovery ◽  
Active Research

Voluminous geographic data have been, and continue to be, collected from various Geographic Information Systems (GIS) applications such as Global Positioning Systems (GPS) and high-resolution remote sensing. For these applications, huge amount of data is maintained in multiple disparate databases and different in spatial data type, file formats, data schema, access mechanism, etc. Spatial data mining and knowledge discovery has emerged as an active research field that focuses on the development of theory, methodology, and practice for the extraction of useful information and knowledge from massive and complex spatial databases. This chapter highlights recent theoretical and applied research in geographic knowledge discovery and spatial data mining in a distributed environment where spatial data are dispersed in multiple sites. The author will present in this chapter, an overall picture of how spatial multi-database mining is achieved through several common spatial data-mining tasks, including spatial cluster analysis, spatial association rule and spatial classification.

scholarly journals Knowledge Discovery Web Service for Spatial Data Infrastructures

2020 ◽  
Vol 10 (1) ◽  
pp. 12
Author(s):  
Morteza Omidipoor ◽  
Ara Toomanian ◽  
Najmeh Neysani Samany ◽  
Ali Mansourian
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Web Service ◽  
Spatial Data ◽  
Spatial Clustering ◽  
Spatial Data Mining ◽  
Data Mining Techniques ◽  
Data Infrastructures ◽  
Spatial Data Infrastructures ◽  
High Level

The size, volume, variety, and velocity of geospatial data collected by geo-sensors, people, and organizations are increasing rapidly. Spatial Data Infrastructures (SDIs) are ongoing to facilitate the sharing of stored data in a distributed and homogeneous environment. Extracting high-level information and knowledge from such datasets to support decision making undoubtedly requires a relatively sophisticated methodology to achieve the desired results. A variety of spatial data mining techniques have been developed to extract knowledge from spatial data, which work well on centralized systems. However, applying them to distributed data in SDI to extract knowledge has remained a challenge. This paper proposes a creative solution, based on distributed computing and geospatial web service technologies for knowledge extraction in an SDI environment. The proposed approach is called Knowledge Discovery Web Service (KDWS), which can be used as a layer on top of SDIs to provide spatial data users and decision makers with the possibility of extracting knowledge from massive heterogeneous spatial data in SDIs. By proposing and testing a system architecture for KDWS, this study contributes to perform spatial data mining techniques as a service-oriented framework on top of SDIs for knowledge discovery. We implemented and tested spatial clustering, classification, and association rule mining in an interoperable environment. In addition to interface implementation, a prototype web-based system was designed for extracting knowledge from real geodemographic data in the city of Tehran. The proposed solution allows a dynamic, easier, and much faster procedure to extract knowledge from spatial data.

Study on visualization of spatial data mining and knowledge discovery

2005 ◽  
Author(s):  
Shanlin Yang ◽  
Yongsen Li ◽  
Shuchu Zhang ◽  
Hao Bai
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Spatial Data ◽  
Spatial Data Mining
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