Knowledge Discovery Web Service for Spatial Data Infrastructures

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 informat...

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Main Authors: Morteza Omidipoor, Ara Toomanian, Najmeh Neysani Samany, Ali Mansourian
Format: Article
Language:English
Published: MDPI AG 2020-12-01
Series:ISPRS International Journal of Geo-Information
Subjects:
Online Access:https://www.mdpi.com/2220-9964/10/1/12
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author Morteza Omidipoor
Ara Toomanian
Najmeh Neysani Samany
Ali Mansourian
author_facet Morteza Omidipoor
Ara Toomanian
Najmeh Neysani Samany
Ali Mansourian
author_sort Morteza Omidipoor
collection DOAJ
description 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.
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spelling doaj.art-c285feade4fd43a4926245a085b5da882023-11-21T07:30:36ZengMDPI AGISPRS International Journal of Geo-Information2220-99642020-12-011011210.3390/ijgi10010012Knowledge Discovery Web Service for Spatial Data InfrastructuresMorteza Omidipoor0Ara Toomanian1Najmeh Neysani Samany2Ali Mansourian3Department of GIS and Remote Sensing, Faculty of Geography, University of Tehran, Tehran 1417853933, IranDepartment of GIS and Remote Sensing, Faculty of Geography, University of Tehran, Tehran 1417853933, IranDepartment of GIS and Remote Sensing, Faculty of Geography, University of Tehran, Tehran 1417853933, IranDepartment of Physical Geography and Ecosystem Science, Lund University, Box 117, SE-223 62 Lund, SwedenThe 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.https://www.mdpi.com/2220-9964/10/1/12spatial data miningknowledge discovery web serviceHadoopspatial data infrastructures
spellingShingle Morteza Omidipoor
Ara Toomanian
Najmeh Neysani Samany
Ali Mansourian
Knowledge Discovery Web Service for Spatial Data Infrastructures
ISPRS International Journal of Geo-Information
spatial data mining
knowledge discovery web service
Hadoop
spatial data infrastructures
title Knowledge Discovery Web Service for Spatial Data Infrastructures
title_full Knowledge Discovery Web Service for Spatial Data Infrastructures
title_fullStr Knowledge Discovery Web Service for Spatial Data Infrastructures
title_full_unstemmed Knowledge Discovery Web Service for Spatial Data Infrastructures
title_short Knowledge Discovery Web Service for Spatial Data Infrastructures
title_sort knowledge discovery web service for spatial data infrastructures
topic spatial data mining
knowledge discovery web service
Hadoop
spatial data infrastructures
url https://www.mdpi.com/2220-9964/10/1/12
work_keys_str_mv AT mortezaomidipoor knowledgediscoverywebserviceforspatialdatainfrastructures
AT aratoomanian knowledgediscoverywebserviceforspatialdatainfrastructures
AT najmehneysanisamany knowledgediscoverywebserviceforspatialdatainfrastructures
AT alimansourian knowledgediscoverywebserviceforspatialdatainfrastructures