Knowledge discoveryweb service for spatial data infrastructures
(2021) In ISPRS International Journal of Geo-Information 10(1).- Abstract
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... (More)
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 DiscoveryWeb 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.
(Less)
- author
- Omidipoor, Morteza ; Toomanian, Ara LU ; Samany, Najmeh Neysani and Mansourian, Ali LU
- organization
- publishing date
- 2021-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Hadoop, Knowledge discovery web service, Spatial data infrastructures, Spatial data mining
- in
- ISPRS International Journal of Geo-Information
- volume
- 10
- issue
- 1
- article number
- 12
- publisher
- MDPI AG
- external identifiers
-
- scopus:85107222995
- ISSN
- 2220-9964
- DOI
- 10.3390/ijgi10010012
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2021 MDPI AG. All rights reserved.
- id
- 6a323af0-6237-45a5-9fb5-6997423edf4f
- date added to LUP
- 2022-03-03 11:46:25
- date last changed
- 2023-10-09 01:13:45
@article{6a323af0-6237-45a5-9fb5-6997423edf4f,
abstract = {{<p>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 DiscoveryWeb 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.</p>}},
author = {{Omidipoor, Morteza and Toomanian, Ara and Samany, Najmeh Neysani and Mansourian, Ali}},
issn = {{2220-9964}},
keywords = {{Hadoop; Knowledge discovery web service; Spatial data infrastructures; Spatial data mining}},
language = {{eng}},
number = {{1}},
publisher = {{MDPI AG}},
series = {{ISPRS International Journal of Geo-Information}},
title = {{Knowledge discoveryweb service for spatial data infrastructures}},
url = {{http://dx.doi.org/10.3390/ijgi10010012}},
doi = {{10.3390/ijgi10010012}},
volume = {{10}},
year = {{2021}},
}